IT Fundamentals for Cybersecurity Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from IBM

Join Free:IT Fundamentals for Cybersecurity Specialization

Specialization - 4 course series

There are a growing number of exciting, well-paying jobs in today’s security industry that do not require a traditional college degree.  Forbes estimates that there will be as many as 3.5 million unfilled positions in the industry worldwide by 2021! One position with a severe shortage of skills is as a junior cybersecurity analyst. 

Throughout this specialization, you will learn concepts around cybersecurity tools and processes, system administration,  operating system and database vulnerabilities, types of cyber attacks and basics of networking. You will also gain knowledge around important topics such as cryptography and digital forensics.  

The instructors are architects , Security Operation Center (SOC) analysts, and distinguished engineers who work with cybersecurity in their day to day lives at IBM with a worldwide perspective.  They will share their skills which they need to secure IBM and its clients security systems.

The completion of this specialization also makes you eligible to earn the IT Fundamentals for Cybersecurity IBM digital badge. 

Applied Learning Project

This specialization will provide you with the basics you need to get started. Throughout this specialization, you will learn concepts around cybersecurity tools and processes, system administration, operating system and database vulnerabilities, types of cyber attacks and basics of networking. You will also gain knowledge around important topics such as cryptography and digital forensics.

This specialization is truly an international offering from IBM with experts from the United States, Costa Rica, Canada and Italy. These instructors are architects, Security Operation Center (SOC) analysts, and distinguished engineers who work with cybersecurity in their day to day lives at IBM. They will share their skills which they need to secure IBM and its clients security systems.

Learners will apply the skills they acquire to complete assessment exams as well as a project at the end of the four courses within this specialization.

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Certified in Cybersecurity Specialization

 

What you'll learn

Demonstrate that you have foundational knowledge of industry terminology, network security, security operations and policies and procedures.

Join Free:Certified in Cybersecurity Specialization

Specialization - 5 course series

Congratulations on your interest in pursuing a career in cybersecurity. The Certified in Cybersecurity (CC) certification will demonstrate to employers that you have foundational knowledge of industry terminology, network security, security operations and policies and procedures that are necessary for an entry- or junior-level cybersecurity role. It will signal your understanding of fundamental security best practices, policies and procedures, as well as your willingness and ability to learn more and grow on the job.

Applied Learning Project

Each course includes a case study that will require students to put into practice the knowledge they have gained throughout each course. Successful completion of course projects will require the basic understanding of the topics covered and the ability to relate those topics to the real world. The objective of each project is to determine whether students have understood course concepts and are able to use them in a real world setting. 

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Microsoft Cybersecurity Analyst Professional Certificate

 

What you'll learn

Understand the cybersecurity landscape and learn core concepts foundational to security, compliance, and identity solutions.

Understand the vulnerabilities of an organizations network and mitigate attacks on network infrastructures to protect data.

Develop and implement threat mitigation strategies by applying effective cybersecurity measures within an Azure environment.   

Demonstrate your new skills with a capstone project and prepare for the industry-recognized Microsoft SC-900 Certification exam. 

Join Free:Microsoft Cybersecurity Analys Professional Certificate

Professional Certificate - 9 course series

Learners who complete this program will receive a 50% discount voucher to take the SC-900 Certification Exam. 

Organizations rely on cybersecurity experts to protect themselves from threats, but nearly 60% report security talent shortages.1 Prepare for a new career in this high-demand field with professional training from Microsoft — an industry-recognized leader in cybersecurity.

The role of a cybersecurity analyst includes monitoring networks for vulnerabilities or potential threats, mitigating attacks on the network infrastructures, and implementing strategies for data protection. With 95% of fortune 500 companies using Azure, it’s critical for cybersecurity professionals to learn to protect data within an Azure environment.2

Through a mix of videos, assessments, and hands-on activities, you’ll learn cybersecurity concepts and how they apply to a business environment, discuss threat mitigation strategies from an enterprise perspective, apply effective cybersecurity policy measures within an Azure environment, & practice on tools like MS defender, Azure Active Directory & more.

When you graduate, you’ll have tangible examples to talk about in job interviews and you’ll also be prepared to take the Microsoft SC-900 Certification Exam.

1McKinsey & Company, Securing your organization to reduce cyber risk (June 2022)

2Microsoft Azure, Microsoft Azure:The only consistent, comprehensive hybrid cloud (Sept 2018)

Applied Learning Project

This program has been uniquely mapped to key job skills required in cybersecurity analyst roles. In each course, you’ll be able to consolidate what you have learned by completing a capstone project that simulates real-world cybersecurity scenarios. You’ll also complete a final capstone project where you’ll create your own cybersecurity proposal for the creation and protection of a business network and infrastructure. The projects will include practicing on:

A real-world scenario focused on penetration testing, building configuration, and testing strategy for white box testing of a penetration test on virtual networks.

A real-world capstone project that enables you to demonstrate your cybersecurity analyst skills. 

To round off your learning, you’ll take a mock exam that has been set up in a similar style to the industry-recognized Microsoft Exam SC-900: Microsoft Security, Compliance, and Identity Fundamentals. 

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Google IT Support Professional Certificate

 

What you'll learn

Gain skills required to succeed in an entry-level IT job

Learn to perform day-to-day IT support tasks including computer assembly, wireless networking, installing programs, and customer service

Learn how to provide end-to-end customer support, ranging from identifying problems to troubleshooting and debugging

Learn to use systems including Linux, Domain Name Systems, Command-Line Interface, and Binary Code

Join Free:Google IT Support Professional Certificate

Prepare for a career in IT Support

Receive professional-level training from Google

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from Google

Qualify for in-demand job titles: IT Specialist, Tech Support Specialist, IT Support Specialist

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Introduction to Cybersecurity Tools & Cyber Attacks

 

What you'll learn

Discuss the evolution of security based on historical events.  

List various types of malicious software.  

Describe key cybersecurity concepts including the CIA Triad, access management, incident response and common cybersecurity best practices.  

Identify key cybersecurity tools which include the following:  firewall, anti-virus, cryptography, penetration testing and digital forensics.   

Join Free: Introduction to Cybersecurity Tools & Cyber Attacks

There are 4 modules in this course

This course gives you the background needed to understand basic Cybersecurity.  You will learn the history of Cybersecurity, types and motives of cyber attacks to further your knowledge of current threats to organizations and individuals.  Key terminology, basic system concepts and tools will be examined as an introduction to the Cybersecurity field.

You will learn about critical thinking and its importance to anyone looking to pursue a career in Cybersecurity.

Finally, you will begin to learn about organizations and resources to further research cybersecurity issues in the Modern era.

This course is intended for anyone who wants to gain a basic understanding of Cybersecurity or as the first course in a series of courses to acquire the skills to work in the Cybersecurity field as a Jr Cybersecurity Analyst.

The completion of this course also makes you eligible to earn the Introduction to Cybersecurity Tools & Cyber Attacks IBM digital badge. 

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Python for Data Analysis: Pandas & NumPy

 

What you'll learn

Understand python programming fundamentals for data analysis

Define single and multi-dimensional NumPy arrays

Import HTML data in Pandas DataFrames

Join Free : Python for Data Analysis: Pandas & NumPy

About this Guided Project

In this hands-on project, we will understand the fundamentals of data analysis in Python and we will leverage the power of two important python libraries known as Numpy and pandas. NumPy and Pandas are two of the most widely used python libraries in data science. They offer high-performance, easy to use structures and data analysis tools. 

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Data Science with NumPy, Sets, and Dictionaries

 

There are 4 modules in this course

Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.

Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackle big data challenges and solve data problems effectively. You'll write Python programs to manipulate and filter data, as well as create useful insights out of large datasets.

By the end of the course, you'll be adept at summarizing datasets, such as calculating averages, minimums, and maximums. Additionally, you'll gain advanced skills in optimizing data analysis with vectorization and randomizing data.

Throughout your learning journey, you'll use many kinds of data structures and analytic techniques for a variety of data science challenges , including mathematical operations, text file analysis, and image processing. Stepwise, guided assignments each week will reinforce your skills, enabling you to solve problems and draw data-driven conclusions independently.

Prepare yourself for a rewarding career in data science by mastering NumPy and honing your programming prowess. Start this transformative learning experience today!

Join Free : Data Science with NumPy, Sets, and Dictionaries

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Python and Pandas for Data Engineering

 

What you'll learn

Setup a provisioned Python project environment

Use Pandas libraries to read and write data into data structures and files

Employ Vim and Visual Studio Code to write Python code

Join Free : Python and Pandas for Data Engineering

There are 4 modules in this course

In this first course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will learn how to set up a version-controlled Python working environment which can utilize third party libraries. You will learn to use Python and the powerful Pandas library for data analysis and manipulation. Additionally, you will also be introduced to Vim and Visual Studio Code, two popular tools for writing software. This course is valuable for beginning and intermediate students in order to begin transforming and manipulating data as a data engineer.

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Data Engineering, Big Data, and Machine Learning on GCP Specialization

 

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from Google Cloud

Join Free:Data Engineering, Big Data, and Machine Learning on GCP Specialization

Specialization - 5 course series

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized 

Google Cloud Professional Data Engineer

 certification.

Here's what you have to do

1) Complete the Coursera Data Engineering Professional Certificate

2) Review other recommended resources for the 

Google Cloud Professional Data Engineer certification

 exam

3) Review the 

Professional Data Engineer exam guide

4) Complete 

Professional Data Engineer 

5) Register

 for the Google Cloud certification exam (remotely or at a test center)

Applied Learning Project

This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Applied Learning Project

 This Specialization incorporates hands-on labs using our Qwiklabs platform.

These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as BigQuery, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

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Connect and Protect: Networks and Network Security

 

What you'll learn

Define the types of networks and components of networks

Illustrate how data is sent and received over a network

Understand how to secure a network against intrusion tactics

Join Free:Connect and Protect: Networks and Network Security

There are 4 modules in this course

This is the third course in the Google Cybersecurity Certificate. These courses will equip you with the skills you need to apply for an entry-level cybersecurity job. You’ll build on your understanding of the topics that were introduced in the second Google Cybersecurity Certificate course.

In this course, you will explore how networks connect multiple devices and allow them to communicate. You'll start with the fundamentals of modern networking operations and protocols. For example, you'll learn about the Transmission Control Protocol / Internet Protocol (TCP/IP) model and how network hardware, like routers and modems, allow your computer to send and receive information on the internet. Then, you'll learn about network security. Organizations often store and send valuable information on their networks, so networks are common targets of cyber attacks. By the end of this course, you'll be able to recognize network-level vulnerabilities, and explain how to secure a network using firewalls, system hardening, and virtual private networks. 

Google employees who currently work in cybersecurity will guide you through videos, provide hands-on activities and examples that simulate common cybersecurity tasks, and help you build your skills to prepare for jobs. 

Learners who complete this certificate will be equipped to apply for entry-level cybersecurity roles. No previous experience is necessary.

By the end of this course, you will: 

- Describe the structure of different computer networks.

- Illustrate how data is sent and received over a network.

- Recognize common network protocols.

- Identify common network security measures and protocols.

- Explain how to secure a network against intrusion tactics.

- Compare and contrast local networks to cloud computing.

- Explain the different types of system hardening techniques.

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Play It Safe: Manage Security Risks

 

What you'll learn

Identify the primary threats, risks, and vulnerabilities to business operations

Examine how organizations use security frameworks and controls to protect business operations

Define commonly used Security Information and Event Management (SIEM) tools

Use a playbook to respond to threats, risks, and vulnerabilities

Join Free:Play It Safe: Manage Security Risks

There are 4 modules in this course

This is the second course in the Google Cybersecurity Certificate. These courses will equip you with the skills you need to apply for an entry-level cybersecurity job. You’ll build on your understanding of the topics that were introduced in the first Google Cybersecurity Certificate course.

In this course, you will take a deeper dive into concepts introduced in the first course, with an emphasis on how cybersecurity professionals use frameworks and controls to protect business operations. In particular, you'll identify the steps of risk management and explore common threats, risks, and vulnerabilities. Additionally, you'll explore Security Information and Event Management (SIEM) data and use a playbook to respond to identified threats, risks, and vulnerabilities. Finally, you will take an important step towards becoming a cybersecurity professional and practice performing a security audit.

Google employees who currently work in cybersecurity will guide you through videos, provide hands-on activities and examples that simulate common cybersecurity tasks, and help you build your skills to prepare for jobs. 

Learners who complete this certificate will be equipped to apply for entry-level cybersecurity roles. No previous experience is necessary.

By the end of this course, you will: 

- Identify the common threats, risks, and vulnerabilities to business operations.

- Understand the threats, risks, and vulnerabilities that entry-level cybersecurity analysts are most focused on.

- Comprehend the purpose of security frameworks and controls.

- Describe the confidentiality, integrity, and availability (CIA) triad.

- Explain the National Institute of Standards and Technology (NIST) framework.

- Explore and practice conducting a security audit.

- Use a playbook to respond to threats, risks, and vulnerabilities.

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Generative AI Fundamentals Specialization

What you'll learn

Explain the fundamental concepts, capabilities, models, tools, applications, and platforms of generative AI foundation models.

Apply powerful prompt engineering techniques to write effective prompts and generate desired outcomes from AI models.

Discuss the limitations of generative AI and explain the ethical concerns and considerations for the responsible use of generative AI.

Recognize the ability of generative AI to enhance your career and help implement improvements at your workplace.

Join Free: Generative AI Fundamentals Specialization

Specialization - 5 course series

Generative AI is revolutionizing our lives.

This specialization provides a comprehensive understanding of the fundamental concepts, models, tools, and applications of generative AI to enable you to leverage the potential of generative AI toward a better workplace, career, and life.

The specialization consists of five short, self-paced courses, each requiring 3–5 hours to complete.

Understand powerful prompt engineering techniques and learn how to write effective prompts to produce desired outcomes using generative AI tools.

Learn about the building blocks and foundation models of generative AI, such as the GPT, DALL-E, and IBM Granite models. Gain an understanding of the ethical implications, considerations, and issues of generative AI.

Listen to experts share insights and tips for being successful with generative AI. Learn to leverage Generative AI to boost your career and become more productive.

Practice what you learn using hands-on labs and projects, which are suitable for everyone and can be completed using a web browser. These labs will give you an opportunity to explore the use cases of generative AI through popular tools and platforms like IBM watsonx.ai, OpenAI ChatGPT, Stable Diffusion, and Hugging Face.

This specialization is for anyone passionate about discovering the power of generative AI and requires no prior technical knowledge or a background in AI. It will benefit professionals from all walks of life.

Applied Learning Project

Throughout this specialization, you will complete hands-on labs and projects to help you gain practical experience with text, image, and code generation, prompt engineering tools, foundation models, AI applications, and IBM watsonx.ai.

Some examples of the labs included are:

Text generation using ChatGPT and Bard

Image generation using GPT and Stable Diffusion

Code generation in action

Getting to know prompting tools

Experimenting with prompts

Different approaches in prompt engineering

Generative AI foundation models

Exploring IBM watsonx.ai and Hugging Face

 

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Generative AI for Everyone

 

What you'll learn

What generative AI is and how it works, its common use cases, and what this technology can and cannot do.

How to think through the lifecycle of a generative AI project, from conception to launch, including how to build effective prompts.

The potential opportunities and risks that generative AI technologies present to individuals, businesses, and society.

Join Free: Generative AI for Everyone

There are 3 modules in this course

Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you'll learn to use generative AI to help in day-to-day work and receive tips on effective prompt engineering, as well as learning how to go beyond prompting for more advanced uses of AI.

You’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases. You’ll get hands-on time with generative AI projects to put your knowledge into action and gain insight into its impact on both business and society. 

This course was created to ensure everyone can be a participant in our AI-powered future.

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Algorithms for Decision Making (Free PDF)

 

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.

Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.

The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Buy : Algorithms for Decision Making by Mykel J. Kochenderfer (Author), Tim A. Wheeler (Author), Kyle H. Wray (Author)

PDF Download : Algorithms for Decision Making

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Computer Graphics [CGR]

 Basic Concepts:

a. Define computer graphics and explain its significance.

b. Differentiate between raster and vector graphics.

Graphics Primitives:

a. Discuss the difference between points, lines, and polygons as graphics primitives.

b. Explain the concept of anti-aliasing in the context of computer graphics.

2D Transformations:

a. Describe the translation, rotation, and scaling transformations in 2D graphics.

b. Provide examples of homogeneous coordinates in 2D transformations.

Clipping and Windowing:

a. Explain the need for clipping in computer graphics.

b. Discuss the Cohen-Sutherland line-clipping algorithm.

3D Transformations:

a. Describe the translation, rotation, and scaling transformations in 3D graphics.

b. Explain the concept of perspective projection.

Hidden Surface Removal:

a. Discuss the challenges of hidden surface removal in 3D graphics.

b. Explain the Z-buffer algorithm.

Color Models:

a. Describe the RGB and CMY color models.

b. Explain the concept of color depth.

Rasterization:

a. Discuss the process of scan conversion in computer graphics.

b. Explain the Bresenham's line-drawing algorithm.

Computer Animation:

a. Define keyframes and in-betweening in computer animation.

b. Discuss the principles of skeletal animation.

Ray Tracing:

a. Explain the concept of ray tracing in computer graphics.

b. Discuss the advantages and disadvantages of ray tracing.

OpenGL:

a. Describe the OpenGL graphics pipeline.

b. Explain the purpose of the Model-View-Projection (MVP) matrix in OpenGL.

Virtual Reality (VR):

a. Define virtual reality and its applications in computer graphics.

b. Discuss the challenges of achieving realism in virtual reality.

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Digital Techniques [DTE]

 Number Systems:

a. Convert the binary number 101010 to its decimal equivalent.

b. Explain the concept of two's complement in binary representation.

Logic Gates:

a. Implement the XOR gate using only NAND gates.

b. Explain the truth table for a half adder circuit.

Combinational Circuits:

a. Design a 4-to-1 multiplexer using basic logic gates.

b. Implement a full subtractor circuit.

Sequential Circuits:

a. Describe the operation of a D flip-flop.

b. Design a 3-bit binary counter using JK flip-flops.

Registers and Counters:

a. Explain the difference between a shift register and a parallel-in/serial-out register.

b. Design a 4-bit synchronous up-counter using T flip-flops.

Memory Units:

a. Define the terms RAM and ROM.

b. Explain the concept of memory decoding in digital systems.

Digital-to-Analog Conversion:

a. Describe the operation of a weighted resistor digital-to-analog converter.

b. Explain the purpose of a sample-and-hold circuit in digital-to-analog conversion.

Analog-to-Digital Conversion:

a. Discuss the successive approximation method for analog-to-digital conversion.

b. Explain the concept of quantization error in the context of analog-to-digital conversion.

Multiplexers and Demultiplexers:

a. Design an 8-to-1 multiplexer using 4-to-1 multiplexers.

b. Explain the function of a demultiplexer.

Digital Logic Families:

a. Compare and contrast TTL and CMOS logic families.

b. Discuss the advantages and disadvantages of ECL logic.

Programmable Logic Devices (PLDs):

a. Describe the types of PLDs and their applications.

b. Explain the concept of a look-up table (LUT) in programmable logic.

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Object Oriented Programming [OOP]

 Basic Concepts:

a. Define what an object is in the context of OOP.

b. Explain the difference between a class and an object.

Encapsulation:

a. Describe the concept of encapsulation and its benefits.

b. Provide an example of encapsulation in a programming language of your choice.

Inheritance:

a. Explain the concept of inheritance and its purpose.

b. Differentiate between single inheritance and multiple inheritance.

Polymorphism:

a. Define polymorphism and explain its types.

b. Provide an example of compile-time and runtime polymorphism.

Abstraction:

a. Discuss the importance of abstraction in OOP.

b. Provide an example of abstraction in a real-world scenario.

Class and Object Relationships:

a. Explain the difference between a class method and an instance method.

b. Describe the concept of composition in OOP.

Interfaces and Abstract Classes:

a. Define an interface and explain its role in OOP.

b. Differentiate between an interface and an abstract class.

Design Patterns:

a. Discuss the singleton design pattern and its use cases.

b. Explain the observer design pattern.

Exception Handling:

a. Describe how exception handling is implemented in an object-oriented language.

b. Discuss the importance of try-catch blocks in OOP.

Object-Oriented Analysis and Design (OOAD):

a. Explain the phases of Object-Oriented Analysis and Design.

b. Discuss the importance of UML (Unified Modeling Language) in OOAD.

Generic Programming:

a. Define generic programming and its advantages.

b. Provide an example of using generics in a programming language.

Reflection:

a. Explain the concept of reflection in OOP.

b. Discuss situations where reflection can be useful.

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Database Management System [DBMS]

 Basics of DBMS:

a. Define what a database is and explain its advantages.

b. Differentiate between DBMS and RDBMS.

Relational Database Concepts:

a. Define the terms: table, tuple, attribute, and primary key.

b. Explain the concept of normalization and its importance in a relational database.

SQL Queries:

a. Write an SQL query to retrieve all records from a table named "Employees."

b. Explain the differences between the WHERE and HAVING clauses in SQL.

Database Design:

a. What is the purpose of a foreign key in a relational database?

b. Describe the steps involved in the normalization process.

Transaction Management:

a. Define the ACID properties in the context of database transactions.

b. Explain the concepts of commit and rollback in a database transaction.

Indexing and Query Optimization:

a. Discuss the importance of indexing in a database.

b. Explain how the query optimizer works in a relational database system.

Concurrency Control:

a. What is a deadlock in the context of database concurrency?

b. Discuss the various methods of handling concurrent transactions.

Data Integrity and Constraints:

a. Explain the concept of referential integrity in a relational database.

b. Define the CHECK constraint in SQL.

NoSQL Databases:

a. Compare and contrast SQL and NoSQL databases.

b. Provide examples of NoSQL databases and their use cases.

Database Security:

a. Discuss the importance of database security.

b. Describe techniques for securing a database, including access control and encryption.

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Data Structures Using C

 Arrays and Strings:

a. Write a C program to find the sum of elements in an array.

b. Explain how you can reverse a string in C.

Linked Lists:

a. Implement a function to insert a node at the beginning of a linked list.

b. Write a program to detect a loop in a linked list.

Stacks:

a. Implement a stack using an array.

b. Write a C program to check for balanced parentheses using a stack.

Queues:

a. Implement a queue using two stacks.

b. Write a C program to perform enqueue and dequeue operations on a queue.

Trees:

a. Implement a binary search tree and perform an inorder traversal.

b. Write a function to find the height of a binary tree.

Graphs:

a. Implement a depth-first search (DFS) algorithm for a graph.

b. Write a program to find the shortest path in a weighted graph using Dijkstra's algorithm.

Sorting and Searching:

a. Implement the quicksort algorithm in C.

b. Write a program to perform binary search on a sorted array.

Hashing:

a. Implement a hash table in C.

b. Write a program to handle collisions in a hash table using chaining.

Dynamic Programming:

a. Solve the Fibonacci sequence using dynamic programming.

b. Implement the knapsack problem using dynamic programming.

Miscellaneous:

a. Explain the difference between a stack and a queue.

b. Describe the advantages and disadvantages of arrays and linked lists.

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Linear Regression with Python

 

What you'll learn

Create a linear model, and implement gradient descent.

Train the linear model to fit given data using gradient descent.

Join Free:Linear Regression with Python

About this Guided Project

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.

Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Data Science as a Field

 

What you'll learn

By taking this course, you will be able explain what data science is and identify the key disciplines involved.

You will be able to use the steps of the data science process to create a reproducible data analysis and identify personal biases.

You will be able to identify interesting data science applications, locate jobs in Data Science, and begin developing a professional network.

Join Free:Data Science as a Field

There are 4 modules in this course

This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline.

Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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Excel/VBA for Creative Problem Solving, Part 3 (Projects)

 

What you'll learn

Create a VBA user form that will implement or solve a real world scenario or problem

Join Free:Excel/VBA for Creative Problem Solving, Part 3 (Projects)

There are 5 modules in this course

In this course, learners will complete several VBA projects.  It is highly recommended that learners first take "Excel/VBA for Creative Problem Solving, Part 1" and "Excel/VBA for Creative Problem Solving, Part 2".  This course builds off of skills learned in those two courses.  This is a project-based course.  Therefore, the projects are quite open-ended and there are multiple ways to solve the problems.  Through the use of Peer Review, other learners will grade learners' projects based on a grading rubric.

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Introduction to Data Analysis Using Excel

 

Build your subject-matter expertise

This course is part of the Business Statistics and Analysis Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Introduction to Data Analysis Using Excel

There are 4 modules in this course

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This is an introductory course in the use of Excel and is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics later. The course is designed keeping in mind two kinds of learners -  those who have very little functional knowledge of Excel and those who use Excel regularly but at a peripheral level and wish to enhance their skills. The course takes you from basic operations such as reading data into excel using various data formats, organizing and manipulating data, to some of the more advanced functionality of Excel. All along, Excel functionality is introduced using easy to understand examples which are demonstrated in a way that learners can become comfortable in understanding and applying them.

To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. 

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WEEK 1

Module 1: Introduction to Spreadsheets

In this module, you will be introduced to the use of Excel spreadsheets and various basic data functions of Excel.

Topics covered include:

• Reading data into Excel using various formats

• Basic functions in Excel, arithmetic as well as various logical functions

• Formatting rows and columns

• Using formulas in Excel and their copy and paste using absolute and relative referencing

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WEEK 2

Module 2: Spreadsheet Functions to Organize Data

This module introduces various Excel functions to organize and query data. Learners are introduced to the IF, nested IF, VLOOKUP and the HLOOKUP functions of Excel. 

Topics covered include:

• IF and the nested IF functions

• VLOOKUP and HLOOKUP

• The RANDBETWEEN function

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WEEK 3

Module 3: Introduction to Filtering, Pivot Tables, and Charts

This module introduces various data filtering capabilities of Excel. You’ll learn how to set filters in data to selectively access data. A very powerful data summarizing tool, the Pivot Table, is also explained and we begin to introduce the charting feature of Excel.

Topics covered include:

• VLOOKUP across worksheets

• Data filtering in Excel

• Use of Pivot tables with categorical as well as numerical data

• Introduction to the charting capability of Excel

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WEEK 4

Module 4: Advanced Graphing and Charting

This module explores various advanced graphing and charting techniques available in Excel. Starting with various line, bar and pie charts we introduce pivot charts, scatter plots and histograms. You will get to understand these various charts and get to build them on your own.

Topics covered include

• Line, Bar and Pie charts

• Pivot charts

• Scatter plots

• Histograms

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Hadoop Platform and Application Framework

 

There are 5 modules in this course

This course is for novice programmers or business people who would like to understand the core tools used to wrangle and analyze big data. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment.   In the assignments you will be guided in how data scientists apply the important concepts and techniques such as Map-Reduce that are used to solve fundamental problems in big data.  You'll feel empowered to have conversations about big data and the data analysis process.

Join Free:Hadoop Platform and Application Framework

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Using Python to calculate standard deviation and variance

 

import numpy as np

data = np.array([1, 3, 5, 7, 9])

sd = np.std(data)

var = np.var(data)

print(f"Standard deviation: {sd:.2f}")

print(f"Variance: {var:.2f}")

#For free code visit clcoding.com

import statistics

data = [1, 3, 5, 7, 9]

sd = statistics.stdev(data)

psd = statistics.pstdev(data)

print(f"Standard deviation: {sd:.2f}")

print(f"Population standard deviation: {psd:.2f}")

#For free code visit clcoding.com

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Python Coding challenge - Day 93 | What is the output of the following Python Code?

 

Solution and Explanation :

The code print(round(1 / 3, 2)) will output the result of dividing 1 by 3 and rounding the result to 2 decimal places. Let's calculate:

1/3≈0.333333...31​≈0.333333...

Rounding this to 2 decimal places, the result will be:

round(13,2)≈0.33round(31​,2)≈0.33

So, the output of the code will be: 0.33

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Applied Machine Learning in Python

 

What you'll learn

Describe how machine learning is different than descriptive statistics

Create and evaluate data clusters

Explain different approaches for creating predictive models

Build features that meet analysis needs

Join Free:Applied Machine Learning in Python

There are 4 modules in this course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. 

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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Neural Networks and Deep Learning

 

Build your subject-matter expertise

This course is part of the Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Neural Networks and Deep Learning

There are 4 modules in this course

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. 

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

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Machine Learning Introduction for Everyone

 

What you'll learn

Compare and contrast artificial intelligence, machine learning, and deep learning 

Explain the machine learning models development lifecycle    

Differentiate between supervised and unsupervised machine learning   

Evaluate classification models using metrics such as accuracy, confusion matrices, precision, and recall

Join Free:Machine Learning Introduction for Everyone

There are 3 modules in this course

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well as create a final project demonstrating your proficiency. 

After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices. 

This Course Is Part of Multiple Programs 

You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.

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Machine Learning for All

 

What you'll learn

You will understand the basic of how modern machine learning technologies work

You will be able to explain and predict how data affects the results of machine learning

You will be able to use a non-programming based platform train a machine learning module using a dataset

You will be able to form an informed opinion on the benefits and dangers of machine learning to society

Join Free: Machine Learning for All

There are 4 modules in this course

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should. The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms. In this course you will learn to understand the basic idea of machine learning, even if you don't have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognise images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high level concepts before the technical details, but it is also great if your role is non-technical. You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding. Or you might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through you first machine learning project.

NB this course is designed to introduce you to Machine Learning without needing any programming. That means that we don't cover the programming based machine learning tools like python and TensorFlow.

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Supervised Machine Learning: Regression and Classification

 

What you'll learn

Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn

Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression

Join Free: Supervised Machine Learning: Regression and Classification

There are 3 modules in this course

In the first course of the Machine Learning Specialization, you will:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

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What is the output of following Python code?

 

What is the output of following Python code?

import math

print(math.floor(-2.8))

print(math.trunc(-2.8))

print(math.ceil(-2.8))

Solution and Explanation: 

The output of the provided Python code is:

-3

-2

-2

Here's a breakdown of each line:

math.floor(-2.8): This line uses the floor function from the math module to round down -2.8 to the nearest integer. Since the largest integer less than or equal to -2.8 is -3, the output is -3.

math.trunc(-2.8): Similar to floor, trunc also rounds down towards zero. However, unlike floor, it truncates the decimal part of the number instead of rounding it. Therefore, math.trunc(-2.8) also outputs -2.

math.ceil(-2.8): This line uses the ceil function, which rounds numbers up to the nearest integer. The smallest integer greater than or equal to -2.8 is -2, so math.ceil(-2.8) outputs -2.

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Machine Learning for Trading Specialization

 

What you'll learn

Understand the structure and techniques used in machine learning, deep learning, and reinforcement learning (RL) strategies.

Describe the steps required to develop and test an ML-driven trading strategy.

Describe the methods used to optimize an ML-driven trading strategy.

Use Keras and Tensorflow to build machine learning models.

Join Free:Machine Learning for Trading Specialization

Specialization - 3 course series

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies.  By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading.  This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.

Applied Learning Project

The three courses will show you how to create various quantitative and algorithmic trading strategies using Python. By the end of the specialization, you will be able to create and enhance quantitative trading strategies with machine learning that you can train, test, and implement in capital markets. You will also learn how to use deep learning and reinforcement learning strategies to create algorithms that can update and train themselves.

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IBM AI Engineering Professional Certificate

 

What you'll learn

Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction 

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn 

Deploy machine learning algorithms and pipelines on Apache Spark 

Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow 

Join Free:IBM AI Engineering Professional Certificate

Professional Certificate - 6 course series

Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.  

You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.

Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.

In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering. 

Applied Learning Project

Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes. 

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Machine Learning Engineering for Production (MLOps) Specialization

 

What you'll learn

Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.

Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.

Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.

Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

Join Free:Machine Learning Engineering for Production (MLOps) Specialization

Specialization - 4 course series

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. 

Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. 

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Applied Learning Project

By the end, you'll be ready to

• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements

• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application

• Build data pipelines by gathering, cleaning, and validating datasets

• Implement feature engineering, transformation, and selection with TensorFlow Extended

• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas

• Apply techniques to manage modeling resources and best serve offline/online inference requests

• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks

• Deliver deployment pipelines for model serving that require different infrastructures

• Apply best practices and progressive delivery techniques to maintain a continuously operating production system.

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AI For Everyone

 

There are 4 modules in this course

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. 

Join Free:AI For Everyone

In this course, you will learn:

- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science

- What AI realistically can--and cannot--do

- How to spot opportunities to apply AI to problems in your own organization

- What it feels like to build machine learning and data science projects

- How to work with an AI team and build an AI strategy in your company

- How to navigate ethical and societal discussions surrounding AI

Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

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Machine Learning Specialization

 

What you'll learn

Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)

Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods

Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection

Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

Join Free : Machine Learning Specialization

Specialization - 3 course series

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Applied Learning Project

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.

• Build and train a neural network with TensorFlow to perform multi-class classification.

• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

• Build a deep reinforcement learning model.

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Python Coding challenge - Day 92 | What is the output of the following Python Code?

 

The above code is a Python list comprehension that adds corresponding elements of sublists in the nested list lis. Let's break it down:

lis = [[8, 7], [6, 5]]

result = [p + q for p, q in lis]

print(result)

lis is a nested list with two sublists: [8, 7] and [6, 5].

The list comprehension [p + q for p, q in lis] iterates over each sublist, and for each sublist, it takes elements p and q and calculates their sum p + q.

The result is a new list (result) containing the sums of corresponding elements from the sublists.

When you print the result, you'll get:

[15, 11]

This is because:

For the first sublist [8, 7], the sum of corresponding elements is 8 + 7 = 15.

For the second sublist [6, 5], the sum of corresponding elements is 6 + 5 = 11.

So, the final output is [15, 11].

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IBM Cybersecurity Analyst Professional Certificate

 

What you'll learn

Develop knowledge of cybersecurity analyst tools including data protection; endpoint protection; SIEM; and systems and network fundamentals.   

Learn about key compliance and threat intelligence topics important in today’s cybersecurity landscape. 

Gain skills for incident responses and forensics with real-world cybersecurity case studies.

Get hands-on experience to develop skills via industry specific and open source Security tools.

Professional Certificate - 8 course series

A growing number of exciting, well-paying jobs in today’s security industry do not require a college degree. This Professional Certificate will give you the technical skills to become job-ready for a Cybersecurity Analyst role. Instructional content and labs will introduce you to concepts including network security, endpoint protection, incident response, threat intelligence, penetration testing, and vulnerability assessment.

Join Free : IBM Cybersecurity Analyst Professional Certificate

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Earn a data science certificate

 

• Earn a data science certificate

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