Web Scraping With GPT: Translate Foreign News Headlines

 

In a world brimming with diverse information, the ability to navigate, extract, and understand global content has become indispensable. The Coursera course “AI Web Scraping with GPT: Translating Foreign News Headlines,”  introduces learners to a groundbreaking approach that combines web scraping and AI-powered translation. This blog delves into the unique features and potential applications of this course.

Why This Course Stands Out

Designed for tech enthusiasts, beginners, and professionals alike, this course merges essential technical skills with practical applications. Rudi Hinds’ offering is particularly noteworthy for:

Focusing on Real-World Relevance: The course centers on scraping and translating foreign news headlines, a practical use case with applications in journalism, market research, and global communication.

Utilizing Advanced AI Tools: Learners are introduced to OpenAI’s GPT technology, renowned for its powerful natural language processing and translation capabilities.

Step-by-Step Learning: The course ensures accessibility by breaking down complex tasks into manageable steps, making it ideal for learners with basic Python skills.

Course Overview

1. Foundations of Web Scraping

Participants are guided through the fundamentals of web scraping using Python libraries like BeautifulSoup. This foundational skill allows users to extract structured data, such as foreign news headlines, from various websites.

2. Integrating GPT for Translation

A standout feature of the course is its integration of GPT for translating foreign headlines into the learner’s preferred language. Learners gain hands-on experience working with OpenAI’s API to:

• Generate accurate translations.
• Maintain contextual integrity across different languages.
• Experiment with parameters to fine-tune the output.

3. Storing and Analyzing Data

The course also covers data organization and storage, providing learners with the skills to compile, sort, and analyze translated headlines. This opens doors to insights into global trends and narratives.

4. Practical Applications

By the end of the course, participants can:

• Automate multilingual data collection.
• Analyze media trends across languages and regions.
• Apply these techniques to personal, academic, or professional projects.

What You Will Gain

The course equips learners with a versatile skill set that combines programming, AI, and global communication. Key takeaways include:

Technical Expertise: Hands-on experience with Python, BeautifulSoup, and OpenAI’s GPT.

Global Awareness: An ability to explore and understand foreign media content in your native language.

Scalable Insights: Skills that can be adapted to various domains, from business intelligence to policy research.

Real-World Applications

1. Journalism and Media

Journalists can use these skills to monitor and analyze international news stories, ensuring diverse coverage and perspectives.

2. Business Intelligence

Marketers and business strategists can uncover global trends, identify opportunities, and assess risks by translating and analyzing international headlines.

3. Education and Research

Academics and students can explore multilingual data sets, enabling cross-cultural studies and fostering global insights.

Why Learn AI-Powered Web Scraping and Translation?

With the proliferation of information online, the ability to automate data extraction and translate it effectively is a game-changer. Rudi Hinds’ course provides an accessible pathway to harnessing these technologies, empowering learners to:

Break language barriers.

Analyze data at scale.

Gain a competitive edge in an increasingly data-driven world.

Join Free: Web Scraping With GPT: Translate Foreign News Headlines

Conclusion:

 “AI Web Scraping with GPT: Translating Foreign News Headlines,” is a must-try for anyone looking to explore the intersection of AI and data. Whether you’re a tech enthusiast, researcher, or professional aiming to stay ahead of the curve, this course provides a robust foundation in one of the most impactful applications of AI today.

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AI Learning Hub - Lifetime Learning Access

What will you get?

✔ 10+ hours of AI content from the fundamentals to advanced.

✔ Hands-on machine learning and deep learning projects with step-by-step coding instructions.

✔ Real-world end-to-end projects to help you build a professional AI portfolio.

✔ A private collaborative community of AI learners and professionals.

✔ Receive feedback on your projects from peers and community members.

✔ Direct access to your instructor.

✔ Lifetime access to every past and future courses and content.

Jon here : AI Learning Hub - Lifetime Learning Access

30-Day Free Trial – No Risk, No Problem!

Join today and enjoy a full 30-day free trial with complete access to all content. No strings attached – experience the program and decide if it's right for you. If you're not satisfied, you can cancel at any time during the trial with zero cost. We’re confident you’ll love it, but you’ve got nothing to lose with our risk-free guarantee!

Program Syllabus

The AI Learning Hub is your ongoing path to mastering AI. This syllabus outlines the key topics you’ll cover throughout the program. Each section is designed to build on the last, ensuring you develop both foundational and advanced skills through practical, hands-on learning. As part of this continuous cohort, new content will be added regularly, so you’ll always be learning the latest in AI.

This schedule is flexible and may change depending on the learning pace of everyone. But don’t worry—once the materials are published, you can go back and learn at your own speed whenever you want.

Phase 1: Python Programming (Starting October)

• Data Types & Variables: Understand basic data types and variables.

• Loops & Iterators: Learn how to iterate over data efficiently.

• Functions & Lambdas: Write reusable code and anonymous functions.

• Lists, Tuples, Sets, Dictionaries: Work with core Python data structures.

• Conditionals: Make decisions using if, elif, and else.

• Exception Handling: Handle errors gracefully.

• Classes & OOP: Grasp object-oriented programming, inheritance, polymorphism, and encapsulation.

Phase 2: Data Analysis with Pandas

• Series & DataFrames: Understand the building blocks of Pandas.

• Editing & Retrieving Data: Learn data selection and modification techniques.

• Importing Data: Import data from CSV, Excel, and databases.

• Grouping Data: Use groupby for aggregate operations.

• Merging & Joining Data: Combine datasets efficiently.

• Sorting & Filtering: Organize and retrieve data.

• Applying Functions to Data: Use functions to manipulate and clean data.

Phase 3: Data Visualization with Matplotlib

• Basic Plotting: Create line plots, scatter plots, and histograms.

• Bar Charts & Pie Charts: Display categorical data.

• Time Series Plots: Visualize data over time.

• Live Data Plotting: Create dynamic visualizations.

Phase 4: Numerical Computing with NumPy

• Creating Arrays: Learn about arrays and their manipulation.

• Array Indexing & Slicing: Access and modify elements in arrays.

• Universal Functions: Perform fast element-wise operations on arrays.

• Linear Algebra & Statistics Functions: Apply matrix operations and statistical computations.

Phase 5: Machine Learning Fundamentals (with Projects)

• ML Life Cycle: Understand the workflow of building machine learning systems.

• Key Algorithms: Explore algorithms like Linear Regression, Decision Trees, Random Forests, and K-Nearest Neighbors.

• Evaluation Metrics: Learn about precision, recall, F1-scores, and the importance of model evaluation.

• Overfitting & Underfitting: Learn how to handle data-related challenges.

• Projects: Apply your knowledge through hands-on projects, solving real-world problems.

Phase 6: Deep Learning Fundamentals (with Projects)

• Neural Networks: Learn how artificial neural networks work.

• Activation Functions: Explore functions like Sigmoid, ReLU, and Tanh.

• Convolutional Neural Networks (CNNs): Understand image-based models and apply them to real-world data.

• Recurrent Neural Networks (RNNs) & LSTMs: Work with sequential data for time series or text.

• Hyperparameter Tuning & Optimization: Fine-tune models for better performance.

• Projects: Implement real-world deep learning models and deploy them into production environments.

Phase 7: Model Deployment & MLOps

• Model Deployment Strategies: Learn how to deploy models using Flask, FastAPI, and cloud platforms.

• Docker & Kubernetes: Containerize your applications and deploy them at scale.

• Kubeflow: Set up workflows for automating ML pipelines.

• MLflow: Track experiments and manage the machine learning lifecycle.

• Airflow: Manage data workflows and model pipelines.

• Cloud-Based Deployment: Deploy your models on platforms like AWS, GCP, and Azure.

• Monitoring & Logging: Use tools like Prometheus and Grafana to monitor model performance and ensure they remain accurate over time.

• CI/CD: Automate the deployment of machine learning models using CI/CD pipelines.

Phase 8: End-to-End Machine Learning Projects

• Complete ML Pipelines: Learn how to build a fully functional machine learning pipeline from data collection to deployment.

• Data Preprocessing: Clean, process, and prepare data for machine learning models.

• Model Building & Training: Implement and train machine learning models tailored to real-world scenarios.

• Model Deployment: Deploy machine learning models into production environments, integrating with APIs and cloud services.

• Monitoring & Maintenance: Understand how to monitor model performance over time and retrain models as needed.

Advanced and Custom Topics

• Advanced NLP & Transformers: Dive deep into cutting-edge natural language processing techniques and transformer architectures.

• Generative AI Models: Explore AI models that generate text, images, and audio, including GANs and diffusion models.

• Custom AI Solutions: Learn how to customize AI models for specialized tasks and industries.

• Suggest a Topic: You can suggest any advanced topics or areas of interest, and we will explore them together as part of the curriculum.
  

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Google AI Essentials

 

Unlock the Power of AI with Google’s AI Essentials Course on Coursera

Artificial Intelligence (AI) is reshaping industries, driving innovation, and solving complex challenges around the globe. As AI becomes an essential part of the tech landscape, learning its core principles has become crucial for both beginners and professionals. Google’s AI Essentials course on Coursera is designed to introduce you to the fundamentals of AI and equip you with the knowledge and skills needed to get started.

If you’re curious about AI and want to learn how it’s used to transform real-world applications, this course offers a comprehensive, beginner-friendly introduction. Let’s dive into what makes this course special and why it’s the perfect starting point for your AI journey.

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Why Learn AI?

AI has rapidly expanded beyond research labs into everyday life. It powers everything from personal voice assistants and recommendation engines to complex medical diagnostics and financial forecasting. AI literacy is becoming a vital skill across industries, making it increasingly valuable for professionals in any field. Learning AI basics gives you an edge in understanding and working with the tools that are shaping the future.

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About Google’s AI Essentials Course

Google, a global leader in AI, has crafted the AI Essentials course on Coursera to help beginners gain foundational knowledge in this field. Created with clarity and simplicity in mind, the course provides learners with an accessible introduction to AI concepts, helping you understand what AI is, its potential, and how it’s applied in the world today.

Key Highlights of the Course:

1. Beginner-Friendly: No prior experience with AI or programming is required, making it ideal for anyone curious about AI.
2. Real-World Applications: You’ll learn how AI solves everyday problems, making it easier to connect theoretical concepts to practical uses.
3. Flexible Schedule: Being online and self-paced, this course allows you to learn on your own time and at your own pace.

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What You’ll Learn

The Google AI Essentials course covers several foundational topics essential to understanding AI and how it’s changing industries. Here’s a quick look at what you’ll learn:

• Understanding AI: Learn what AI is and isn’t, exploring the different branches, such as machine learning and deep learning.
• AI and Everyday Life: Discover how AI powers common applications like recommendation engines, smart assistants, and image recognition systems.
• Intro to Machine Learning: Get introduced to machine learning, a critical subset of AI, and learn about supervised and unsupervised learning techniques.
• Real-World Applications: Understand how AI is transforming sectors like healthcare, finance, and entertainment, showing the vast impact AI has on society.

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Real-World Applications of AI

One of the standout features of this course is its focus on real-world applications, making it relatable for learners from any background. By the end of the course, you’ll gain insights into how AI applications solve problems across various industries:

• Healthcare: AI assists in diagnosing diseases, personalizing treatment plans, and optimizing healthcare operations.
• Finance: Machine learning models help detect fraudulent transactions, assess credit risk, and automate trading strategies.
• Retail: AI enhances customer experiences with personalized recommendations, targeted marketing, and improved inventory management.
• Entertainment: AI algorithms power recommendation systems in streaming platforms, shaping user experience and content discovery.

This approach not only makes learning more engaging but also provides you with a broader understanding of how AI impacts different sectors.

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Why Choose Google’s AI Essentials Course on Coursera?

1. Industry Leader: Google is at the forefront of AI research and applications. Learning directly from Google’s experts provides you with insights and approaches grounded in cutting-edge practices.
2. Hands-On Experience: Although designed for beginners, the course includes practical examples and scenarios to deepen your understanding of AI concepts.
3. Career Boost: With AI playing a critical role in the future of work, having a certification from Google on Coursera enhances your resume, showing employers that you understand AI fundamentals.

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Getting Started

Whether you're a professional looking to enhance your skillset, a student aiming to learn about AI, or just curious about technology, the Google AI Essentials course is a fantastic place to start. It’s a well-rounded introduction to AI fundamentals and applications, and it prepares you to explore further in the world of AI.

Learn more and enroll here: Coursera Google AI Essentials Course.

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Final Thoughts

Artificial Intelligence is more than just a trend; it's a transformative technology that’s changing the world. Google’s AI Essentials course on Coursera offers a clear, beginner-friendly path to understanding AI’s impact, applications, and potential. By completing this course, you’ll gain a foundational knowledge that can serve as a stepping stone to advanced AI studies or applications in your own career.

Whether you’re a beginner or a professional looking to expand your skills, this course will give you the insights you need to understand AI's transformative potential. Embrace the future of technology—start your AI journey today!

Join Free: Google AI Essentials

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Learn to code with AI

 

What you'll learn

How to use AI to build web apps without any programming knowledge

How to deploy your web apps to the web

The very basics of HTML, CSS, and JavaScript

There are 3 modules in this course

Imagine waking up tomorrow as a web developer. What would you want to build?

With AI tools like ChatGPT, you're already a developer, regardless of your experience, if you know how to work with them.

So in this course, you'll build functional, interactive front-end projects while learning how to write effective prompts and debug and refine your code with the help of AI.

No coding experience needed! We'll focus on helping you prototype and build projects with AI's assistance, turning you from a non-coder into a capable problem solver.

By the end of this course, you'll have a collection of mini-projects, newly acquired skills, and a solid foundation to keep building with AI.

You'll work on various projects using HTML, CSS, and JavaScript. Let's do this!

Join for Free: Learn to code with AI

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AI for Everyday Life

 

What you'll learn

Craft an input and output using the prompt engineering methods for generative AI

Apply your knowledge of one prompt engineering method to a real-world scenario

Articulate two methods of prompt engineering for everyday uses.

There are 2 modules in this course

This course takes the mystery generative artificial intelligence (gen-AI) and explains its uses straightforward language for people who want to use it in their everyday lives. 

Knowing how to describe and use generative AI effectively is an Important skillset to successfully engaging in all types of personal communication, from social media posts to emails and blogs. Learners will gain a clear understanding what generative AI is and learn the fundamental skills required to use gen-AI ethically and effectively. Participants will be provided tested methods for prompting an AI Assistant, such as ChatGPT, Claude, and Gemini to yield useful results.

Join for Free: AI for Everyday Life

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Developing AI Applications with Python and Flask

 

What you'll learn

Describe the steps and processes involved in creating a Python application including the application development lifecycle 

Create Python modules, run unit tests, and package applications while ensuring the PEP8 coding best practices

Explain the features of Flask and deploy applications on the web using the Flask framework

Create and deploy an AI-based application onto a web server using IBM Watson AI Libraries and Flask

Join Free: Developing AI Applications with Python and Flask

There are 3 modules in this course

This mini course is intended to apply basic Python skills for developing Artificial Intelligence (AI) enabled applications. In this hands-on project you will assume the role of a developer and perform tasks including:  

- Develop functions and application logic 

- Exchange data using Watson AI libraries

- Write unit tests, and 

- Package the application for distribution. 

You will demonstrate your foundational Python skills by employing different techniques to develop web applications and AI powered solutions. After completing this course, you will have added another project to your portfolio and gained the confidence to begin developing AI enabled applications using Python and Flask, Watson AI libraries, build and run unit tests, and package the application for distribution out in the real world.

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Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

 

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models

Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

- Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models

- Build solutions for real-world computer vision problems using PyTorch

- All the code files are available on GitHub and can be run on Google Colab

Book Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.

The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.

You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production.

By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

- Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer

- Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks

- Implement multi-object detection and segmentation

- Leverage foundation models to perform object detection and segmentation without any training data points

- Learn best practices for moving a model to production

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

Table of Contents

- Artificial Neural Network Fundamentals

- PyTorch Fundamentals

- Building a Deep Neural Network with PyTorch

- Introducing Convolutional Neural Networks

- Transfer Learning for Image Classification

- Practical Aspects of Image Classification

- Basics of Object Detection

- Advanced Object Detection

- Image Segmentation

- Applications of Object Detection and Segmentation

- Autoencoders and Image Manipulation

- Image Generation Using GANs

SOFT Copy: Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Hard Copy: Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition by V Kishore Ayyadevara (Author), Yeshwanth Reddy (Author)

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Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

 

Get to grips with the LangChain framework from theory to deployment and develop production-ready applications.

Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments.

Purchase of the print or Kindle book includes a free PDF eBook.

Key Features

Learn how to leverage LLMs' capabilities and work around their inherent weaknesses

Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges

Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Book Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.

Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

What you will learn

Understand LLMs, their strengths and limitations

Grasp generative AI fundamentals and industry trends

Create LLM apps with LangChain like question-answering systems and chatbots

Understand transformer models and attention mechanisms

Automate data analysis and visualization using pandas and Python

Grasp prompt engineering to improve performance

Fine-tune LLMs and get to know the tools to unleash their power

Deploy LLMs as a service with LangChain and apply evaluation strategies

Privately interact with documents using open-source LLMs to prevent data leaks

Who this book is for

The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.

Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.

Table of Contents

What Is Generative AI?

LangChain for LLM Apps

Getting Started with LangChain

Building Capable Assistants

Building a Chatbot like ChatGPT

Developing Software with Generative AI

LLMs for Data Science

Customizing LLMs and Their Output

Generative AI in Production

The Future of Generative Models

Hard Copy: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

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Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

 

Printed in Color

Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact

Leverage the power of Generative AI with Kaggle Models

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models

Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound

Improve the style and readability of your Notebooks, making them more impactful and compelling

Book Description

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.

For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable.

Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.

What you will learn

Approach a dataset or competition to perform data analysis via a notebook

Learn data ingestion and address issues arising with the ingested data

Structure your code using reusable components

Analyze in depth both small and large datasets of various types

Distinguish yourself from the crowd with the content of your analysis

Enhance your notebook style with a color scheme and other visual effects

Captivate your audience with data and compelling storytelling techniques

Who this book is for

This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to:

Beginners on Kaggle from any background

Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization

Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings

Professionals who already use Kaggle for learning and competing

Table of Contents

Introducing Kaggle and Its Basic Functions

Getting Ready for Your Kaggle Environment

Starting Our Travel - Surviving the Titanic Disaster

Take a Break and Have a Beer or Coffee in London

Get Back to Work and Optimize Microloans for Developing Countries

Can You Predict Bee Subspecies?

Text Analysis Is All You Need

Analyzing Acoustic Signals to Predict the Next Simulated Earthquake

Can You Find Out Which Movie Is a Deepfake?

Unleash the Power of Generative AI with Kaggle Models

Closing Our Journey: How to Stay Relevant and on Top

Hard Copy: Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

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IBM AI Foundations for Business 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: IBM AI Foundations for Business Specialization

Specialization - 3 course series

This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.  

Applied Learning Project

Each of the courses in this specialization include Checks for Understanding, which are designed to assess each learner’s ability to understand the concepts presented as well as use those concepts in actual practice.  Specifically, those concepts are related to introductory knowledge regarding 1) artificial intelligence; 2) data science, and; 3) the AI Ladder.  

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Evaluations of AI Applications in Healthcare

 

What you'll learn

Principles and practical considerations for integrating AI into clinical workflows

Best practices of AI applications to promote fair and equitable healthcare solutions

Challenges of regulation of AI applications and which components of a model can be regulated

What standard evaluation metrics do and do not provide

Join Free: Evaluations of AI Applications in Healthcare

There are 7 modules in this course

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

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Generative AI: Enhance your Data Analytics Career

 

What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

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Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

 

Unlock the Future of AI!

Delve into the world of Generative AI with Daneyal Anis' groundbreaking book, "The Ultimate Step by Step Guide to ChatGPT Using Python". If you've ever been intrigued by how machine learning, data science, and artificial intelligence can be harnessed for tangible results, this guide is your key.

In today's digital age, the fields of Artificial Intelligence (AI), Machine Learning (ML), and Data Science are not just buzzwords; they are the foundational pillars that drive innovations across industries. From big tech giants to emerging startups, AI-powered solutions are the backbone of breakthroughs.

Here's what you'll discover within this comprehensive guide:

How the union of Python, the most popular language in data science, and GPT is revolutionizing the tech space.

Deep dives into the power and potential of GPT - learning its strengths, nuances, and applications.

Strategies for monetizing your AI and ML skills, unveiling the golden opportunities that await in the AI space.

Building robust AI portfolios and utilizing automation tools for efficiency and scalability.

Crafting AI profiles, including creating dynamic chatbots using ChatGPT.

Navigating the ethical considerations and responsibilities in the AI domain.

Beyond just the knowledge, this guide is crafted to action. That's why Daneyal also offers an exclusive 90-Day Plan to make passive income using Generative AI, leading you from the theoretical to practical monetization of your skills. Plus, get exclusive access to an in-depth Step by Step Course for those wanting a hands-on learning experience.

Editorial Reviews

The Digital Era is here, and AI is at its forefront. Equip yourself with the knowledge, tools, and strategies to not only participate in this revolution but also to thrive and lead. With "The Ultimate Step by Step Guide to ChatGPT Using Python", your transformative journey in the realm of AI is set on a promising path.

Hard Copy: Ultimate Step by Step Guide to ChatGPT Using Python: 90 Day Plan to Make Passive Income with Generative AI (Ultimate Step by Step Guide to Machine Learning Book 4)

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Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

 

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python

Key Features

Get up and running with artificial intelligence in no time using hands-on problem-solving recipes

Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images

Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more

Book Description

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.

Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.

By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.

What you will learn

Implement data preprocessing steps and optimize model hyperparameters

Delve into representational learning with adversarial autoencoders

Use active learning, recommenders, knowledge embedding, and SAT solvers

Get to grips with probabilistic modeling with TensorFlow probability

Run object detection, text-to-speech conversion, and text and music generation

Apply swarm algorithms, multi-agent systems, and graph networks

Go from proof of concept to production by deploying models as microservices

Understand how to use modern AI in practice

Who this book is for

This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Table of Contents

Getting Started with Artificial Intelligence in Python

Advanced Topics in Supervised Machine Learning

Patterns, Outliers, and Recommendations

Probabilistic Modeling

Heuristic Search Techniques and Logical Inference

Deep Reinforcement Learning

Advanced Image Applications

Working with Moving Images

Deep Learning in Audio and Speech

Natural Language Processing

Artificial Intelligence in Production

Hard Copy: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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Generative AI Essentials: Overview and Impact

 

What you'll learn

Learn how generative AI works

Explore the benefits and drawbacks of generative AI

Learn how generative AI can integrate into our daily lives

Join Free: Generative AI Essentials: Overview and Impact

There is 1 module in this course

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like. This course brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI on our current society and its implications for the future.

This course is licensed CC BY-SA 4.0 with the exclusion of the course image.

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Introduction to AI in the Data Center

 

What you'll learn

What is AI and AI use cases, Machine Learning, Deep Leaning, and how training and inference happen in a Deep Learning Workflow.

The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.    

Become familiar with deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem or cloud.

Requirements for multi-system AI clusters and considerations for infrustructure planning, including servers, networking, storage and tools. 

Join Free: Introduction to AI in the Data Center

There are 4 modules in this course

Welcome to the Introduction to AI in the Data Center Course!

As you know, Artificial Intelligence, or AI, is transforming society in many ways. 

From speech recognition to improved supply chain management, AI technology provides enterprises with the compute power, tools, and algorithms their teams need to do their life’s work. 

But how does AI work in a Data Center? What hardware and software infrastructure are needed? 

These are some of the questions that this course will help you address. 

This course will cover an introduction to concepts and terminology that will help you start the journey to AI and GPU computing in the data center. 

You will learn about:

* AI and AI use cases, Machine Learning, Deep Learning, and how training and inference happen in a Deep Learning Workflow. 

* The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.

* Deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment. ​ 

* Requirements for multi-system AI clusters​​, considerations for infrastructure planning, including servers, networking, and storage and tools for cluster management, monitoring and orchestration. 

This course is part of the preparation material for the NVIDIA Certified Associate - ”AI in the Data Center” certification. 

This certification will take your expertise to the next level and support your professional development.

Who should take this course?

* IT Professionals

* System and Network Administrators

* DevOps

* Data Center Professionals

No prior experience required.

This is an introduction course to AI and GPU computing in the data center. 

To learn more about NVIDIA’s certification program, visit: 

https://academy.nvidia.com/en/nvidia-certified-associate-data-center/

So let's get started!

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CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

 

What you'll learn

Learn about the business problems that AI/ML can solve as well as the specific AI/ML technologies that can solve them.  

Learn important tasks that make up the workflow, including data analysis and model training and about how machine learning tasks can be automated. 

Use ML algorithms to solve the two most common supervised problems regression and classification, and a common unsupervised problem: clustering.

Explore advanced algorithms used in both machine learning and deep learning. Build multiple models to solve business problems within a workflow.

Join Free:CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Professional Certificate - 5 course series

The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP. 

AI and ML have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. 

The specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam. 

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work.  Projects include: 

Create an AI project outline

Follow a machine learning workflow to predict demand 

Build a regression, classification, or clustering model

Build a convolutional neural network (CNN)

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Artificial Intelligence (AI) Education for Teachers

 

What you'll learn

Compare AI with human intelligence, broadly understand how it has evolved since the 1950s, and identify industry applications

Identify and use creative and critical thinking, design thinking, data fluency, and computational thinking as they relate to AI applications

Explain how the development and use of AI requires ethical considerations focusing on fairness, transparency, privacy protection and compliance

Describe how thinking skills embedded in Australian curricula can be used to solve problems where AI has the potential to be part of the solution

Join Free:Artificial Intelligence (AI) Education for Teachers

There are 6 modules in this course

Today’s learners need to know what artificial intelligence (AI) is, how it works, how to use it in their everyday lives, and how it could potentially be used in their future. Using AI requires skills and values which extend far beyond simply having knowledge about coding and technology.

This course is designed by teachers, for teachers, and will bridge the gap between commonly held beliefs about AI, and what it really is. AI can be embedded into all areas of the school curriculum and this course will show you how. 

This course will appeal to teachers who want to increase their general understanding of AI, including why it is important for learners; and/or to those who want to embed AI into their teaching practice and their students’ learning. There is also a unique opportunity to implement a Capstone Project for students alongside this professional learning course.

Macquarie School of Education at Macquarie University and IBM Australia have collaborated to create this course which is aligned to AITSL ‘Proficient Level’ Australian Professional Standards at AQF Level 8.

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Artificial Intelligence on Microsoft Azure

 

What you'll learn

How to identify guiding principles for responsible AI

How to identify features of common AI workloads

Join Free:Artificial Intelligence on Microsoft Azure

There is 1 module in this course

Whether you're just beginning to work with Artificial Intelligence (AI) or you already have AI experience and are new to Microsoft Azure, this course provides you with everything you need to get started. Artificial Intelligence (AI) empowers amazing new solutions and experiences; and Microsoft Azure provides easy to use services to help you build solutions that seemed like science fiction a short time ago; enabling incredible advances in health care, financial management, environmental protection, and other areas to make a better world for everyone.

In this course, you will learn the key AI concepts of machine learning, anomaly detection, computer vision, natural language processing, and conversational AI. You’ll see some of the ways that AI can be used and explore the principles of responsible AI that can help you understand some of the challenges facing developers as they try to create ethical AI solutions. 

This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the first course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains.  This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them.

This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.  To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology,  general technology concepts, including concepts of machine learning and artificial intelligence.

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AI, Business & the Future of Work

 

What you'll learn

How AI can give you better information upon which to make better decisions.

How AI can help you automate processes and become more efficient.

How AI will impact your industry so that you can avoid the pitfalls and seize the benefits.

Join Free:AI, Business & the Future of Work

There are 4 modules in this course

This course from Lunds university will help you understand and use AI so that you can transform your organisation to be more efficient, more sustainable and thus innovative. 

The lives of people all over the world are increasingly enhanced and shaped by artificial intelligence. To organisations there are tremendous opportunities, but also risks, so where do you start to plan for AI, business and the future of work?

Whether you are in the public or private sector, in a large organisation or a small shop, AI has a growing impact on your business. Most organisations don’t have a strategy in place for how to make AI work for them. 

The teacher, Anamaria Dutceac Segesten, will guide you through the topics with short lectures, interviews and interactive exercises meant to get you thinking about your own context.

12 industry professionals, AI experts and thought leaders from different industries have been interviewed and will complement the short lectures to give you a broad overview of perspectives on the topics. You will meet:

Kerstin Enflo

Professor in Economic History

Lund University

Dr. Irene Ek

Founder

Digital Institute

Samuel Engblom

Policy Director

The Swedish Confederation of Professional Employees

Pelle Kimvall

Lead Solution Ideator

AFRY X

Joakim Wernberg

Research Director, Digitalisation and Tech Policy

Swedish Entrepreneurship Forum

Marcus Henriksson

Empathic Leader of AI & Automation and Digital Business Development

Empathic

Johan Grundström Eriksson

Board Advisor, Innovation Management & Corporate Governance

Founder & Chairman, aiRikr Innovation AB

Jakob Svensson

Professor in Media and Communication Studies

Malmö University

Ulrik Franke

Senior Researcher

RISE Research Institutes of Sweden

Björn Lorentzon

Nordic Growth Lead

Sympa

Anna Felländer

Founder 

AI Sustainability Center

Prof. Fredrik Heintz

Associate Professor of Computer Science

Linköping University

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