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Solution and Explanation :
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
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
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
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
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
.png)
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.
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.
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
%20Specialization.PNG)
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.
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.
- 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.
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
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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].
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.
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.
Demonstrate proficiency of SQL syntax and explain how it’s used to interact with a database.
Create databases from scratch and learn how to add, manage and optimize your database.
Write database driven applications in Python to connect clients to MySQL databases.
Develop a working knowledge of advanced data modeling concepts.
Professional Certificate - 9 course series
Want to get started in the world of database engineering? This program is taught by industry-recognized experts at Meta. You’ll learn the key skills required to create, manage and manipulate databases, as well as industry-standard programming languages and software such as SQL, Python, and Django used for supporting outstanding websites and apps like Facebook, Instagram and more.
In this program, you’ll learn:
Core techniques and methods to structure and manage databases.
Advanced techniques to write database driven applications and advanced data modeling concepts.
MySQL database management system (DBMS) and data creation, querying and manipulation.
How to code and use Python Syntax
How to prepare for technical interviews for database engineer roles.
Any third-party trademarks and other intellectual property (including logos and icons) referenced in the learning experience remain the property of their respective owners. Unless specifically identified as such, Coursera’s use of third-party intellectual property does not indicate any relationship, sponsorship, or endorsement between Coursera and the owners of these trademarks or other intellectual property.
You’ll complete a series of 5 projects in which you will demonstrate your proficiency in different aspects of database engineering.
You’ll demonstrate your skills with database normalization by structuring your own relational database by defining relationships between entities and developing relational schema.
This is followed by a stored procedure project in which you’ll demonstrate your competency in SQL automation by writing a stored procedure to solve real world problems. After developing your skills in Python, you’ll create a Python application to administer a MySQL database and program its interactions with clients.
In the next project, you are required to apply data modeling to a real-world project by enacting advanced data modeling concepts such as automation, storage and optimization.
Finally, you’ll be tasked with creating a MySQL database solution for an app by drawing on the knowledge and skills that they have gained throughout the program.
Make use of unicode characters and strings
Understand the basics of building a search engine
Select and process the data of your choice
Create email data visualizations
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
Use regular expressions to extract data from strings
Understand the protocols web browsers use to retrieve documents and web apps
Retrieve data from websites and APIs using Python
Work with XML (eXtensible Markup Language) data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
Explain the principles of data structures & how they are used
Create programs that are able to read and write data from files
Store data as key/value pairs using Python dictionaries
Accomplish multi-step tasks like sorting or looping using tuples
This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.
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clcoding = '786'
*coding_list, = clcoding
print(coding_list)
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The code you provided creates a list of individual characters from the string clcoding and then prints the list. Here's a breakdown:
clcoding = '786'
# Convert the string 'clcoding' into a list of individual characters
char_list = list(clcoding)
# Print the resulting list
print(char_list)
When you run this code, it will output:
['7', '8', '6']
This is because the list() function is used to convert the string '786' into a list of its individual characters.
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num = [10, 20, 30, 40, 50]
num[1:4] = [15, 25, 35]
print(num)
Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?
Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).
Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.
Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.
In a quest to understand how video games themselves are implemented, you'll explore the design of such childhood games as:
Super Mario Bros.
Pong
Flappy Bird
Breakout
Match 3
Legend of Zelda
Angry Birds
Pokémon
3D Helicopter Game
Dreadhalls
Portal
The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful?
This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, machine learning, large language models, and other topics in artificial intelligence as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.
Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.
HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.
HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.
Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.
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print(1+True)
In Python, True is treated as 1 when used in numeric operations. Therefore, 1 + True is equivalent to 1 + 1, and the result is 2.
So, the output of print(1 + True) will be:
2
Answer :
Default value of sep in print( ) - ' '
Default value of end in print( ) - \n
Easiest way to print output - Using fstring
Return type of split( ) - str
print('{num:>5}') - Right justify num in 5 columns
print('{num:<5}') - Left justify num in 5 columns
Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.
Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.
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In the given code, you have two tuples, a and b. a is a tuple containing three elements, and b is a tuple containing only one element. When you try to concatenate these tuples using the + operator, you might encounter an error.
Let's analyze the code:
a = (10, 20, 30)
b = (40)
print(a + b)
Here, the variable b is not a tuple; it's just an integer because a single-element tuple should have a comma after the element. To create a single-element tuple, you should write it as follows:
b = (40,)
Now, if you want to concatenate the two tuples, you can use the + operator:
a = (10, 20, 30)
b = (40,)
print(a + b)
The output will be:
(10, 20, 30, 40)
This code creates a new tuple by concatenating the elements of a and b.
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In Python, the is keyword is used to check if two variables refer to the same object in memory, while the == operator is used to check if the values of the two variables are equal.
In your example:
a = "Hello"
b = "Hello"
print(f"a is b: {a is b}")
print(f"a == b: {a == b}")
The output will be:
a is b: True
a == b: True
This is because string literals (like "Hello") are interned in Python, meaning that the interpreter will reuse the same object in memory for equal string literals. So, both a and b refer to the same string object in memory, and hence a is b is True. The == comparison also evaluates to True because the values of a and b are the same.
a = (1, 2, 3) # tuple
b = (1, 2, 3)
print(f"a is b: {a is b}") # True
a = {1, 2, 3} # set
b = {1, 2, 3}
print(f"a is b: {a is b}") # False
a = 1 + 2j # complex number
b = 1 + 2j
print(f"a is b: {a is b}") # True
a = (1, 2, 3) # tuple
b = (1, 2, 3)
print(f"a is b: {a is b}") # True
Here, you are creating two tuples a and b with the same values (1, 2, 3). Tuples are immutable in Python, and for small immutable objects like tuples, Python often optimizes and reuses the same object in memory. Therefore, a is b is True because both variables reference the same tuple object.
a = {1, 2, 3} # set
b = {1, 2, 3}
print(f"a is b: {a is b}") # False
In this part, you are creating two sets a and b with the same values {1, 2, 3}. Unlike tuples, sets are mutable in Python. The optimization for reusing the same object in memory doesn't typically happen with mutable objects. Therefore, a is b is False because each set is a distinct object in memory.
a = 1 + 2j # complex number
b = 1 + 2j
print(f"a is b: {a is b}") # True
In the last part, you are creating two complex numbers a and b with the same values 1 + 2j. Similar to tuples, complex numbers are immutable, so Python optimizes and reuses the same object in memory. Therefore, a is b is True because both variables reference the same complex number object.
In summary, the behavior of is and == depends on the type of objects being compared and whether they are mutable or immutable. For immutable objects, like tuples and complex numbers in your examples, is may evaluate to True because Python may reuse the same object in memory for efficiency. However, for mutable objects, like sets, is is more likely to evaluate to False because each object is distinct in memory. It's generally safer to use == for equality comparisons unless you specifically want to check object identity.
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a = 10
b = 10
print(f"a is b: {a is b}")
print(f"a == b: {a == b}")
the code line by line:
a = 10
Here, you are assigning the value 10 to the variable a. This means that the variable a now refers to the integer object 10.
b = 10
Similarly, you are assigning the value 10 to the variable b. Like before, the variable b now refers to the same integer object 10. In Python, for small integers, the interpreter often optimizes and reuses the same object in memory.
print(f"a is b: {a is b}")
This line prints the result of the identity comparison using the is operator. It checks if the variables a and b refer to the exact same object in memory. Since integers are often optimized for small values, a is b will usually be True because both variables reference the same 10 object in memory.
print(f"a == b: {a == b}")
This line prints the result of the equality comparison using the == operator. It checks if the values of a and b are equal. Since both a and b have the value 10, a == b will be True.
In summary, the code demonstrates the difference between the is operator, which checks identity (whether two variables reference the exact same object), and the == operator, which checks equality (whether the values of two variables are the same). In this specific case with small integers, both comparisons evaluate to True.
#!/usr/bin/env python
# coding: utf-8
# # Calculate derivatives in Python
# In[9]:
import sympy as sym
# In[11]:
x = sym.Symbol('x') # Symbolize X
func= x**4+4*x**2+5*x-6 # Function
sym.Derivative(func, x) # Derivative expression
# In[12]:
sym.Derivative(func, x, evaluate=True) # Calculate derivative of func
# In[13]:
func.diff(x) # Or use this for the same
# In[14]:
# Create functions with lambdify
expr= sym.lambdify(x, func)
expr_der=sym.lambdify(x, func.diff(x))
# In[15]:
print(f'value of func at x=5: {expr(5)}')
print(f'derivative of func at x=5: {expr_der(5)}')
#!/usr/bin/env python
# coding: utf-8
# # 1. Using Matplotlib library
# In[1]:
import matplotlib.pyplot as plt
# Sample data
categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']
values = [10, 25, 15, 30]
# Create a bar graph
plt.bar(categories, values)
# Adding labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Graph Example')
# Show the graph
plt.show()
#clcoding.com
# # 2. Using Seaborn library
# In[2]:
import seaborn as sns
import matplotlib.pyplot as plt
# Sample data
categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']
values = [10, 25, 15, 30]
# Create a bar plot using Seaborn
sns.barplot(x=categories, y=values)
# Adding labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot Example')
# Show the plot
plt.show()
#clcoding.com
# # 3. Using Plotly library
# In[3]:
import plotly.express as px
# Sample data
categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']
values = [10, 25, 15, 30]
# Create an interactive bar graph using Plotly
fig = px.bar(x=categories, y=values, labels={'x': 'Categories', 'y': 'Values'}, title='Bar Graph Example')
# Show the plot
fig.show()
#clcoding.com
# # 4. Using Bokeh library
# In[4]:
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
# Sample data
categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']
values = [10, 25, 15, 30]
# Create a bar graph using Bokeh
p = figure(x_range=categories, title='Bar Graph Example', x_axis_label='Categories', y_axis_label='Values')
p.vbar(x=categories, top=values, width=0.5)
# Show the plot in a Jupyter Notebook (or use output_file for standalone HTML)
output_notebook()
show(p)
#clcoding.com
# In[ ]:
.png)
The remove() method in a set in Python removes the specified element. In your code:
s = set([1, 0, 2, 0, 3])
s.remove(0)
print(s)
It removes the element 0 from the set s. The output will be a set without the removed element:
{1, 2, 3}
g = [1, 0, 2, 0, 3]
g.remove(0)
print(g)
A) [1, 0, 2, 3]
B) [1, 2, 3]
C) [1, 2, 0, 3]
D) [0, 2, 0, 3]
The remove() method in Python removes the first occurrence of a specified value from a list. In the code you provided:
g = [1, 0, 2, 0, 3]
g.remove(0)
print(g)
The element 0 is removed from the list g, and the updated list is then printed. Therefore, the output will be:
[1, 2, 0, 3]
.png)
Let's break down the code:
my_list = [60, 70, 80, 90, 100]
result = my_list[4::-1]
print(result)
In this code, my_list is a list containing the elements [60, 70, 80, 90, 100]. The expression my_list[4::-1] is a slicing operation with the following parameters:
4 is the starting index, and it starts from the last element (index 4).
:: indicates the slicing with a step of -1, which means it goes backward.
So, my_list[4::-1] will start from index 4 and go backward with a step of 1, including the element at index 4 itself. Therefore, it will select elements in reverse order.
The result will be a new list containing the elements [100, 90, 80, 70, 60]. When you print the result, you'll get:
[100, 90, 80, 70, 60]
.png)
result = min(0.0, -0.0)
print(result)
.png)
my_list = [1, 2, 3, 4, 5]
result = my_list[1:4:2]
print(result)
Python Coding is not a book you can read while relaxing on the couch. This book is for those that are ready to start working right away to write your own codes. I do not recommend this book if you are the type of person who reads a book once and never opens it again because you think you have mastered the book's technical contents. Tech books are different from novels and other non-fiction books. They demand more than merely one reading. Buy this book if you have made up your mind to read it and practice it again and again.
This book will compel you to step into the practical world. What makes this book different from the other books is its specific features and contents. Let's take a look at both.
Features of the book:
You'll discover...
If you're interested in the practical application of learning to code with Python, then this book is for you.
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