A simple text-based guessing game in Python

 

import random

def guess_the_number():

    # Generate a random number between 1 and 100

    secret_number = random.randint(1, 100)

    print("Welcome to the Guess the Number game!")

    print("I have selected a number between 1 and 100. Can you guess it?")

    attempts = 0

    while True:

        try:

            # Get player's guess

            guess = int(input("Enter your guess: "))

            attempts += 1

            # Check if the guess is correct

            if guess == secret_number:

                print(f"Congratulations! You guessed the number in {attempts} attempts.")

                break

            elif guess < secret_number:

                print("Too low! Try again.")

            else:

                print("Too high! Try again.")

        except ValueError:

            print("Please enter a valid number.")

if __name__ == "__main__":

    guess_the_number()

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Bar Graph plot using different Python Libraries

#!/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[ ]:

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Python Workout: 50 ten-minute exercises

 

The only way to master a skill is to practice. In Python Workout, author Reuven M. Lerner guides you through 50 carefully selected exercises that invite you to flex your programming muscles. As you take on each new challenge, you’ll build programming skill and confidence.

Summary
The only way to master a skill is to practice. In Python Workout, author Reuven M. Lerner guides you through 50 carefully selected exercises that invite you to flex your programming muscles. As you take on each new challenge, you’ll build programming skill and confidence. The thorough explanations help you lock in what you’ve learned and apply it to your own projects. Along the way, Python Workout provides over four hours of video instruction walking you through the solutions to each exercise and dozens of additional exercises for you to try on your own.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
To become a champion Python programmer you need to work out, building mental muscle with your hands on the keyboard. Each carefully selected exercise in this unique book adds to your Python prowess—one important skill at a time.

About the book
Python Workout presents 50 exercises that focus on key Python 3 features. In it, expert Python coach Reuven Lerner guides you through a series of small projects, practicing the skills you need to tackle everyday tasks. You’ll appreciate the clear explanations of each technique, and you can watch Reuven solve each exercise in the accompanying videos.

What's inside

50 hands-on exercises and solutions
Coverage of all Python data types
Dozens more bonus exercises for extra practice

About the reader
For readers with basic Python knowledge.

About the author
Reuven M. Lerner teaches Python and data science to companies around the world.

Table of Contents

1 Numeric types

2 Strings

3 Lists and tuples

4 Dictionaries and sets

5 Files

6 Functions

7 Functional programming with comprehensions

8 Modules and packages

9 Objects

10 Iterators and generators

Hard Copy : Python Workout: 50 ten-minute exercises

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Python Coding challenge - Day 115 | What is the output of the following Python Code?

 

Code :

list1 = [2, 3, 9, 12, 4]

list1.insert(4, 17)

list1.insert(2, 23)

print(list1[-4])  

Explanation:

Let's break down the code step by step:

Initial list: [2, 3, 9, 12, 4]
list1.insert(4, 17): Insert the value 17 at index 4. The list becomes [2, 3, 9, 12, 17, 4].
list1.insert(2, 23): Insert the value 23 at index 2. The list becomes [2, 3, 23, 9, 12, 17, 4].
print(list1[-4]): Print the element at the fourth position from the end of the list. The element at index -4 is 9.
So, the output of the code will be:

9

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10 Levels of Writing Python Functions

Level 1: Basic Function

Level 2: Function with Parameters

Level 3: Return Values

Level 4: Default Parameters

Level 5: Docstrings

Level 6: Variable Scope

Level 7: Recursion

Level 8: Lambda Functions

Level 9: Function Decorators

Level 10: Advanced Functions

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How much do you know about list in Python?

1. Which value is used to represent the first index of

list?

(a) 1 (b) 0

(c) −1 (d) a

Ans. (b) To access the list’s elements, index number is used. The

index number should be an integer. Index of 0 refers to first

element, 1 refers to second element and so on.

2. Choose the output of following Python code.

1= list ()

print ( 1)

(a) [] (b) ()

(c) [,] (d) Empty

Ans. (a) Empty list can be created in Python using []. To create

empty list, list () is also used.

3. Suppose list

1= [10, 20, 30, 40, 50, 60, 70]

print( 1[−3])

(a) 30 (b) 50

(c) 40 (d) Error

Ans. (b) The index of −1 refers to the last element, −2 refers to the

second last element and so on. Hence, −3 refers to third last

element, i.e. 50.

4. Choose the output from following code.

list1 = [‘A’, ‘R’, ‘I’,‘H’,‘A’,‘N’,‘T’]

print (list1 [7])

(a) T (b) N

(c) A (d) Error

Ans. (d) In the given code, we are trying to access 8th element

from the list which does not exist as we are having total 7

elements for which the last index is 6. So, Python will give

an IndexError.

5. Which function is used to insert an element at

specified position in the list?

(a) extend () (b) append ()

(c) insert () (d) add ()

Ans. (c) insert () function is used to insert an element at specified

position in the list. This method takes two arguments : one

for index number and second for element value.

Syntax list_name.insert(index, element)

6. Choose the correct option for the following.

l1 = [2, 5, 7]

l = l1+ 5

print (l)

(a) [7, 10, 12]

(b) [2, 5, 7, 5]

(c) [5, 2, 5, 7]

(d) TypeError

Ans. (d) + operator cannot add list with other type as number or

string because this operator is used only with list types.

So, it will give TypeError as it can only concatenate list (not

“int”) to list.

7. What is the output of following code?

l1 = [3, 2, 6]

l = l1 * 2

print (l)

(a) [3, 2, 6, 3, 2, 6]

(b) [6, 4, 12]

(c) [3, 4, 12]

(d) TypeError

Ans. (a) * operator can repeat the elements of the list.

Syntax list = list1 * digit

8. Which of the following is true regarding lists in

Python?

(a) Lists are immutable.

(b) Size of the lists must be specified before its initialisation.

(c) Elements of lists are stored in contiguous memory

location.

(d) size(list1) command is used to find the size of lists.

Ans. (c) Elements of lists are stored in contiguous memory

location, so it is true regarding lists in Python.

9. Suppose list1 is [56, 89, 75, 65, 99], what is the

output of list1 [− 2]?

(a) Error (b) 75

(c) 99 (d) 65

Ans. (d) −1 corresponds to the last index in the list, −2 represents

the second last element and so on.

So, the output for list1 [− 2] is 65 because 65 is second last

element of list1.

10. Identify the output of following code.

List1=[1, 2, 3, 7, 9]

L=List1.pop(9)

print(L)

(a) Syntax error (b) 9

(c) [1, 2, 3, 7] (d) None of these

Ans. (a) In pop(9), parentheses put index number instead of

element. In the given list, maximum index number is 4, then

9 is out of index range.

11. Suppose list1 is [2445,133,12454,123], what is the

output of max(list1)?

(a) 2445 (b) 133

(c) 12454 (d)123

Ans. (c) max() returns the maximum element in the list. From

given options, 12454 is the element with maximum value.

12. To add a new element to a list, which command will

we use?

(a) list1.add(8)

(b) list1.append(8)

(c) list1.addLast(8)

(d) list1.addEnd(8)

Ans. (b) We use the function append() to add an element to the list.

13. What will be the output of the following Python

code?

list1=[9, 5, 3, 5, 4]

list1[1:2]=[7,8]

print(list1)

(a) [9,5, 3, 7, 8] (b) [9, 7, 8, 3, 5, 4]

(c) [9,[ 7, 8], 3, 5,4] (d) Error

Ans. (b) In the piece of code, slice assignment has been

implemented. The sliced list is replaced by the assigned

elements in the list.

14. Consider the declaration a=[2, 3, ‘Hello’, 23.0].

Which of the following represents the data type of

‘a’?

(a) String (b) Tuple

(c) Dictionary (d) List

Ans. (d) List contains a sequence of heterogeneous elements

which store integer, string as well as object. It can created to

put elements separated by comma (,) in square brackets [].

15. Identify the output of the following Python

statement.

x=[[1, 2, 3, 4], [5, 6, 7, 8]]

y=x [0] [2]

print(y)

(a) 3 (b) 4

(c) 6 (d) 7

Ans. (a) x is a list, which has two sub-lists in it. Elements of first

list will be represented by [0] [i] and elements of second list

will be represented by [1] [i].

16. Which method will empty the entire list?

(a) pop() (b) clear()

(c) sort() (d) remove()

Ans. (b) clear() method is used to remove all the items of a list.

This method will empty the entire list.

Syntax

list_name.clear()

17. Which of the following allows us to sort the list

elements in descending order?

(a) reverse = True

(b) reverse = False

(c) sort (descending)

(d) sort. descending

Ans. (a) sort() is used to sort the given list in ascending order. The

sort() has an argument called reverse = True. This allows us

to sort the list elements in descending order.

18. Identify the correct output.

>>>l1 = [34, 65, 23, 98]

>>>l1. insert(2, 55)

>>>l1

(a) [34, 65, 23, 55] (b) [34, 55, 65, 23, 98]

(c) [34, 65, 55, 98] (d) [34, 65, 55, 23, 98]

Ans. (d) insert() is used to insert an element at specified position

in the list. This method takes two arguments : one for index

number and second for element value.

Syntax

list_name.insert(index, element)

19. Find the output from the following code.

list1=[2, 5, 4, 7, 7, 7, 8, 90]

del list1[2 : 4]

print(list1)

(a) [2, 5, 7, 7, 8, 90] (b) [5, 7, 7, 7, 8, 90]

(c) [2, 5, 4, 8, 90] (d) Error

Ans. (a) del keyword is used to delete the elements from the list.

20. Slice operation is performed on lists with the use of

(a) semicolon (b) comma

(c) colon (d) hash

Ans. (c) In Python list, there are multiple ways to print the whole

list with all the elements, but to print a specific range of

elements from the list, we use slice operation. Slice

operation is performed on lists with the use of colon (:).

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Play Fidget Spinner game using Python

from turtle import *

speed(0)

bgcolor('lightgray')

title('Fidget Spinner Game')

spinner_radius = 250  # Increased radius

spinner_speed = 0

spinner_colors = ['red', 'green', 'blue']

def create_spinner():

    penup()

    goto(0, 0)  # Center the spinner

    pendown()

    for color in spinner_colors:

        fillcolor(color)

        begin_fill()

        circle(spinner_radius/len(spinner_colors))

        end_fill()

        right(360 / len(spinner_colors))

def spin_spinner(x, y):

    global spinner_speed

    spinner_speed += 10

def stop_spinner(x, y):

    global spinner_speed

    spinner_speed = max(0, spinner_speed - 5)

def animate_spinner():

    global spinner_speed

    clear()

    create_spinner()

    right(spinner_speed)

    update()

    ontimer(animate_spinner, 20)

def main():

    setup(600, 600)

    hideturtle()

    tracer(False)

   

    create_spinner()

    onscreenclick(spin_spinner, 1)

    onscreenclick(stop_spinner, 3)

    animate_spinner()

    done()

if __name__ == "__main__":

    main()

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What is the output of following Python code?

 What is the output of following Python code?

a =[[0, 1, 2], [3, 4, 5, 6]]

b =a[1] [2]

print (b)

Answer with Explanation:

Let's break down your code step by step:

a = [[0, 1, 2], [3, 4, 5, 6]]

Here, you have defined a list a that contains two sub-lists. The first sub-list is [0, 1, 2], and the second sub-list is [3, 4, 5, 6]. So, a is a list of lists.

b = a[1][2]

This line of code is accessing elements in the nested list. Let's break it down:

a[1] accesses the second sub-list in a. In Python, indexing starts from 0, so a[1] refers to [3, 4, 5, 6].

[2] then accesses the third element in this sub-list. Again, indexing starts from 0, so a[1][2] refers to the element at index 2 in the sub-list [3, 4, 5, 6].

As a result, b is assigned the value 5, which is the third element in the second sub-list.

print(b)

Finally, the print(b) statement outputs the value of b, which is 5, to the console.

So, when you run this code, the output will be:

5

This is because b contains the value at index [1][2] in the nested list a, which is 5.

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Python Coding challenge - Day 114 | What is the output of the following Python Code?

 

Code: 

def fun(num):

    if num > 10:

        return num - 10

    return fun(fun(num + 11))

print(fun(5))

Solution and Explanantion: 

The output of the code is 1.

Here's a breakdown of how the code works:

Function Definition:

The code defines a recursive function named fun that takes a single argument, num.

Base Case:

The if statement checks if num is greater than 10. If it is, the function returns num - 10. This is the base case that stops the recursion.

Recursive Calls:

If num is not greater than 10, the function calls itself twice, creating two recursive calls:

fun(fun(num + 11))

The inner call fun(num + 11) adds 11 to num and passes the result to the fun function again.

The outer call takes the result of the inner call and passes it to the fun function once more.

Execution Flow:

When you call fun(5), here's what happens:

5 is not greater than 10, so the function calls fun(fun(16)).

The inner call fun(16) now executes. 16 is greater than 10, so the base case triggers and returns 16 - 10 = 6.

The outer call now has 6 as its argument. 6 is not greater than 10, so it calls fun(fun(17)).

The inner call fun(17) executes. 17 is greater than 10, so the base case returns 17 - 10 = 7.

The outer call now has 7 as its argument. 7 is not greater than 10, so it calls fun(fun(18)).

This process continues until fun(25) is reached. At this point, the base case returns 25 - 10 = 15.

The values start propagating back through the recursive calls, eventually leading to the final return value of 1.

Key Points:

The code demonstrates recursion, where a function calls itself within its own definition.

The base case is crucial to prevent infinite recursion and ensure the function eventually terminates.

Recursive functions can be helpful for solving problems that can be broken down into smaller, self-similar subproblems. 

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Which of the following is not correct for to create an empty list, empty tuple, empty set and empty dictionary?

 

All are correct 

l = []      # Empty list

t = ()      # Empty tuple

s = set()   # Empty set

d = {}      # Empty dictionary

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Which of the following are valid return statements?

 

Which of the following are valid return statements?

return (a, b, c)

return a + b + c

return a, b, c

All three of the provided return statements are valid in Python, but they have different implications:

return (a, b, c): This returns a single tuple containing the values of a, b, and c. For example, if a = 1, b = 2, and c = 3, the function would return the tuple (1, 2, 3).

return a + b + c: This returns the sum of a, b, and c. If a = 1, b = 2, and c = 3, the function would return the value 6.

return a, b, c: This returns a tuple containing the values of a, b, and c. This is a more concise way of expressing the first option. For the same example values, it would return the tuple (1, 2, 3).

Choose the one that fits your specific use case and the desired return value format in your function.

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Python Coding challenge - Day 113 | What is the output of the following Python Code?

 

The code you provided is attempting to generate a Fibonacci sequence and append the values to the list lst. However, it seems like there's a small mistake in your code. The range in the list comprehension should go up to 4, not 5, to generate the Fibonacci sequence up to the 4th element. Here's the corrected code:

lst = [0, 1]

[lst.append(lst[k - 1] + lst[k - 2]) for k in range(2, 5)]

print(lst)

After running this code, lst will be:

[0, 1, 1, 2, 3]

This list represents the Fibonacci sequence up to the 4th element. The initial list [0, 1] is extended by three more elements generated by adding the last two elements of the list to get the next one.

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How to write program that generates a list of integer coordinates for all points in the first quadrant from (1, 1) to (5, 5) using list comprehension?

 

You can use list comprehension to generate a list of integer coordinates for all points in the first quadrant from (1, 1) to (5, 5). Here's an example in Python:

coordinates = [(x, y) for x in range(1, 6) for y in range(1, 6)]

print(coordinates)

This code will produce a list of tuples where each tuple represents a coordinate in the first quadrant, ranging from (1, 1) to (5, 5). The range(1, 6) is used to include values from 1 to 5 (inclusive) for both x and y.

After running this code, coordinates will contain the following list:

[(1, 1), (1, 2), (1, 3), (1, 4), (1, 5),  (2, 1), (2, 2), (2, 3), (2, 4), (2, 5),  (3, 1), (3, 2), (3, 3), (3, 4), (3, 5),  (4, 1), (4, 2), (4, 3), (4, 4), (4, 5),  (5, 1), (5, 2), (5, 3), (5, 4), (5, 5)]

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Python Coding challenge - Day 112 | What is the output of the following Python Code?

 

Code :

x = 15

y = 10

result = x if x < y else y

print(result)

Solution and Explanation: 

Let's break down the code step by step:

Variable Initialization:

x = 15

y = 10

Two variables, x and y, are initialized with the values 15 and 10, respectively.

Conditional Expression:

result = x if x < y else y

This line uses a conditional expression. The syntax is a if condition else b, meaning if the condition is true, the value of a is assigned to the variable; otherwise, the value of b is assigned. In this case, it's checking whether x is less than y. If it is true, result will be assigned the value of x, otherwise, it will be assigned the value of y.

Printing the Result:

print(result)

Finally, the code prints the value of result.

Execution:

In this specific example, x (15) is not less than y (10). Therefore, the conditional expression evaluates to y, and the value 10 is assigned to result. The print(result) statement then outputs 10 to the console.

In summary, the code compares the values of x and y and assigns the smaller value to the variable result, which is then printed. In this particular case, the output will be 10 because y is smaller than x.

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50 Algorithms Every Programmer Should Know

 

Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul

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

Key Features

• Familiarize yourself with advanced deep learning architectures
• Explore newer topics, such as handling hidden bias in data and algorithm explainability
• Get to grips with different programming algorithms and choose the right data structures for their optimal implementation

Book Description

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.

You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.

Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.

You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.

Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.

By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

What you will learn

• Design algorithms for solving complex problems
• Become familiar with neural networks and deep learning techniques
• Explore existing data structures and algorithms found in Python libraries
• Implement graph algorithms for fraud detection using network analysis
• Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples
• Create a recommendation engine that suggests relevant movies to subscribers
• Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs

Who this book is for

This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.

Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.

Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Table of Contents

1. Core Algorithms
2. Data Structures
3. Sorting and Searching Algorithms
4. Designing Algorithms
5. Graph Algorithms
6. Unsupervised Machine Learning Algorithms
7. Supervised Learning Algorithms
8. Neural Network Algorithms
9. Natural Language Processing
10. Sequential Models
11. Advanced Machine Learning Models
12. Recommendation Engines
13. Algorithmic Strategies for Data Handling
14. Large-Scale Algorithms
15. Evaluating Algorithmic Solutions
16. Practical Considerations

Hard Copy : 50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography

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How much do you know about Modules and packages in python?

 

a. A function can belong to a module and the module can belong to a

package.

Answer

True

b. A package can contain one or more modules in it.

Answer

True

c. Nested packages are allowed.

Answer

True

d. Contents of sys.path variable cannot be modified.

Answer

False

e. In the statement import a.b.c, c cannot be a function.

Answer

True

f. It is a good idea to use * to import all the functions/classes defined in a

module.

Answer

True

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PostgreSQL for Everybody Specialization

 

What you'll learn

How to use the PostgreSQL database effectively

Explore database design principles

Dive into database architecture and deployment strategies

Compare and contrast SQL and NoSQL database design approaches and acquire skills applicable to data mining and application development

Join Free: PostgreSQL for Everybody Specialization

Specialization - 4 course series

Across these four courses, you’ll learn how to use the PostgreSQL database and explore topics ranging from database design to database architecture and deployment. You’ll also compare and contrast SQL and NoSQL approaches to database design. The skills in this course will be useful to learners doing data mining or application development.

Applied Learning Project

This course series utilizes a custom autograding environment for an authentic set of graded and practice assignments, including: creating and manipulating tables, designing data models, constructing advanced queries, techniques for working with text in databases, including regular expressions, and more.

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Python Data Structures

 

What you'll learn

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

Join Free: Python Data Structures

There are 7 modules in this course

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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Web Applications for Everybody Specialization

 

What you'll learn

Installing your development environment

Developing a database application with PHP and MySQL

Using JavaScript to interact with a PHP web app

Modeling many-to-many relationships 

Join Free: Web Applications for Everybody Specialization

Specialization - 4 course series

This Specialization is an introduction to building web applications for anybody who already has a basic understanding of responsive web design with JavaScript,  HTML, and CSS. Web Applications for Everybody is your introduction to web application development. You will develop web and database applications in PHP, using SQL for database creation, as well as functionality in JavaScript, jQuery, and JSON.

Over the course of this Specialization, you will create several web apps to add to your developer portfolio. This Specialization (and its prerequisites) will prepare you, even if you have little to no experience in programming or technology, for entry level web developer jobs in PHP.

You’ll demonstrate basic concepts, like database design, while working on assignments that require the development of increasing challenging web apps. From installing a text editor to understanding how a web browser interacts with a web server to handling events with JQuery, you’ll gain a complete introductory overview of web application development.

Applied Learning Project

The courses in this specialization feature assignments requiring development of increasingly challenging web sites, to demonstrate basic concepts as they are introduced.  The projects will demonstrate the students skills in HTML, CSS, PHP, SQL, and JavaScript.

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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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Python 3 Programming Specialization

What you'll learn

Learn Python 3 basics, from the basics to more advanced concepts like lists and functions.

Practice and become skilled at solving problems and fixing errors in your code.

Gain the ability to write programs that fetch data from internet APIs and extract useful information.

Join Free: Python 3 Programming Specialization

Specialization - 5 course series

This specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance.

You will have lots of opportunities to practice. You will also learn ways to reason about program execution, so that it is no longer mysterious and you are able to debug programs when they don’t work.

By the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them.  And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.

This specialization is a good next step for you if you have completed 

Python for Everybody but want a more in-depth treatment of Python fundamentals and more practice, so that you can proceed with confidence to specializations like 

Applied Data Science with Python

But it is also appropriate as a first set of courses in Python if you are already familiar with some other programming language, or if you are up for the challenge of diving in head-first.

Applied Learning Project

By the end of the second course, you will create a simple sentiment analyzer that counts the number of positive and negative words in tweets. In the third course, you will mash up two APIs to create a movie recommender. The final course, Python Project: pillow, tesseract, and opencv (Course 5), is an extended project in which you'll perform optical character recognition (OCR) and object detection in images.

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Python Coding challenge - Day 111 | What is the output of the following Python Code?

 

Code : 

complex_num = 4 + 3j

print(abs(complex_num))

Solution and Explanantion:

The above code calculates the absolute value (magnitude) of a complex number and prints the result. In this case, the complex number is 4 + 3j. The absolute value of a complex number is given by the square root of the sum of the squares of its real and imaginary parts.

Let's break it down:

complex_num = 4 + 3j

This line creates a complex number with a real part of 4 and an imaginary part of 3.

print(abs(complex_num))

This line calculates the absolute value of the complex number using the abs function and then prints the result. The output will be:

5.0

So, the absolute value of the complex number 4 + 3j is 5.0.

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Applied Data Science with Python Specialization

 

What you'll learn

Conduct an inferential statistical analysis

Discern whether a data visualization is good or bad

Enhance a data analysis with applied machine learning

Analyze the connectivity of a social network

Join Free: Applied Data Science with Python Specialization

Specialization - 5 course series

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization.  After completing those, courses 4 and 5 can be taken in any order.  All 5 are required to earn a certificate.

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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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IBM DevOps and Software Engineering Professional Certificate

 

What you'll learn

Develop  a DevOps mindset, practice Agile philosophy & Scrum methodology -  essential to succeed in the era of Cloud Native Software Engineering

Create applications using Python  language, using various programming constructs and logic, including functions, REST APIs, and  libraries

Build applications composed of microservices and deploy using containers (e.g. Docker, Kubernetes, and OpenShift) & serverless technologies

Employ tools for automation, continuous integration (CI) and continuous deployment (CD) including Chef, Puppet, GitHub Actions, Tekton and  Travis. 

Join Free:IBM DevOps and Software Engineering Professional Certificate

Professional Certificate - 14 course series

DevOps professionals are in high demand! According to a recent GitLab report,  DevOps skills are expected to grow 122% over the next five years,  making it one of the fastest growing skills in the workforce. 

This certificate will equip you with the key concepts and technical know-how to build your skills and knowledge of DevOps practices, tools and technologies and prepare you for an entry-level role in Software Engineering. 

The courses in this program will help you develop skill sets in a variety of DevOps philosophies and methodologies including Agile Development, Scrum Methodology, Cloud Native Architecture, Behavior and Test-Driven Development, and Zero Downtime Deployments.

You will learn to program with the Python language and Linux shell scripts,  create projects in GitHub, containerize and orchestrate your applications using Docker, Kubernetes & OpenShift,  compose applications with microservices, employ serverless technologies,  perform continuous integration and delivery (CI/CD), develop testcases,  ensure your code is secure, and monitor & troubleshoot your cloud deployments.

Guided by experts at IBM, you will be prepared for success. Labs and projects in this certificate program are designed to equip job-ready hands-on skills that will help you launch a new career in a highly in-demand field. 

This professional certificate is suitable for both - those who have none or some programming experience, as well as those with and without college degrees.

Applied Learning Project

Throughout the courses in this Professional Certificate,  you will develop a portfolio of projects to demonstrate your proficiency using various popular tools and technologies in DevOps and Cloud Native Software Engineering. 

You will: 

Create applications using Python programming language, using different programming constructs and logic, including functions, REST APIs, and various Python libraries.

Develop Linux Shell Scripts using Bash and automate repetitive tasks

Create projects on GitHub and work with Git commands

Build  and deploy applications composed of several microservices and deploy  them to cloud using containerization tools (such as Docker, Kubernetes,  and OpenShift); and serverless technologies

Employ various tools for automation, continuous integration (CI) and  continuous deployment (CD) of software including Chef, Puppet, GitHub  Actions, Tekton and Travis.

Secure and Monitor your applications and cloud deployments using tools like sysdig and Prometheus.

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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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Web Design for Everybody: Basics of Web Development & Coding Specialization

 

What you'll learn

Add interacitivity to web pages with Javascript

Describe the basics of Cascading Style Sheets (CSS3)

Use the Document Object Model (DOM) to modify pages

Apply responsive design to enable page to be viewed by various devices

Specialization - 5 course series

This Specialization covers the basics of how web pages are created – from writing syntactically correct HTML and CSS to adding JavaScript to create an interactive experience. While building your skills in these topics you will create websites that work seamlessly on mobile, tablet, and large screen browsers. During the capstone you will develop a professional-quality web portfolio demonstrating your growth as a web developer and your knowledge of accessible web design. This will include your ability to design and implement a responsive site that utilizes tools to create a site that is accessible to a wide audience, including those with visual, audial, physical, and cognitive impairments.

Join : Web Design for Everybody: Basics of Web Development & Coding Specialization

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