AI & ML Pro Course

Comprehensive Course Timetable for Python, Statistics, Probability, EDA, and Artificial Intelligence

Overview

Total Duration: 12 weeks (3 months) Total Hours: 120 hours

Days per Week: 5 days (Monday to Friday) *(can be changed to Saturday and Sunday) Hours per Day: 2 hours

Week 1-2: Python Basics (20 hours)

Week 1:

Day 1 (2 hours): Introduction to Python

Topics:

Python installation, IDE setup

Basic syntax, variables, data types

Day 2 (2 hours): Control Structures

Topics:

Conditional statements (if-else)

Loops (for, while)

 Day 3 (2 hours): Functions and Modules

 Topics:

Defining functions

Importing modules, standard library overview

 Day 4 (2 hours): Data Structures

Topics:

Lists, tuples, dictionaries, sets

Day 5 (2 hours): File Handling
Topics:

Reading and writing files

Handling exception

Week 2:

Day 1-2 (4 hours): Hands-on Practice

Activity:

Practical exercises and mini-projects

Day 3-4 (4 hours): Object-Oriented Programming (OOP)

Topics:

Classes, objects, inheritance, polymorphism

Day 5 (2 hours): Review and Q&A

Activity:

Addressing queries and reinforcing concepts

Week 3-4: Python Advanced (20 hours)

Week 3:

Day 1-2 (4 hours): Working with Libraries

Topics:

Introduction to NumPy and pandas

Day 3-4 (4 hours): Advanced Data Structures

Topics:

Stacks, queues, linked lists

Day 5 (2 hours): Error Handling and Debugging

Topics:

Exception handling, debugging techniques

 
Week 4:

Day 1-2 (4 hours): Hands-on Practice

Activity:

Practical exercises and mini-projects

Day 3-4 (4 hours): Review and Q&A

Activity:

Addressing queries and reinforcing concepts

Day 5 (2 hours): Advanced Topics

Topics:

Decorators, generators

Week 5-6: Statistics and Probability (20 hours)

Week 5:

Day 1-2 (4 hours): Descriptive Statistics

Topics:

Mean, median, mode

Variance, standard deviation

Day 3-4 (4 hours): Data Distributions

Topics:

Normal distribution

Skewness, kurtosis

Day 5 (2 hours): Probability Basics

Topics:

Probability theory

Conditional probability, Bayes' theorem

 Week 6:

Day 1-2 (4 hours): Inferential Statistics

Topics:

Hypothesis testing

Confidence intervals

Day 3-4 (4 hours): Hands-on Practice

 Activity:

Exercises and real-world data analysis

 Day 5 (2 hours): Review and Q&A

 Activity:

Addressing queries and reinforcing concepts

 Topics:

Feature scaling, encoding categorical variables

 Day 5 (2 hours): Supervised Learning – Regression

Topics:

Linear regression, evaluation metrics

Week 10:

Day 1-2 (4 hours): Supervised Learning – Classification

Topics:

Logistic regression, evaluation metrics

 Day 3-4 (4 hours): Hands-on Practice

 Activity:

Building and evaluating regression and classification models

 Day 5 (2 hours): Review and Q&A

 Activity:

Addressing queries and reinforcing concepts

Week 11-12: Unsupervised Learning, Advanced Topics, and
Review (20hr) Week 11:

Day 1-2 (4 hours): Clustering

Topics:

K-means, hierarchical clustering

Day 3-4 (4 hours): Dimensionality Reduction

Topics:

PCA, t-SNE

 Day 5 (2 hours): Ensemble Learning

Topics:

Bagging, boosting, stacking

. Basic Syntax

• Python is designed to be easy to read and write. It uses indentation (whitespace) to define the structure of the code, which makes the code more organized and readable.

• Indentation: Python uses indentation to define blocks of code. Unlike other programming languages that use curly braces {}, Python uses indentation levels to mark code blocks. Consistency in indentation is important—typically, 4 spaces are used.

  python

  if 10 > 5: print("10 is greater than 5")

  In this example, the code inside the if block is indented to show it's part of the conditional statement.

• Comments: Comments in Python are added using the # symbol. Comments are ignored by the Python interpreter and are used to explain the code.

  python

  # This is a comment print("Hello, World!") # This prints a message

2. Variables

• Variables are used to store data in Python. You can assign a value to a variable using the = operator. Python is dynamically typed, meaning you don't need to declare the type of a variable; the interpreter infers it from the value assigned.

  python

  x = 10 # Integer y = 3.14 # Float name = "Alice" # String is_active = True # Boolean

• Variable Naming Rules:

  • Variable names must start with a letter or an underscore (_).
  • They cannot start with a number.
  • Variable names can only contain letters, numbers, and underscores (e.g., my_var, var1).
  • Python is case-sensitive, so myVar and myvar are considered different variables.

• Examples:

  python

  age = 25 height = 5.9 name = "John" is_student = False

3. Data Types

Python supports various data types that allow you to store different kinds of data. Here are the most commonly used data types:

• Integer (int): Whole numbers without a decimal point.

  python

  num1 = 10 num2 = -5

• Float (float): Numbers with a decimal point.

  python

  pi = 3.14 temperature = 36.6

• String (str): A sequence of characters enclosed in single (') or double (") quotes.

  python

  greeting = "Hello, World!"

  You can also use triple quotes (''' or """) for multi-line strings:

  python

  multiline_text = """This is a multi-line string"""

• Boolean (bool): Represents True or False values.

  python

  is_raining = True has_passed = False

• List: A collection of items that can be of different data types, enclosed in square brackets []. Lists are mutable, meaning their elements can be changed.

  python

  fruits = ["apple", "banana", "cherry"]

• Tuple: Similar to a list, but tuples are immutable (their elements cannot be changed). They are enclosed in parentheses ().

  python

  coordinates = (10, 20)

• Dictionary (dict): A collection of key-value pairs, where each key maps to a value. Dictionaries are enclosed in curly braces {}.

  python

  person = {"name": "Alice", "age": 30}

• Example Code:

  python

  age = 25 # Integer height = 5.9 # Float name = "John" # String is_student = True # Boolean fruits = ["apple", "banana", "cherry"] # List coordinates = (10, 20) # Tuple person = {"name": "Alice", "age": 30} # Dictionary

Conclusion

Understanding Python's basic syntax, how to work with variables, and the different data types is fundamental to writing effective Python code. By mastering these concepts, you'll have a solid foundation to build more complex programs and solve a variety of problems in Python.