Goal: Set up the coding environment and understand the basics of Python programming.

Live Class 1 : Environment Setup & Introduction
Installing Python | Installing VS Code / IDE | Setting Up Interpreter | Writing First Python Code | Understanding Print Function | Comments | Variables & Data Types

Live Class 2 : Input & Basic Operations
Taking User Input | Type Conversion | Arithmetic Operators | String Concatenation | Basics of Debugging (Errors & Fixing)

Goal: Manage and transform data effectively using Python’s built-in collections.

Live Class 1: Built in Data Structures (Part 2)
Tuples | Tuple Methods | Sets | Set Methods | Dictionaries | Dictionary Methods

Live Class 2: Advanced Looping with Data Structures
Iterating over Lists, Tuples, Sets, Dictionaries | Zip and Enumerate | Nested Dictionary/Lists | Mini Practice Problems

মডিউল ১ থেকে মডিউল ৪ পর্যন্ত যা যা আলোচনা হয়েছে, সেটার উপর ভিত্তি করে থাকবে একটা বড়সড় এসাইনমেন্ট ও লাইভ টেস্ট। ঝালিয়ে নিতে পারবেন নিজেকে।

Objective: Understand the Full ML Pipeline and Get ready to deep dive into the world of ML

Live Class 1: Master Pandas, Numpy , Vector and its Importance in ML

Live Class 2: Machine Learning Pipeline , Best Practices | Underfit , Overfit , Bais , Variance

Live Class 1: Introduction to linear regression | Fitting a line to data using least squares | Understanding slope and intercept | What is overfitting in regression | Introduction to regularization | Ridge regression and L2 penalty | Lasso regression and L1 penalty | Comparing ridge and lasso | When to use ridge vs lasso

Live Class 2: Introduction to model evaluation | What is Mean Absolute Error (MAE) | Interpreting MAE in regression models | What is Root Mean Squared Error (RMSE) | Difference between MAE and RMSE | What is R-squared (R²) | Interpreting R² as model accuracy | Choosing the right metric for evaluation

Project: House Price Predictor

Goal: Work with strings, numbers, and write simple decision-making programs.

Live Class 1 : Strings and Conditional Statement
Strings | Formatted Strings | String Methods | Numbers and Math Operations | Operator Precedence | Math Functions | If Statements | Logical Operators | Comparison Operators | While Loops

Live Class 2: Loops and Built in Data Structures (Part 1)
For Loops | Nested Loops | Break and Continue Statements | Lists | List Methods | List Comprehension

Goal: Write reusable code, work with files, and learn OOP basics to prepare for real projects.

Live Class 1: Functions and Modules
Defining Functions | Parameters & Arguments | Return Values | Default & Keyword Arguments | Variable Scope | Try & Except | Using Built-in Modules

Live Class 2: File Handling and OOP Basics
Reading & Writing Files | File Modes | With Statement | Introduction to OOP | Classes & Objects | Attributes & Methods | Mini Project (Student Grade Calculator or Contact Book)

Objective: Understand the full course and get a breif knowledge about ML

Live Class 1: AI Engineer Full Roadmap | Intro to ML

Live Class 2: Types of Machine Learning | Intro to EDA

Objective: Introduce Git, Jupyter, Colab, and environment setup.

Live Class 1: What is Git and why use it | Basic Git commands: init, add, commit | Understanding version control | What is GitHub and how it works | Creating and managing repositories on GitHub | Cloning and pushing code | Branching and merging basics | Typical project folder structure | Best practices for organizing codebase

Live Class 2: What is Jupyter Notebook | Running and managing code cells | Introduction to Google Colab | Uploading and sharing notebooks | Writing clean and readable code | Using comments and proper indentation | Writing Markdown in notebooks | Formatting text, lists, and code blocks with Markdown | Creating headings and notes for better understanding

Live Class 1: Introduction to logistic regression | Understanding sigmoid function and probabilities | Binary classification with logistic regression | Introduction to K-Nearest Neighbors (KNN) | How KNN makes predictions based on distance | Choosing the right value of K | Introduction to decision trees | Splitting criteria: Gini and Entropy | Overfitting and pruning in decision trees | Comparing logistic, KNN, and decision trees

Live Class 2: What is a confusion matrix | Understanding TP, FP, TN, FN | Calculating precision and recall | What is F1 score and why it matters | Balancing precision and recall | Introduction to ROC curve | Interpreting ROC-AUC score | Choosing metrics based on problem type

Project: Titanic Dataset