ব্যাচের কার্যক্রম - মেইন লাইভ ক্লাস শুরুর আগেই যারা ইনরোল করেছেন, তারা কি বসে থাকবেন ? না! এই মডিউলে ব্যাসিক বেশ কিছু ভিডিও দেওয়া আছে। শুরু হয়ে যাক তবে শেখার জার্নি! 

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

Mathematical Concepts Covered:

Strings and Text Representation | Mathematical Operations and Functions | Operator Precedence (BODMAS Rule) | Logical and Comparison Operators | Conditional Logic (If Statements) | Iteration and Loop Logic (For, While, Nested Loops) | Control Flow (Break, Continue) | Data Structures (Lists and List Comprehension) | Algorithmic Thinking and Problem Solving

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)

Mathematical Concepts Covered:
Functional Abstraction and Modularity | Parameters, Arguments, and Return Logic | Variable Scope and Data Flow | Error Handling and Logical Debugging | File Input/Output Operations | Data Persistence and Representation | Object-Oriented Thinking (Classes, Objects, Attributes, Methods) | Applied Logic through Mini Project (Computation & Data Management)

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)

Mathmaticals Concepts Covered:
Number Systems | Basic Arithmetic Awareness | Data Representation | Logic & Abstraction | Arithmetic Operations | Order of Operations (BODMAS rule) | Type Conversion & Numeric Data Types | String Concatenation | Error Debugging (Logic Errors)

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

Mathematical Concepts Covered:

Data Organization and Representation (Tuples, Sets, Dictionaries) | Understanding Uniqueness and Immutability | Mapping and Key-Value Relationships | Iteration Logic over Complex Structures | Sequence Pairing (Zip, Enumerate) | Nested Data Handling | Logical Structuring and Abstraction | Problem Solving with Data Patterns

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 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

Mathematical Concepts Covered:
Foundations of Artificial Intelligence and Machine Learning | Data Representation and Abstraction | Introduction to Statistical Thinking | Types of Machine Learning (Supervised, Unsupervised, Reinforcement) | Exploratory Data Analysis (EDA) Concepts | Data Patterns, Distributions, and Correlations | Logical and Analytical Reasoning for Model Understanding

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

Mathematical Concepts Covered:
Version Control Logic and Change Tracking | Workflow Structuring and Project Organization | Logical Sequencing of Code Operations (Init, Add, Commit, Merge) | Systematic Thinking for Code Management | Documentation and Presentation through Markdown | Structured Data Representation in Notebooks | Readability, Clarity, and Code Abstraction | Analytical Organization of Code and Notes for Reproducibility

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

Mathematical Concepts Covered:
Logistic Regression and Sigmoid Function | Probability and Binary Classification | K-Nearest Neighbors Algorithm and Distance Metrics | Model Parameter Tuning (Choosing K) | Decision Trees and Splitting Criteria (Gini, Entropy) | Overfitting and Pruning Techniques | Comparative Analysis of Classification Models | Confusion Matrix and Classification Outcomes (TP, FP, TN, FN) | Precision, Recall, and F1 Score Calculations | Balancing Evaluation Metrics | ROC Curve and AUC Interpretation | Metric Selection Based on Problem Context

Live Class 1: Introduction to K-Means clustering | How K-Means groups similar data points | Choosing number of clusters | What is the Elbow Method | Using Elbow Method to find optimal K | Introduction to PCA (Principal Component Analysis) | Reducing dimensionality with PCA | Interpreting principal components | PCA vs clustering: when to use what

Live Class 2: Hierarchical Clustering & DBSCAN

Project: Clustering Assignment: Iris Dataset

Mathematical Concepts Covered:
K-Means Clustering and Data Grouping | Cluster Count Selection and Optimization | Elbow Method for Determining Optimal Clusters | Dimensionality Reduction with Principal Component Analysis (PCA) | Interpretation of Principal Components | Comparative Analysis of PCA and Clustering Techniques | Hierarchical Clustering Methods | Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

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

Mathematical Concepts Covered:
Vectors and Linear Algebra Foundations | Numerical Computation with NumPy | Data Manipulation and Analysis using Pandas | Understanding Dimensions and Data Structures | Machine Learning Pipeline Concepts | Model Evaluation and Optimization | Underfitting vs Overfitting | Bias-Variance Tradeoff | Statistical Reasoning and Data Preprocessing Best Practices

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

Mathematical Concepts Covered:
Linear Regression and Line Fitting using Least Squares | Understanding Slope, Intercept, and Line Equation | Overfitting and Model Complexity | Regularization Techniques (L1 and L2 Penalties) | Ridge and Lasso Regression Comparison | Statistical Model Evaluation Metrics (MAE, RMSE, R²) | Error Measurement and Interpretation | Model Accuracy and Performance Assessment | Analytical Decision-Making for Metric Selection

Project: House Price Predictor

মডিউল ৬ থেকে মডিউল ১০ পর্যন্ত যা যা আলোচনা হয়েছে, সেটার উপর ভিত্তি করে থাকবে একটা বড়সড় এসাইনমেন্ট ও লাইভ টেস্ট। ঝালিয়ে নিতে পারবেন নিজেকে।