Day 1: Introduction to Python for Machine Learning (7 Hours)

• Overview of Machine Learning & AI

• Python Basics: Variables, Data Types, Control
    y Structures

• Development Environment Setup: Jupyter Notebook & PyCharm

• Core Python Programming: Loops, Functions, and Modules

• NumPy Fundamentals: Arrays, Indexing, Slicing, Basic Operations

• Pandas Essentials: Series, DataFrames, Import/Export (CSV, Excel, JSON)

• Interactive Session: MCQs and Hands-on Practice

Day 2: Understanding Data & Feature Engineering (7 Hours)

• Data Handling with Pandas: Missing Values, Filtering, Indexing

• Data Cleaning & Transformation: Managing Imbalanced Datasets, Duplicates

• Feature Engineering Techniques: Normalization, Min-Max Scaling, Encoding

• Data Integration: Merging, Joining, Concatenating DataFrames

• Interactive Session: MCQs and Hands-on Practice

Day 3 & 4: Data Visualization & Supervised Machine Learning (7 Hours Each)

• Introduction to Data Visualization with Matplotlib

  • Line, Bar, and Scatter Plots

  • Plot Customization (Titles, Labels, Legends)

• Advanced Visualization with MATLAB

• Regression Techniques:

  • Simple Linear Regression

  • Multiple Linear Regression

• Classification Algorithms:

  • Logistic Regression

  • k-Nearest Neighbours (KNN)

• Interactive Session: MCQs and Hands-on Practice

Day 5 & 6: Model Analysis, Optimization & Project Work (7 Hours Each)

• Model Evaluation Techniques: Confusion Matrix, Precision, Recall, F1-score

• Decision Tree Classifier

• Support Vector Machines (SVM) & Naïve Bayes

• Unsupervised Learning Methods:

  • K-Means Clustering

  • Hierarchical Clustering

• Introduction to Deep Learning:

  • Basics of Neural Networks

  • Neural Network Implementation using Python

• Capstone Project: Real-world Case Study & Model Development