Introduction to Machine Learning

• What is Machine Learning and Why It Matters

• Applications of ML in Real Life

• ML Workflow and Data Pipeline

• Types of Learning: Supervised, Unsupervised & Reinforcement

 Python for Machine Learning

• Python Basics: Data Types, Functions, Loops

• Working with NumPy and Pandas

• Data Cleaning and Preprocessing

• Exploratory Data Analysis (EDA)

 Mathematics & Statistics for ML

• Linear Algebra Essentials

• Probability and Statistics

• Hypothesis Testing

• Correlation, Regression & Cost Functions

  Supervised Learning Algorithms

• Linear & Logistic Regression

• Decision Trees and Random Forests

• K-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

  Unsupervised Learning Algorithms

• Clustering: K-Means, Hierarchical Clustering

• Dimensionality Reduction: PCA

• Association Rule Learning

  Model Evaluation & Optimization

• Train-Test Split, Cross-Validation

• Overfitting & Underfitting

• Confusion Matrix, Accuracy, Precision, Recall

• Hyperparameter Tuning

  Neural Networks & Deep Learning (Intro)

• Basics of Neural Networks

• Activation Functions

• Introduction to TensorFlow & Keras

• Building Simple Deep Learning Models

   Project & Career Guidance

• Real-Time Machine Learning Project

• Model Deployment Overview

• Resume Building and Mock Interviews

• Placement Assistance & Career Support