Machine Learning & Algorithms

Chapter 1: Foundations of Machine Learning

• What is Machine Learning & where it’s used

• Types: Supervised, Unsupervised, Reinforcement

• Real-world ML applications (industry case studies)

• ML vs AI vs Data Science
  📌 Outcome: Clear conceptual understanding

Chapter 2: Python for Machine Learning

• Python basics for ML

• NumPy, Pandas, Matplotlib, Seaborn

• Data handling & preprocessing
  📌 Mini Project: Data analysis on real dataset

Chapter 3: Mathematics for ML (Simplified & Practical)

• Linear Algebra essentials

• Probability & Statistics for ML

• Cost functions & optimization intuition
  📌 Focus: Concepts, not heavy math

Chapter 4: Data Preprocessing & Feature Engineering

• Handling missing data

• Encoding categorical data

• Feature scaling & selection

• Train-test split & pipelines
  📌 Mini Project: Clean messy real-world data

Chapter 5: Supervised Learning – Regression Algorithms

• Linear Regression

• Polynomial Regression

• Regularization (L1, L2)

• Model evaluation metrics
  📌 Project: House price prediction

Chapter 6: Supervised Learning – Classification Algorithms

• Logistic Regression

• KNN

• Decision Trees

• Naive Bayes

• Confusion matrix, precision, recall
  📌 Project: Spam or fraud detection

Chapter 7: Ensemble Learning & Advanced Algorithms

• Random Forest

• Gradient Boosting

• XGBoost / LightGBM (intro)

• Bias vs Variance tradeoff
  📌 Project: High-accuracy prediction model

Chapter 8: Unsupervised Learning Algorithms

• K-Means Clustering

• Hierarchical Clustering

• DBSCAN

• Principal Component Analysis (PCA)
  📌 Project: Customer segmentation

Chapter 9: Machine Learning Model Optimization

• Hyperparameter tuning

• Cross-validation

• Overfitting & underfitting

• Model deployment readiness
  📌 Outcome: Production-ready mindset

Chapter 10: Introduction to Deep Learning

• Neural network fundamentals

• Perceptron & activation functions

• TensorFlow / PyTorch basics

• Simple ANN models
  📌 Project: Handwritten digit recognition

Chapter 11: Real-World ML Projects & Case Studies

• End-to-end ML pipeline

• Business problem → ML solution

• ML for startups & enterprises

• Resume-ready capstone project
  📌 Outcome: Portfolio + confidence

**Chapter 12: Paid Internship Program (Internovate Exclusive)

• Selection assessment & interview

• Paid ML internship with real tasks

• Live industry project work

• Mentor feedback & evaluation

• Internship certificate + experience letter
  📌 Outcome: Real experience + income

Rs. 3999 Rs.1999

Duration: 3 months