My Posts in this series will follow below said topics.

1. Introduction to AI and ML
   • What is AI?
   • What is Machine Learning?
   • Types of Machine Learning
     • Supervised Learning
     • Unsupervised Learning
     • Reinforcement Learning
   • Key Terminologies
2. Python for Machine Learning
   • Introduction to Python
   • Python Libraries for ML: NumPy, Pandas, Matplotlib, Scikit-Learn
3. Data Preprocessing
   • Data Cleaning
   • Data Normalization and Standardization
   • Handling Missing Data
   • Feature Engineering
4. Supervised Learning
   • Linear Regression
   • Logistic Regression
   • Decision Trees
   • Random Forests
   • Support Vector Machines (SVM)
   • Neural Networks
5. Unsupervised Learning
   • K-Means Clustering
   • Hierarchical Clustering
   • Principal Component Analysis (PCA)
   • Anomaly Detection
6. Model Evaluation and Selection
   • Train-Test Split
   • Cross-Validation
   • Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
   • Model Selection and Hyperparameter Tuning
7. Advanced Topics
   • Deep Learning
   • Convolutional Neural Networks (CNNs)
   • Recurrent Neural Networks (RNNs)
   • Natural Language Processing (NLP)
   • Generative Adversarial Networks (GANs)
8. Practical Projects
   • Project 1: Predicting House Prices
   • Project 2: Classifying Handwritten Digits (MNIST)
   • Project 3: Sentiment Analysis on Movie Reviews
   • Project 4: Image Classification with CNNs
9. Final Project
   • End-to-End ML Project