Chapter 1: Introduction to Machine Learning

• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• ML workflow and model evaluation

Chapter 2: Supervised Learning

• Regression: Linear, Logistic
• Classification: Decision Trees, Random Forest, SVM
• Model evaluation: Confusion matrix, ROC-AUC

Chapter 3: Unsupervised Learning

• Clustering: K-means, Hierarchical
• Dimensionality Reduction: PCA, LDA
• Anomaly detection

Chapter 1: Deep Learning

• Introduction to Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

Chapter 2: Natural Language Processing (NLP)

• Text preprocessing and vectorization
• Sentiment analysis
• Sequence models and transformers

Chapter 3: Model Deployment

• Model saving and loading
• Introduction to Flask and Django
• Deploying models using cloud platforms (AWS, GCP, Azure)

Chapter 1: Capstone Project

• Project planning and execution
• Data collection and preprocessing
• Model building and evaluation
• Presentation and reporting

Chapter 2: Job Readiness

• Resume building and LinkedIn optimization
• Interview preparation: Technical and HR
• Mock interviews and feedback

Chapter 3: Special Topics

• Ethical considerations in Data Science
• Real-world case studies
• Continuous learning and staying updated