Chapter 1: Introduction to Spark No of pages -15 Sub -Topics 1. Spark Evolution 2. Spark Fundamentals 3. Setting up Spark 4. Spark Components Chapter 2: Introduction to Machine Learning No of pages : 10 Sub - Topics: 1. Supervised Machine Learning 2. Unsupervised Machine Learning 3. Semi supervised Machine Learning 4. Reinforcement Learning Chapter 3: Data Processing with PySpark No of pages: 15 Sub - Topics 1. Data Ingestion 2. Data Cleaning 3. Data Transformation Chapter 4: Linear Regression with PySpark No of pages:15 Sub - Topics: 1. Feature Engineering 2. Model Training 3. Model Tuning Chapter 5: Logistic Regression with PySpark No of pages:15 1. Feature Engineering 2. Model Training 3. Model Tuning

Chapter 6: Random Forests with PySpark No of pages:15 1. Feature Engineering 2. Model Training 3. Model Tuning Chapter 7: Clustering with PySpark No of pages:15 1. Hierarchical Clustering 2. K Means 3. Gaussian Mixture

Chapter 8: Recommendation Engine with PySpark No of pages:15 1. Collaborative Filtering 2. Introduction to hybrid recommendation

Chapter 9: NLP with PySpark No of pages:15 1. Sequence Embeddings for Prediction 2. Dimensionality Reduction Chapter 10: Use Case: End to End lifecycle of Machine Learning model with PySpark No of pages:10 1. Business Challenge 2. Machine Learning solution using PySpark