Build Python-based Machine Learning and Deep Learning Models
Focusing on practical applications, this book guides readers in leveraging machine and deep learning models with PySpark to analyze real-time data. It is designed for individuals eager to enhance their skills in exploratory data analysis and tackle various business challenges, providing a comprehensive approach to using PySpark effectively in real-world scenarios.
With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform
Focusing on practical applications, this book guides readers through the process of building and deploying machine learning and deep learning models in real-world scenarios. It offers comprehensive end-to-end examples that illustrate key concepts, making it accessible for practitioners looking to implement these technologies effectively in production environments. The content emphasizes hands-on learning, ensuring readers gain the skills needed to navigate the complexities of machine learning workflows.
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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