Chapter 01 – Fundamentals of Artificial Intelligence
Chapter 02 – Intelligent Agents and Environments
Chapter 03 – Intelligent Systems
Chapter 04 – Artificial Intelligence Search Algorithms
Chapter 05 – Fuzzy Logic Systems in Artificial Intelligence
Chapter 06 – Expert Systems
Chapter 07 – Learning
Chapter 08 – Artificial Intelligence Natural Language Processing
Chapter 09 – Perception
Chapter 10 – Robotics
Chapter 11 – Conclusion

Chapter 01 – Fundamentals of Machine Learning
Chapter 02 – Types of Machine Learning Algorithms
Chapter 03 – Planning for Machine Learning
Chapter 04 – Mathematical and Statistical Concepts – A Quick Recap
Chapter 05 – Regression
Chapter 06 – Tree Modelling
Chapter 07 – Classification
Chapter 08 – Clustering
Chapter 09 – Reinforcement Learning
Chapter 10 – Machine Learning with R
Chapter 11 – Deep Learning
Chapter 12 – Going Deep Using TensorFlow
Chapter 13 – Artificial Neural Networks (ANN)
Chapter 14 – Convolutional Neural Networks (CNN)
Chapter 15 – Recurrent Neural Networks (RNN)
Chapter 16 – Distributed TensorFlow
Chapter 17 – Deep Learning Applications

Chapter 01 – Introduction to Interest of Things (IoT)
Chapter 02 – IoT Architecture
Chapter 03 – IoT Communication Protocols and Networking
Chapter 04 – Software and Hardware Platforms
Chapter 05 – C Programming Concepts
Chapter 06 – Arduino and Raspberry Pi Platform Overview

Chapter 01 – Introduction to Big Data
Chapter 02 – Working with Hadoop and its Components & Concepts.
Chapter 03 – Scripting with Hive & HBase
Chapter 04 – Programming using MapReduce for Big Data – 1
Chapter 05 – Programming using MapReduce for Big Data – 2
Chapter 06 – Distributed resources synchronization using Zookeeper.
Chapter 07 – Data loading using Sqoop.
Chapter 08 – Handling large log files using Flumes.
Chapter 09 – Handling workflows using Oozie.
Chapter 10 – Understanding popular Big Data Platforms