This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs, LSTMs, and auto encoders. Keras-based code samples are included to supplement the theoretical discussion. In addition, this book contains appendices for Keras, Tensor Flow 2, and Pandas.
Features:

• Covers an introduction to programming concepts related to AI, machine learning, and deep learning


• Includes material on Keras, Tensor Flow2 and Pandas