Hands_On_Machine_Learning_with_Scikit_Learn_and_TensorFlow book and code

What is Machine Learning? What problems does it try to solve? What are the main categories and fundamental concepts of Machine Learning systems? • The main steps in a typical Machine Learning project. • Learning by fitting a model to data. • Optimizing a cost function. • Handling, cleaning, and preparing data. • Selecting and engineering features. • Selecting a model and tuning hyperparameters using cross-validation. • The main challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance tradeoff ). • Reducing the dimensionality of the training data to fight the curse of dimensionality. • The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods. What are neural nets? What are they good for? • Building and training neural nets using TensorFlow. • The most important neural net architectures: feedforward neural nets, convolutional nets, recurrent nets, long short-term memory (LSTM) nets, and autoencoders. • Techniques for training deep neural nets. • Scaling neural networks for huge datasets. • Reinforcement learning.