Machine Learning Algorithms helps you harness the real power of machine learning algorithms to implement smarter ways of meeting today’s overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem-including NumPy and Keras, to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning course teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this course, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
In Machine Learning you will learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you will start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You will then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you are done working through these fun and informative projects, you will have a comprehensive machine learning skill set you can apply to practical on-the-job problems.