Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated versionโs approach is based on the โsix degrees of separationโ theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.
Youโll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. Youโll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.
Finally, youโll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
What Youโll Learn
Understand machine learning development and frameworks
Assess model diagnosis and tuning in machine learning
Examine text mining, natuarl language processing (NLP), and recommender systems
Review reinforcement learning and CNN