This course starts with a Python crash course and then shows you how to get set up on Microsoft Windows-based PCs, Linux desktops, and Macs. After setup, we will cover the machine learning, AI, and data mining techniques real employers are looking for, including deep learning / neural networks with TensorFlow and Keras; generative models with variational auto-encoders and generative adversarial networks; data visualization in Python with Matplotlib and Seaborn; transfer learning, sentiment analysis, image recognition, and classification; regression analysis, K-Means Clustering, Principal Component Analysis, train/test and cross-validation, Bayesian methods, decision trees and random forests.

We will also cover multiple regression, multi-level models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbor, bias/variance tradeoff, ensemble learning, term frequency / inverse document frequency, experimental design, and A/B tests, feature engineering, hyperparameter tuning, and much more! There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster.

By the end of this course, you will be able to become a professional data scientist.

All the resources for this course are available at https://github.com/packtpublishing/data-science-and-machine-learning-with-python---hands-on-

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Spis treści

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