【机器学习】 Python: Real World Machine Learning (2016)

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Python: Real World Machine Learning

Prateek Joshi et al.



Learn to solve challenging data science problems by building powerful machine learning models using Python

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you’ll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

Table of Contents

1: THE REALM OF SUPERVISED LEARNING
2: CONSTRUCTING A CLASSIFIER
3: PREDICTIVE MODELING
4: CLUSTERING WITH UNSUPERVISED LEARNING
5: BUILDING RECOMMENDATION ENGINES
6: ANALYZING TEXT DATA
7: SPEECH RECOGNITION
8: DISSECTING TIME SERIES AND SEQUENTIAL DATA
9: IMAGE CONTENT ANALYSIS
10: BIOMETRIC FACE RECOGNITION
11: DEEP NEURAL NETWORKS
12: VISUALIZING DATA
13: UNSUPERVISED MACHINE LEARNING
14: DEEP BELIEF NETWORKS
15: STACKED DENOISING AUTOENCODERS
16: CONVOLUTIONAL NEURAL NETWORKS
17: SEMI-SUPERVISED LEARNING
18: TEXT FEATURE ENGINEERING
19: FEATURE ENGINEERING PART II
20: ENSEMBLE METHODS
21: ADDITIONAL PYTHON MACHINE LEARNING TOOLS
22: FIRST STEPS TO SCALABILITY
23: SCALABLE LEARNING IN SCIKIT-LEARN
24: FAST SVM IMPLEMENTATIONS
25: NEURAL NETWORKS AND DEEP LEARNING
26: DEEP LEARNING WITH TENSORFLOW
27: CLASSIFICATION AND REGRESSION TREES AT SCALE
28: UNSUPERVISED LEARNING AT SCALE
29: DISTRIBUTED ENVIRONMENTS – HADOOP AND SPARK
30: PRACTICAL MACHINE LEARNING WITH SPARK

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