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Python: Real World Machine Learning

Code repository for Python: Real World Machine Learning

##What You Will Learn:

  • Use predictive modeling and apply it to real-world problems
  • Understand how to perform market segmentation using unsupervised learning
  • Apply your new found skills to solve real problems, through clearly-explained code for every technique and test
  • Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
  • Increase predictive accuracy with deep learning and scalable data-handling techniques
  • Work with modern state-of-the-art large-scale machine learning techniques

Software and Hardware (Module 1):

Chapter number Software required (with version) Download links to the software Hardware specifications OS required
All Scikit-learn 0.17.0, Numpy 1.11, Matplotlib 1.5.1, Scipy 0.17.0 http://scikit-learn.org/stable/install.html, http://www.scipy.org/scipylib/download.html, http://matplotlib.org/downloads.html, http://www.scipy.org/install.html 4 GB of RAM and 16GB of disk Linux, Mac OS X, Windows
6 NLTK 3.0, Gensim 0.12.4 http://www.nltk.org/install.html, https://radimrehurek.com/gensim/install.html 4 GB of RAM and 16GB of disk Linux, Mac OS X, Windows
7, 8 hmmlearn 0.2.1, python_speech_features http://hmmlearn.readthedocs.org/en/latest/, http://pythonspeechfeatures.readthedocs.org/en/latest/ 4 GB of RAM and 16GB of disk Linux, Mac OS X, Windows
8 Pandas 0.18.0, Pystruct 0.2.4 http://pandas.pydata.org/getpandas.html, https://pystruct.github.io/installation.html 4 GB of RAM and 16GB of disk Linux, Mac OS X, Windows
9, 10 OpenCV 3.0.0 http://opencv.org/downloads.html 4 GB of RAM and 16GB of disk Linux, Mac OS X, Windows
11 NeuroLab 0.3.5 https://pythonhosted.org/neurolab/install.html 4 GB of RAM and 16GB of disk Linux, Mac OS X, Windows

Software and Hardware (Module 2):

Chapter number Software required (with version)
1 Python 3 (3.4 recommended), sklearn (numpy, scipy), matplotlib
2-4 Theano
5 Semisup-learn
6 Natural Language Toolkit (NLTK), BeautifulSoup
7 Twitter API account
8 XGBoost
9 Lasagne, TensorFlow

###Note Modules 1, 2 and 3 have code arranged by chapter (for the chapters that have code). Click here if you have any feedback or suggestions.

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