# Python-Real-World-Machine-Learning

**Repository Path**: bit2atom/Python-Real-World-Machine-Learning

## Basic Information

- **Project Name**: Python-Real-World-Machine-Learning
- **Description**: Code files added
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-29
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://docs.google.com/forms/d/e/1FAIpQLSe5qwunkGf6PUvzPirPDtuy1Du5Rlzew23UBp2S-P3wB-GcwQ/viewform) if you have any feedback or suggestions.