Python Data Science and Machine Learning Tutorials

This repository contains Data Science, Machine Learning, and Deep Learning tutorials that I have personally implemented and commented in Jupyter Notebook for free. It can be used for educational purposes or as a reference guide.

Some of the tutorials (including the algorithmic models) are based on real world data, but some other are based on technical produced data (fake) data for obvious reasons, such as data that include fake users and their fake credit card numbers.

Note: I will keep updating the repository with new problems and I will evolve the projects in the future, so keep visiting these tutorials for new examples, challenges, practicing, and of course to be up-to-date. The evaluation results are not always the best that can be achieved as my purpose is to shows you the basic steps of applying the ML models, as well as I'm letting to you to experiment with the parameters of these models to achieve the best outcomes by yourself.

The topics and libraries related to Data Science, Machine Learning, and Deep learning that are presented in these tutorials are the following:

• Jupyter Notebook
  • Notebook documents are documents produced by the Jupyter Notebook App, which contain both computer code (e.g. python) and rich text elements (paragraph, equations, figures, links, etc…). Notebook documents are both human-readable documents containing the analysis description and the results (figures, tables, etc..) as well as executable documents which can be run to perform data analysis.
• Data Science:
  • Numpy - scientific computing with Python
  • Pandas - data structures and data analysis tools
  • Matplotlib - data visualization
  • Seaborn - data visualization
  • Pandas built-in functions - data visualization
  • Plotly and Cufflinks - interactive data visaluzation
  • Geographical Plotting with Plotly and Cufflinks - interactive data visaluzation
• Machine Learning
  • Scikit-learn Machine Learning library for Python
  • Supervised Learning
    • Linear Regression
    • Logistic Regression
    • K-Nearest Neighbors
    • Desicion Trees and Random Forest
    • Support Vector Machines
  • Unsupervised Learning
    • K-Means Clustering
  • Model Evaluation and Bias-Variance Trade-off
  • Principal Component Analysis
• Applied Machine Learning Projects in various fields:
  • Recommendation Systems
  • Natural Language Processing
  • Big Data and Spark tool
    • Apache Spark is an open-source distributed general-purpose cluster-computing framework. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance
• Deep Learning
  • Tensorflow - The core open source library Deep Learning to help you develop and train ML models.

References

Python and Libraries:

• Python
• Jupyter Notebook
• Data Science and Data Visualization:
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
  • Plotly
  • Cufflinks
• Machine Learning and Deep Learning:
  • Scikit-learn
  • Tensorflow

Books:

• An Introduction to Statistical Learning: with Applications in R by James, G., Witten, D., Hastie, T., Tibshirani, R.
• Hands-On Machine Learning with Scikit-Learn and TensorFlow (Concepts, Tools, and Techniques to Build Intelligent Systems) by Aurélien Géron