Fantastic Python: Data Science & Machine Learning

Description

This course in the Fantastic Python Series is a complete guide on Python Coding & Machine Learning for beginners and intermediate level coders. You will learn not only Python, but also how to conduct data analysis, data visualization and Machine Learning (ML) using pandas,  numpy, scikit-learn, statsmodels, seaborn and more.

Practical Examples for ML includes: (1) hand-written digits classification; (2) facial recognition; (3) heart-disease prediction; (4) penguins classification; (5) World Happiness Index; and many more.

In particular, this course consists of 3 major parts ("mini-courses"):

1. Learn Python Coding

   • All essential data types and common operations

   • Comprehensive string manipulations

   • Control flows

   • Lists, Tuples and Sets

   • Dictionaries

   • Object-Oriented Programming

   • Inheritance

   • Datetime

   • Modules and Packages

   • Exceptions Handling, etc

     
     

2. Learn Data Analytics and Visualization with pandas and Seaborn

   • Series and Data Frames

   • Indexing, filtering, sorting, counting, etc

   • Merge/Joins

   • Aggregation

   • Line plots

   • Bar plots

   • Scatter plots

   • Histogram, etc

     
     

3. Learn Machine Learning with Scikit-Learn

   • Linear Regressions

   • Logistic Regressions

   • Linear Discriminant Analysis

   • Principal Component Analysis

   • K-Means

   • K-Nearest Neighbors

   • Support Vector Machines

   • Neural Networks

   • Decision Trees

   • Random Forests

   • Hyper-parameters Tuning

The course is one of the most comprehensive and detailed course ever on the Pandas package. It highlights the complexity of data wrangling which occupies about 80% of data scientists' time, and gives you a solid foundation to meet the challenging requirements of handling messy real-world data.

The focus for Machine Learning (ML) is on practical applications and gaining an intuitive understanding of the algorithms rather than diving into the theories and mathematics.

By the end of this course, you will not only become a competent Python programmer, but also a budding data scientist ready to take on real-world challenges.

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