Python - Data Science Essentials (Part 2)

Date - 27th October, 2020 - 06th November, 2020

This repository contains my learning path of Python for Data Science (Part 2).

Here, I haven't included the files of Chapter 1 and 2, since there was no practical implementation works needed and was more of an introduction. However, I am listing out the chapters.

Chapter 1 - Introduction to Data Science

• Defining data science
• Why use Python for data science?
• Where does AI fit in?

Chapter 2 - Introduction to Machine Learning

• Machine learning 101
• Grouping Machine learing algorithms

Chapter 3 - Regression Models

• Linear regression
• Multiple linear regression
• Logistic regression : Concepts
• Logistic regression : Data preparation
• Logistic regression : Treat missing values
• Logistic regression : Re-encode variables
• Logistic regression : Validating dataset
• Logistic regression : Model deployment
• Logistic regression : Model evaluation
• Logistic regression : Test prediction

Chapter 4 - Clustering Models

• K-means method
• Hierarchical methods
• DBSCAN for outlier detection

Chapter 5 - Dimension Reduction Methods

• Explanatory factor analysis
• Principal componenet analysis (PCA)

Chapter 6 - Other Popular Machine Learning Methods

• Association rules models with Apriori
• Neural networks with a perceptron
• Instance-based learning with KNN
• Decision tree models with CART
• Bayesian models with Naive Bayes
• Ensemble models with random forests