Course Content

I. Python

Chapter 1: Getting started with Python programming
Chapter 2: Variables, keywords, and Operators
Chapter 3: Control flow statements
Chapter 4: Numbers and Functions
Chapter 5: Strings, Lists, Tuples, Dictionary, and Sets
Chapter 6: File handling, Exception handling
Chapter 7: Object-oriented programming with Python
Chapter 8: Module and Packages

II. Data Science and Machine learning

Chapter 1: Introduction to Data Science
Chapter 2: Data Analysis using Numpy and Pandas
Chapter 3: Mathematical and Scientific computation using Scipy.
Chapter 4: Data Visualization using Matplotlib, Seaborn
Chapter 5: Machine Learning Introduction
Chapter 6:
a. Supervised Learning
b.Regression – Linear Regression
c.Regression – Multiple Linear Regression
d.Classification – Logistic Regression
e.Classification – k-Nearest Neighbor(KNN)
f.Classification – SVM (Support Vector Machine)
g.Classification –Decision Trees
h.Classification – Ensemble Methods
Chapter 7:
a.Un-supervised Learning
b.Clustering – K-Means

Use case and Exercise