Python Training

Introduction to PYTHON PROGRAMMING for Data Science, Machine Learning

Section 1: Introduction to Python

1. Setting up environment

2. Variables,

○ Using Numbers,

○ Using Strings,

○ Understanding standard types of variables,

○ Boolean values,

○ Concepts of lists, tuples, and dictionaries.

3. Syntax, operators and operator precedence

4. Making Decisions,

○ Control Structures,

○ Loops

5. Review of week-1 Concepts

6. Python Project - 1

Section 2: I/O Requests, Functions, and Packages

1. Input/output

a. Reading user input

b. Reading input from File, STDIN

c. Writing output to File, STDOUT and STDERR

d. Concepts of File Objects

2. Functions

a. Defining Functions

b. Passing Parameters

c. Naming the arguments

d. Concept of return values

3. Packages

a. Using standard library modules

b. Using third-party modules

4. Python Project - 2

Section 3: Introduction to Data Analysis with python

1. Data wrangling with Pandas, Numpy

○ DataFrames and operations

○ Import and export of data from Pandas

2. Data visualization with Matplotlib, Seaborn

○ Scatter plots, line plots, and other popular formats

○ Plotting exercises

3. Data Science Project - 1 and 2

Section 4: Introduction to Machine Learning with python

1. Unsupervised machine learning with python

○ K-means clustering

○ Hierarchical clustering

2. Supervised machine learning with python

○ Logistic regression

○ Support vector machine

3. Machine Learning Project 1 & 2