Machine Learning using Python

SYLLABUS

SESSION 1

Topics to be covered:                                                                  

• Introductory Remark about Python
• A Brief History of Python
• How Python is different from other languages
• Python Version
• Installing Python
• IDLE
• Getting Help
• How To execute Python program
• Writing your first program

SESSION 2

Topics to be covered:      

• Python coding Introduction
• Python keywords and Identifiers
• Python statements
• Comments in python
• Getting user input
• Variables
• Data types
• Numbers
• Strings
• Lists ,tuples & dictionary

SESSION 3

Topics to be covered:      

• Control flow and syntax
• The if statement
• Python operators
• The while Loop
• Break and continue
• The for Loop
• Pass statement
• Packages

SESSION 4

Topics to be covered:      

• Scientific Computing with NumPy.
• N-Dimensional Array Object
• Array Slicing Methods
• Array reshapeing methods
• Numerical routines in NumPy

SESSION 5

Topics to be covered:      

• Introduction To Matplotlib
• Python 2D plotting
• Plotting with default settings
• Customizing matplotlib Graphics with colors and line width
• Generate plots, histograms, power spectra,
• Generate bar charts, scatterplots
• Introduction To Pandas
• Pandas data structures and data analysis

 

SESSION 6

Topics to be covered:      

• Introduction To Statistics
• Implementation of Statistics in Python
• Python Math package to mathematical functions
• Concepts of Calculus and Linear Algebra
• Vector and Matrices in Python
• Operation on Matrices using Python

SESSION 7

Topics to be covered:      

• Introduction To Machine Learning
• History and Evolution
• Artificial Intelligence Evolution
• Find out where Machine Learning is applied in Technology and Science.
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Learning

SESSION 8

Topics to be covered:      

• Regression
• Understand how continuous supervised learning is different from discrete learning.
• Code a Linear Regression in Python with scikit-learn.
• Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
• k-Nearest Neighbor
• Linear models
• Naive Bayes Classifiers
• Decision trees
• Support Vector Machines

SESSION 9

Topics to be covered:

• Prepare Your Data For Machine Learning
• Need For Data Pre-processing
• Data Transforms
• Rescale Data
• Standardize Data
• Normalize Data
• Binarize Data

SESSION 10

Topics to be covered:

• Supervised Machine Learning Algorithms
• Prepare Your Data For Machine Learning
• k-Nearest Neighbor
• Linear models
• Naive Bayes Classifiers
• Decision trees
• Support Vector Machines
• Unsupervised Learning and Preprocessing
• Challenges in unsupervised learning
• Preprocessing and Scaling
• Applying data transformations
• Scaling training and test data the same way