Machine Learning using Python

Objectives

Python is used for Machine Learning as well: predicting stocks or spam detection, it is also used for Browser Automation. Actually you use selenium with Python to automate your browser to do social media posts.

Python used in Artificial Intelligence: With AI, you can actually make a machine mimic the human brain which has the power to think, analyze and make decisions.

Pre-requisites

C Language

C++ / Java (Optional)

Foundation of Python (Optional)

Course Content

Module 1: Software Requirement Specification

  • Installing Anaconda Navigator IDE (Python 3.6)

  • Setting up class path of Python

  • Text Editor: Visual Studio Code

  • Module 2 : Basic programs handling Data type

  • Running python

  • Python identifier, keywords, comments etc

  • Assigning values to variable

  • different data types in python

  • python numbers, strings

  • Module 3: Operators and Decision making in python

  • operators in python

  • decision making in python

  • if elif

  • break and continue

  • loops

  • while loop with else

  • for loop

  • Module 4 : Functions in python-

  • defining a function with ‘def’ keyword

  • calling a function in python

  • pass by value and pass by reference

  • local vs global variable

  • modules and packages in python

  • default argument, keyword argument and arbitrary argument

  • programs on function

Module 5: Data Structure in python

  • lists and its different functions

  • list comprehension

  • dictionary and tuples

  • Set and empty set

  • Pop and push on set

  • Using list as stack

Module 6: Jupyter Overview

  • Implementing Jupyter Notebook

  • Web Application embed Visualization

Module 7: Machine learning packages in python

  • Numpy package

  • Panda package

  • Matplotlib package

  • Sklearn package

Module 8: Numerical and Computation using packages

  • Creating Numpy array and manipulation

  • Indexing and slicing of Numpy array

  • Numpy operations

Module 9: Python for Data Analysis – panda

  • Series, Dataframes, Groupby, Merging Joining

  • programs using python data frames

  • Data Manipulation with Panda

Module 10: Python for Data visualization – Matplotlib

  • Data visualization with Matplotlib

  • Scatter Plot, Straight Line Plot

  • Plotting of images

Module 11: Introduction to Machine Learning using UCI Machine Learning Repository 

  • What are Features

  • What are Labels

  • Pre-processing of Data (mean max normalization)

  • Overfitting and Underfitting of Model

Module 12: Regression problem 

Linear regression

  • Logistic regression

  • Polynomial regression

Module 13: Classification Problems

  • KNN

  • Naïve Bayes

  • Decision Tree

  • Support Vector Machine

Module 14: Clustering Problems

  • Introduction to Un-Supervised Learning

  • K-Means Clustering

Module 15: NLP

  • Working with Text Data

Module 16: Project

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