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Machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. It is all about extracting knowledge from data and the application of machine learning methods has in recent years become ubiquitous in everyday life


89259 06892


Duration | 40 hours (2 hrs/day)


  • Pursuing any degree

  • Graduates

  • Basic programming knowledge and mathematical knowledge


  • End of the Course the learners can build a Machine Learning model by themselves

  • Hands-on practical sessions

  • Real-world case studies

  • Interactive visualizations of algorithms

  • Module wise Assessments enable instructor/Learner to track & judge Learner’s knowledge level


1. Introduction to Machine Learning

  • What is Machine Learning?

  • Applications of Machine Learning

  • Types of Machine Learning Algorithms

  • Steps to Solve a Machine Learning Problem

2. Introduction to Python

  • Installation of Python using Anaconda Navigator

  • Python Overview

  • Python Keywords

  • Python Identifiers

  • Data types

  • Type Conversion

  • Input and Output Statements

  • Operators

  • Python control structures

  • Selection Statement / Decision Making Statement

  • Looping Statement / Iterative Statement

  • Python Modules

3. Vectors, Matrices, and Arrays

  • Creating a Vector

  • Creating a Matrix

  • Creating a Sparse Matrix

  • Selecting Elements

  • Describing a Matrix

  • Applying Operations to Elements

  • Finding the Maximum and Minimum Values

  • Calculating the Average, Variance, and Standard Deviation

  • Reshaping Arrays

  • Transposing a Vector or Matrix

  • Flattening a Matrix

  • Finding the Rank of a Matrix

  • Calculating the Determinant

  • Getting the Diagonal of a Matrix

  • Finding Eigenvalues and Eigenvectors

  • Calculating Dot Products

  • Adding and Subtracting Matrices

  • Multiplying Matrices

  • Inverting a Matrix

4. Loading Data

  • Loading a Sample Dataset

  • Loading a CSV File

  • Loading an Excel File

5. Data Wrangling

  • Creating a Data Frame

  • Describing the Data

  • Navigating DataFrames

  • Selecting Rows Based on Conditionals

  • Replacing Values

  • Renaming Columns

  • Finding the Minimum, Maximum, Sum, Average, and Count

  • Finding Unique Values

  • Handling Missing Values

  • Deleting a Column

  • Deleting a Row

  • Dropping Duplicate Rows

  • Grouping Rows by Values

  • Applying a Function

  • Concatenating DataFrames

  • Merging DataFrames


6. Handling Numerical Data

  • Standardizing a Feature

  • Normalizing Observations

  • Generating Polynomial and Interaction Features

  • Transforming Features

  • Detecting Outliers

  • Handling Outliers

  • Deleting Observations with Missing Values

  • Imputing Missing Values


7. Handling Categorical Data

  • Encoding Nominal Categorical Features

  • Encoding Ordinal Categorical Features

  • Encoding Dictionaries of Features

  • Imputing Missing Class Values

  • Handling Imbalanced Classes


8. Handling Text

  • Cleaning Text

  • Parsing and Cleaning HTML

  • Removing Punctuation

  • Tokenizing Text

  • Removing Stop Words

  • Stemming Words

  • Tagging Parts of Speech

  • Encoding Text as a Bag of Words

  • Weighting Word Importance

9. Handling Dates and Times

  • Introduction

  • Converting Strings to Dates

  • Handling Time Zones

  • Selecting Dates and Times

  • Breaking Up Date Data into Multiple Features

  • Calculating the Difference Between Dates

  • Encoding Days of the Week

  • Creating a Lagged Feature

  • Using Rolling Time Windows

  • Handling Missing Data in Time Series

10. A Machine Learning Model using Linear Regression

11. A Machine Learning Model using Trees and Forests

12. A Machine Learning Model using K-Nearest Neighbors

13. A Machine Learning Model using Logistic Regression

14. A Machine Learning Model using Support Vector Machines

15. A Machine Learning Model using Naive Bayes

16. A Machine Learning Model using K-means Clustering


By The End Of Course The Learner Will Be Able To:

  • Design Machine Learning Model

  • Visualize the data

  • Data Analytics

  • Work with various python libraries numpy, pandas, matplotlib, seaborn and sklean

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