Demo Videos

Course Highlights

    • 15+ Hours of Course Content
    • Covers Python & Jupyter Notebook
    • Expertise on Linear & Logistic Regression, Naive Bayes, Decision Tree
    • Instant Doubt Clarification
    • Telegram Developer Community
    • Real-world Capstone Project
    • Quizzes & Mini-projects
    • Certification of Completion
    • Become an Alumni of MicroDegree
    • *12+ hours uploaded. Fresh Course Content Updated Daily
    • Unlimited access*
  • Who Should Attend

    • Anybody interested in learning Machine Learning and Coding
    • Any Degree, Engineering & IT Students
    • Early Professionals
  • Job Opportunities

    • Machine Learning Engineer
    • AI Engineer
    • Data Analyst
    • Data Scientist
    • Data Engineer
    • ML Architect
    • Researcher - ML
    • Python Developer
    • Manager - Machine Learning

Course curriculum

  • 1

    Basics

    • Welcome Message

    • Course Intro

    • What is Machine Learning

    • Applications of Machine Learning

    • Join Developer Community

    • AI vs Ml vs DL

    • Types of Learning

    • Machine Learning LifeCycle

    • Traditional Software Engineering vs Machine Learning

    • Basics Summary

    • Machine Learning Basics Quiz

    • Student Feedback - Fundamentals

  • 2

    Foundations

    • Python - Course Preview

    • Python - What is Python

    • Python - Who should Learn Python?

    • Python - Why Python?

    • Python - How to learn Python

    • Python - Basics Of Programming

    • Python - Installation & Setup

    • Python - My First Python Program

    • Python - Instruction Execution

    • Python - Variables

    • Python - Taking User Input

    • Python - Build Calculator Exercise

    • Python - Strings from Beginning

    • Python - String Manipulation

    • Python - Working With Numbers

    • Python - Intro to DataStructures

    • Python - Lists in Python

    • Python - List Methods

    • Python - Number List Manipulation

    • Python - Tuples

    • Python - If Else Statements

    • Python - Logical Operators

    • Python - Comparison Operators

    • Python - Build a Converter App

    • Python - Loops Intro

    • Python - While Loop

    • Python - Guessing Game Project

    • Python - For Loops In Python

    • Python - 2D Lists & Nested Loops

    • Python - Dictionaries

    • Python - Functions Intro

    • Python - Parameters in Functions

    • Python - Return Statements in Functions

    • Python - Word Counter Exercise

    • Python - Intermediate Project - Student Management System

    • Python - Handling Errors

    • Python - Generic Exceptions & Finally

    • Python - Modules

    • Python - Packages in Python

    • Python - File IO

    • Python - Object Oriented Programming (OOPs)

    • Python - Classes and Objects

    • Python - PyPI And Pip

    • Python - Coding Standards

    • Python - Price Tracker App - Intro

    • Python - Web Scraping Using Beautiful Soup

    • Python - Parsing Data

    • Python - Dynamic Multiple Inputs

    • Python - Price Check Logic

    • Python - Write Output File

    • Python - Project Wrap Up

    • Student Feedback - Python

    • Certification Instructions

    • Jupyter Notebook Setup

    • Google Colab - Intro & Basic Setup

    • DataSet - What, Where , How

    • Kaggle Intro

    • Kaggle - Building a Profile

    • Kaggle - Working with Notebooks

    • Kaggle - Sharing a Notebook

    • Kaggle - Dataset Upload

    • Kaggle - Link Dataset to Notebook

    • Assignment: Getting comfortable with Kaggle

    • Foundations - Kaggle quiz

    • Python Fundamentals Recap

    • Python Recap Installing Library

    • Data Visualization using Python - Intro to Matplotlib

    • Intro to Numpy in Python

    • Intro to Pandas in Python

    • Intro to PIL/OpenCV

    • ML Frameworks - Intro to Scikit-learn, TensorFlow, Pytorch

    • Assignment: Getting comfortable with pandas, numpy, matplotlib

    • Python recap quiz

    • Join Developer Community

    • Student Feedback - ML Basics

    • Referral Program

  • 3

    Intermediate

    • Intro to Linear Regression

    • Linear Regression - Understanding Data

    • Linear Regression - Loading Data

    • Linear Regression - Build & Test a Model

    • Linear Regression Quiz

    • Linear Regression - Finding Linearity

    • Linear Regression - Understanding Linear Function and Slope

    • Linear Regression - Merging Math Line & Training Data

    • Linear Regression - Manual Training Line of Best Fit Regression Line

    • Linear Regression - Root Mean Squared Error

    • Linear Regression - Inferencing

    • Linear Regression - Coefficient & Intercept

    • Linear Regression - Bias & Variance

    • Linear Regression - UnderFitting vs Overfitting

    • Linear Regression Summary

    • Student Feedback - Linear Regression

    • Logistic Regression Intro

    • Logistic Regression - Data Setup

    • Logistic Regression - Data Cleanup & Feature Engineering - Part 1

    • Logistic Regression - Data Cleanup & Feature Engineering - Part 2

    • Logistic Regression - Data Cleanup & Feature Engineering - Part 3

    • Logistic Regression - Predicting Future Tips Data - Part 1

    • Logistic Regression - Predicting Future Tips Data - Part 2

    • Logistic Regression - Predicting Future Tips Data - Part 3

    • Logistic Regression - Predicting Future Tips Data - Part 4

    • Logistic Regression - Predicting Future Tips Data - Part 5

    • Logistic Regression - Theoretical Understanding - Part 1

    • Logistic Regression - Theoretical Understanding - Part 2

    • Logistic Regression - Theoretical Understanding - Part 3

    • Logistic Regression Summary

    • Intro to Linear Regression

  • 4

    Advanced

    • How ML Algorithms Learn - Part 1

    • How ML Algorithms Learn - Part 2

    • How ML Algorithms Learn - Part 3

    • Student Feedback - Logistic Regression

    • Naive Bayes - Intro

    • Naive Bayes - Classification vs Regression

    • Naive Bayes - What is Customer Segmentation

    • Naive Bayes - Data Cleanup And Feature Engineering

    • Naive Bayes - Train & Test

    • Naive Bayes - Confusion Matrix

    • Naive Bayes - How it Works?

    • Naive Bayes - Summary

    • Student Feedback - Naive Bayes

    • Decision Tree - Overview

    • Decision Tree - Understanding DataSet

    • Using Decision Tree

    • What is Decision Tree

    • How To Use Decision Tree

    • How to Use Decision Tree Continued

    • Decision Tree - Genie Impurity

    • Visualize Decision Tree Model

    • Random Forest

    • Decision Tree - Summary

    • Student Feedback - Decision Tree

  • 5

    Expert

    • Intro to Image Processing

  • 6

    Capstone Project

    • Intro to Capstone Project

    • Pipeline Of Image Processing

    • Convert Image Data to Features - Part 1

    • Convert Image Data to Features - Part 2

    • Train & Evaluate Model

    • Model Saving & End-To-End Prediction

    • What did you feel about the entire Course

    • Capstone Summary

  • 7

    Bonus

    • What to Learn Next

  • 8

    Certification

    • Project Intro

    • Project Option 1

    • Project Option 2

    • Referral Program

Expert Instructor

 Chandan Adiga

ML Architect

Chandan Adiga is a Machine Learning expert with 10+ years of experience. He completed his M.Tech with Data Analytics specialization from BITS, Pilani - WILP program, and currently serves as an ML architect. He is also skilled at technologies spread across Android, iOS, MEAN stack, and ML/DL in his decade long IT career.

Chandan recommends learning AI & ML since the current technology trends are moving towards the world of automation. Having these relevant skills can give a head start to a potential career path across industries

 Darshan Adiga

Machine Learning Engineer at Datoin

Darshan Adiga is a Machine Learning Engineer and an NLP enthusiast. With an industry experience of more than 6 years, he has gained expertise in the field of Deep Learning, BigData & Distributed Systems. Throughout his ML journey, he has worked on Keras, PyTorch, TensorFlow, scikit-learn and other ML technologies.

He has been contributing to the field of ML and has published numerous research papers as well. With a particular interest in NLP, he has been working on enabling NLP solutions in Kannada Language using cutting-edge technologies.

Learner Review

  • Akshata Kothari

    Thank you so much for this course. The concepts explained by the trainer was really good and clear. As it was in kannada it helped me in understanding the concept better.

  • karthik k

    You are giving examples for every concept which makes us learn faster. Excellent. Keep going

  • Chethana Amin

    It's really a good thought to start a course in kannada. It helps lot of people.

  • Divya G

    Explaining in Kannada is understanding in good manner

  • Deepashree N

    Awesome thing... u guys made python very very easy which can be learnt in our mother tongue.... thank you...

  • Monica Govindappa

    Learning in Kannada is helping out to grasp things quickly over all making it to learn in an interesting way.

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