Machine Learning and Data Science ( Hindi )

Machine Learning and Data Science ( Hindi )

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Course Overview

Interested in the field of Machine Learning? This course is for you. Not only will you learn what all the hype is all about but also do a deep dive into the "behind the curtain" workings of the AI ML world.
Currently, every single business/tech domain is being influenced by data science in one way or another, so why not take the first step of upgrading yourself for the future.

This course is made by an experienced Data Scientist and is made as simple as possible to allow anybody new to the field to easily grasp the basics of how statistics and coding works in machine learning.

We will be working on live examples with real-life datasets and finally will finish the course with 2 mini projects on AI/ML. We will be covering the following topics:

1. Regression
2. Classification
3. Clustering
4. Preprocessing
5. Model optimizations
6. Statistics basic and in-depth formulas
and much more

As a bonus, this course includes all the shared slides and the codes done during teaching for easy access to the content. These codes can be downloaded easily and used for your own problem-solving.

1. What are the requirements?

  • All it needs is basic high school level mathematics knowledge and some basic coding knowledge, preferably python

2. What am I going to get from this course?

  • The goal is to get an in-depth understanding of the fundamentals of AI and ML

3. Who this course is for?

  • The course is for anybody who - Is interested in machine learning - Is a student thinking of building a lucrative and interesting career in the data science domain - Is a working professional wishing for a change to the cutting edge tech - Is looking to add value to their jobs - Is a data analyst who wants to level up into a data scientist - Intermediates who wish to get a holistic idea of how AI functions

About the Author

    1. I am Abhinav Mahapatra. I am a senior data scientist at a multinational company having over 6 years of work experience and over 4 years of teaching experience. I am deeply passionate about AI and its applications. I actively research the latest and upcoming technologies and try to make them as simple as possible for my viewers to understand.


Course Curriculum

Preparation

  • Installation of Anaconda and Python
     
  • Introduction to AI/ML
     
  • The pros and cons of AI/ML
     
  • Numpy basics
     
  • Pandas basics
     
  • Scikit-learn basics
     
  • matplotlib basics
     

Basic Statistics

  • Introduction to statistics with focus on inferential statistics
     
  • Continuous vs. Discrete values and how are they used in ML?
     
  • Standard Deviation and Variance
     
  • Mean, Median and Mode
     
  • Central limit theorem and where it is used in AI?
     
  • Conditional probability with problems
     
  • Hypothesis testing
     

Introduction to Machine learning

  • How machine learning different traditional software development?
     
  • How is a ML model made?
     
  • Concepts of Over fitting and under fitting in ML
     
  • What is bias, variance, Bias-Variance Trade off and how to deal with it?
     

Regression

  • What Is Regression And The Underlying Mathematics Behind It?
     
  • Gradient Descent in regression (optional statistics)
     
  • Simple Linear Regression
     
  • Multiple Linear Regression
     
  • Polynomial regression
     
  • Decision tree regression
     
  • Random forests
     
  • Training, Testing and Evaluation of regression models
     

Classification

  • What is Classification and the underlying mathematics behind it.
     
  • Cross entropy in classification (Optional statistics)
     
  • Types of Classification - 1 Logistic regression
     
  • K-nearest neighbors
     
  • Kernel-SVM
     
  • Naïve Bayes
     
  • Decision tree classification
     
  • Random forests
     

Clustering

  • What is Clustering and the underlying mathematics behind it.
     
  • Types of Clustering and Math/working behind them. 1 - K-means clustering
     

Additional content

  • Lasso and Ridge regression
     
  • K-fold cross validation
     
  • PCA and LDA
     
  • Grid search
     

Final Project/Grand Hackathon

  • Final Project/Grand Hackathon 1
     

Final Project/Grand Hackathon 2

  • Final Project/Grand Hackathon 2
     

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