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Machine Learning Essentials - Master core ML concepts (Udemy.com)

Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ projects!

Created by: Mohit Uniyal

Last updated April 2026

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What you will learn

  • Jumpstart the world of AI & ML
  • Maths of Machine Learning
  • Regression & Classification Techniques
  • Linear & Logistic Regression
  • K-Nearest Neighbours, K-Means
  • Naive Bayes, Text Classification
  • Decision Trees & Random Forests
  • Ensemble Learning - Bagging & Boosting
  • Dimensionality Reduction
  • Neural Networks
  • 8+ Hands on Projects

How it compares

Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:

  • Rating4.5 out of 5higher than 62% of them
  • Reviews874#62 of 156 by number of reviews
  • Length27.9 hourslonger than 88% of them
  • Last updatedApril 2026more recently than 85% of them

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Course Description

Read to jumpstart the world of Machine Learning & Artificial intelligence?


This hands-on course is designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions"

Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.

This comprehensive course is taught by Prateek Narang & Mohit Uniyal, who not just popular instructors but also have worked in Software Engineering and Data Science domains with companies like Google. They have taught thousands of students in several online and in-person courses over last 3+ years.

We are providing you this course to you at a fraction of its original cost! This is action oriented course, we not just delve into theory but focus on the practical aspects by building 8+ projects.

With over 170+ high quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science.

Some of the topics that you will learn in this course.

  • Logistic Regression

  • Linear Regression

  • Principal Component Analysis

  • Naive Bayes

  • Decision Trees

  • Bagging and Boosting

  • K-NN

  • K-Means

  • Neural Networks


    Some of the concepts that you will learn in this course.

    • Convex Optimisation

    • Overfitting vs Underfitting

    • Bias Variance Tradeoff

    • Performance Metrics

    • Data Pre-processing

    • Feature Engineering

    • Working with numeric data, images & textual data

    • Parametric vs Non-Parametric Techniques

Sign up for the course and take your first step towards becoming a machine learning engineer! See you in the course!

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Instructor Details

Mohit Uniyal

Mohit is a Senior Machine Learning Engineer at PayPal and a trusted instructor in Data Science and AI. With 6+ years of teaching experience, he has trained 100,000+ students to become industry-ready and land top tech roles. His courses focus on what truly matters—building real-world, production-ready ML systems and solving business problems, not just theory. As the ML lead at Coding Minutes, he has created structured, practical learning paths to take you from beginner to job-ready.

If you’re serious about a career in AI, his courses give you the exact skills, projects, and mindset needed to succeed.

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Reviews

4.5

874 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Deepu Pk on 9/17/2026

Best course in udemy platform. This course teach you the full mathematics behind machine learning and it's code implementation.Some topics are missing like regularization technique in ML, Polynomial regression, Support Vector Machine etc

By Gunjan Sharma on 1/18/2025

I liked the explanation. still going through the course, will write detailed review once completion. One thing I noticed instead of reading like Robot they could make lectures more engaging, as a listener you have to make effort to keep following him, instructor is in his flow.

By Mohammed Ahmed diaa on 7/14/2024

the explination of concepts are clear and examples make me more understanding the concepts very well but you must provide a presentation or pdf for us ,if we get confused by some concepts instead of watching a whole section ,we can see this pdf last point the projects is good ,but i excepected one project at least that where very attractive in prototfolio but at all thanks alot and hope to see course about deep learning with solving this issues

By Parth Patel on 1/17/2024

This course helped me understand the ML basics very well. Couple of suggestions to improve upon: 1. DT: maybe include how Gini index is different than entropy 2. NN: cover a bit about separate activation functions, optimizers 3. SVM: add the chapter 4. Dimensionality Reduction: Add maybe LDA

By Mario Pardo Tellez on 12/30/2023

Great course! Great for understanding the logic and math, and then the code implementations. Only thing lacking is community and instructor engagement (answering questions), but thankfully I didn't run into any unsolvable problems

By Anonymized User on 7/4/2023

I am actually a AI/ML specialist, holding a PhD in Computer Science and even I am enjoying this and learning! Much of this I already know, but there are lots of little things that I have forgotten or never did. The two lecturers are great in what they do, explaining the concepts really well.

By Minh Cong Hoang on 4/23/2023

hi, could you please create slides similar to the C++ courses you have previously released? I found the dark background color of the slides more eye-friendly, and I can look at them for longer periods of time. I have taken your C++ course and have become accustomed to this format. The white background with black text is not good for my myopic eyes, and I have difficulty concentrating on the material. I have also taken a bit of your machine learning course, but the white background used in that course causes my eyes to tire more quickly than usual, making it difficult for me to concentrate and complete the course. I still appreciate the high-quality images used in your C++ course. Besides, the subtitles of the Coding Minutes courses are often incorrect, and I really hope that you can fix them as soon as possible. It is very difficult for me to learn without accurate subtitles, I am studying at a slow pace and not fully absorbing the material due to the issue of incorrect subtitles. Thank so much!

By Shubham Gupta on 4/18/2023

Perfect start for beginners.. Quite an extensive range of topics covered ... Explains the working and intuition behind each... Code discussions...Community support...Great Correlated explainations with real world for somewhat difficult topics like gradient descent, etc..Must recommend for beginners. :)

By Harsha on 3/25/2023

Each concept is very well explained and I really like the intro to deep learning section (neural networks).

By Manya Kumari on 2/26/2023

This is just awesome!! It helped me to develop my interest in ML I really waited too long for this. My semester project is definitely benefiting from the material of this course, which has made me quite happy and satisfied overall. The tutors of this course deserve a sincere thank you. The most anticipated course of all time is this one.

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Overall Score : 90 / 100