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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 15 AI YouTubers to Follow in 2025

A
Analytics Vidhya Last Updated : 05 Dec, 2024
8 min read

Artificial Intelligence is now witnessing exponential growth spanning various fields. With its expansion, the domain presents a plethora of opportunities for aspiring individuals to learn and master. Among them, several AI enthusiasts have emerged, excelling in their respective fields and driven by their unwavering passion for teaching. They strive to make the journey of other learners easier. Yes, YouTubers create informative content freely available on YouTube to educate their audience. Here we will introduce you to 15 AI YouTubers who have established themselves as top performers with a wide array of highly acclaimed videos on Artificial Intelligence, deep learning, and machine learning.

Table of contents

List of Top 15 AI YouTubers to follow in 2024

This list a compilation of influential and knowledgeable content creators in the field of Artificial Intelligence (AI). These YouTubers produce engaging videos, tutorials, and discussions about AI advancements, machine learning, deep learning, and related topics. By following them, viewers can stay up-to-date with the latest AI trends, insights, and learn from experts in the industry.

  • 3Blue1Brown
  • Joma Tech
  • CS Dojo
  • Analytics Vidhya
  • Two Minute Papers
  • Sentdex
  • Corey Schafer
  • StatQuest with Josh Starmer
  • Alex The Analyst
  • Brandon Foltz
  • Harshit Vashisth
  • Ken Jee
  • Data Science Dojo
  • Abhishek Thakur
  • Lex Fridman

3Blue1Brown

3Blue1Brown’s YouTube channel | AI youtuber

Grant Sanderson, an AI YouTuber, owns the channel. He uses animations to explain complex mathematics and machine-learning concepts. His most popular video is on the Fourier series. The covered domains include Data Science, Machine Learning, and Maths. The channel counts among the best Machine Learning YouTube channels.

Joma Tech

Joma Tech’s YouTube channel AI youtuber

His videos are primarily based on data science, Python programming, Machine Learning, and data visualization themes, and life in big tech companies. Joma Tech has created the best YouTube channel for machine Learning where users can dive in for marketing trends and hiring processes. His most popular video is on ‘A Day in the Life of an engineer working from Home.’

CS Dojo

CS Dojo’s YouTube channel AI youtuber

The YouTube channel teaches programming and computer science interestingly and more efficiently. The videos have covered the topics like Learning Python software engineering and web development. His popular video is “Python tutorial for absolute beginners #1- What are Variables,” making the channel the best YouTube channel for machine Learning.

Analytics Vidhya

Analytics Vidhya’s YouTube channel AI youtuber

Analytics Vidhya, with one of the best Machine Learning YouTube channels, defines itself as a platform that helps master the skill of extracting insights from raw data. The company provides a platform for Analytics and Data Science professionals to learn. The covered topics include Natural Language Processing, Machine Learning algorithms, and data visualization. The most popular video is about Data Science projects significant for beginners.

Two Minute Papers

Two minute paper’s YouTube channel AI youtuber

The channel by Konrad Kording, an AI YouTuber, is dedicated to researchers, professionals, and practitioners who wish to remain updated on research works. The domain of interest here is computer graphics and machine learning while dealing with physics and philosophy. The most popular video of this channel is based on ‘Open AI Plays Hide and Seek… and Breaks the Game’.

Sentdex

Sentdex’s YouTube channel

The founder of Sentdex, Harrison Kensley, is a software developer and data scientist by profession and the creator of the best machine learning YouTube channels. Providing a combination of machine learning and programming, the media guide students in learning Python. Besides, the information expands to topics such as Data Analysis, Finance, Robotics, and others. His most popular video deals with practical machine-learning tutorials with Python.

Corey Schafer

Corey Schafer’s YouTube channel

The founder, Corey Schafer, has founded one of the best YouTube channels for Machine Learning. The topics covered here include terminal commands, programming terms, Python, Computer science fundamentals, Git, SQL, and development environments. Besides, there are numerous tricks and tips for career enhancement to learn from. The audience who serve the positions of programmers, software developers, and engineers tend to enjoy and discover the most from the channel. The most popular video by the YouTuber concerns a Python object-oriented programming tutorial.

StatQuest with Josh Starmer

StartQuest with Josh Stammer’s YouTube channel

Another one on the list of the best machine learning YouTube channels, this channel owner believes in breaking down complex topics into smaller understandable bits. Going on with this motto, the issues of interest are mathematics, machine learning, data science, and statistics. Focussing on practical aspects, the most popular video on this channel is about simple and clear explanations of neural networks.

Alex The Analyst

Alex The Analyst’s YouTube channel

The YouTuber Alex Freberg provides a helping hand to the people in the world of Data analysis. Guiding the people willing for a career change, the host offers information on required skills. Besides, the AI YouTuber also deals with programming languages, numerous tools, techniques useful for data analysts, and much more. The most popular video on the best machine learning YouTube channels is about a Data analyst portfolio project where he talks about SQL data exploration.

Brandon Foltz

Brandon Foltz’s YouTube channel

The AI YouTuber is a data scientist and instructor passionate about teaching. Having reached the list of best YouTube channels for machine learning, the content shared here includes lectures, tutorials, and demonstrations. The topics benefitting the audience here are finite mathematics, introductory statistics, operations management, management science, and basic accounting. The videos are well-explanatory and lengthy, requiring will and focus on learning. His love for teaching is evident in his exciting way of explaining and delivering the concepts.

Harshit Vashisth

Harshit Vashishth’s YouTube channel

A full-stack developer runs this best machine-learning YouTube channel. His passion for teaching and love for coding reflects in his videos. With highly viewed videos, his topics of interest are Python and coding, along with other related topics such as HTML, CSS, Javascript, GIT, and much more. His popular video is the introductory course on Python 3.

Ken Jee

Ken Jee’s YouTube channel

Ken Jee is a famous data scientist owing to the production of his best YouTube channel for machine learning. His expertise in Machine Learning, data science, and artificial intelligence lead to creating multiple easily understandable videos. He has also dealt with topics like Kaggle, AI, different projects, books, etc. The most popular video of Ken Jee guides users on methods to start a career in data science.

Data Science Dojo

Data Science Dojo’s YouTube channel

Dojo’s videos on artificial intelligence, machine learning, and data science have brought forward a company with excellent content. The channel is among the best YouTube channels for machine learning. The tutorials range on varying topics with detailed service. For instance, text analytics, R and Python programming, web scraping, and more. Besides, users can also expect tutorials and community talks. The most viewed video of Data Science Dojo is an introduction to web scraping with Python and beautiful soup.

Abhishek Thakur

Abhishek Thakur’s YouTube channel

This best machine learning YouTube channel is available for all data science, machine learning, and deep learning enthusiasts. Created by the world’s first Quadruple Grandmaster of Kaggle, he is also an author of a book. The renowned AI YouTuber focuses on practical and diverse aspects of the topic in machine learning and data science.

Lex Fridman

Lex Fridman
Source: Youtube

Lex Fridman is a prominent figure in the realms of artificial intelligence (AI), robotics, and autonomous systems research. As a Russian-American computer scientist, podcaster, and author, he holds a position as an artificial intelligence researcher at the Massachusetts Institute of Technology. Lex Fridman also curates the popular Lex Fridman Podcast, which encompasses both audio and video content, and is accessible on platforms like YouTube. Through his work and contributions to the field, Fridman has become a respected authority in the AI and robotics communities.

  • Joined: Sep 20, 2006
  • Subscribers: 3.22M
  • Number of Videos: 779
  • Website: https://lexfridman.com/
  • Link: https://www.youtube.com/@lexfridman

Conclusion

The list of YouTubers explaining the concepts in more accessible and fun methods is never-ending. Users can check out the recommended channels to begin. Besides, learners can also find other ways to interact and work on themselves through communities, projects, tutorials, articles, and meetups.

As you embark on your AI learning journey, remember that the quest for knowledge is ongoing. Continue to explore the captivating content created by these AI YouTubers. And if you’re ready to take your understanding of AI to the next level, consider diving into Analytics Vidhya’s Generative AI program. It’s a transformative opportunity to delve deeper into the realm of AI and unleash your creative potential.

We wish you good luck in your AI journey!

Frequently Asked Questions

Q1. What AI do YouTubers use?

A. YouTubers employ various AI tools, such as video analytics, recommendation algorithms, and voice recognition, to enhance content creation, reach their audience, and improve user experiences.

Q2. What is the best YouTube channel for AI?

A. “Two Minute Papers” is a renowned YouTube channel that provides insightful and concise explanations of cutting-edge AI research papers and developments in the field.

Q3. What is the best YouTube channel to learn AI and ML?

Analytics Vidhya is a highly regarded YouTube channel that specializes in machine learning and AI tutorials. It covers a wide range of topics, making it an excellent resource for learning AI and ML.

A
Analytics Vidhya

Analytics Vidhya Content team

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