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Reading list

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
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 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 11 YouTube Channels to Learn Generative AI

Yana Khare
Yana Khare 09 Sep, 2024
8 min read

Introduction

As Generative AI continues to reshape industries from content creation to automation, learning this technology has become essential for aspiring AI professionals and enthusiasts alike. With its vast educational channels, YouTube offers an accessible platform for mastering Generative AI concepts, tools, and applications. This article explores the top 10 YouTube channels to learn Generative AI.

Table of contents

CodeWithHarry

@CodeWithHarry | 6.6M subscribers | 2.3K video

YouTube Channels to Learn Generative AI

Channels. It focuses on programming tutorials, including Python, machine learning, and artificial intelligence. Overall, the channel offers detailed and engaging explanations best suited for newcomers and intermediate learners in the coding and AI fields. This YouTube channel teaches basic and advanced Python programming tutorials for utilizing Generative AI. It offers detailed instructions for implementing different AI algorithms and techniques, emphasizing real-world uses.

Key Features:

  • Beginner-Friendly: Offers accessible content to those new to programming and AI.
  • Practical Projects: Focuses on hands-on projects that help viewers apply their knowledge.
  • Clear Explanations: Provides straightforward and engaging explanations of coding concepts and AI techniques.

Click here to access this YouTube channel to learn Generative AI

Two Minute Papers

@TwoMinutePapers | 1.58M subscribers | 914 videos

YouTube Channels to Learn Generative AI

Key Features:

  • Concise: Videos are typically 2–5 minutes long, focusing on key findings from research papers.
  • Up-to-date: Provides summaries of the most recent research papers and AI advancements.
  • Accessible: Presents complex AI concepts in an easy-to-understand format, making it suitable for beginners and enthusiasts.

Click here to access this YouTube channel to learn Generative AI

StatQuest with Josh Starmer

@statquest | 1.24M subscribers | 283 videos

StatQuest with Josh Starmer is well-known for its knack for simplifying intricate statistical and machine-learning ideas, including those tied to Generative AI. Josh Starmer’s informative breakdowns assist viewers in grasping the mathematical principles and algorithms that underpin AI models. This YouTube channel for understanding Generative AI explains statistical and machine learning concepts utilized in Generative AI, like probability distributions, neural networks, and optimization techniques. It provides lessons on implementing and comprehending different AI models, including ones for Generative AI.

Key Features:

  • Simplified Explanations: Presents complex statistical and machine learning concepts in an easy-to-understand format.
  • Educational Focus: Focuses on teaching the foundational principles and algorithms used in AI.
  • Engaging Content: This website uses a friendly and engaging style to make learning about AI and its underlying math more accessible.

Click here to access this YouTube channel to learn Generative AI

Google Cloud Tech

@googlecloudtech | 1.15M subscribers | 5K videos

YouTube Channels to Learn Generative AI

Key Features:

  • Cloud Integration: Focuses on how to use Google Cloud’s tools and services for AI development.
  • Comprehensive Guides: Provides step-by-step guides for using cloud-based AI solutions and services.
  • Real-World Use Cases: Demonstrates practical applications and benefits of Generative AI in various industries.

Click here to access this YouTube channel to learn Generative AI.

DeepLearning.AI

@Deeplearningai | 335K subscribers | 446 videos

YouTube Channels to Learn Generative AI

Established by AI innovator Andrew Ng, DeepLearning.AI provides organized courses on AI and machine learning, focusing on Generative AI. The channel continues Ng’s well-liked Coursera classes, offering thorough teachings on AI, machine learning, and deep learning, emphasizing practical uses and real-life examples. This YouTube channel provides tutorials and courses on machine learning, AI, and Generative AI, focusing on deep learning and neural networks. It includes interviews with top business figures, conversations about ethical artificial intelligence, and observations on artificial intelligence trends. It also delves into practical uses of AI, like generating images, natural language processing , and applying AI technologies.

Key Features:

  • Expert-Led: Courses and tutorials are led by Andrew Ng and other AI experts.
  • Structured Learning: Provides a step-by-step approach to mastering AI, suitable for learners from beginner to advanced levels.
  • Practical Applications: Focuses on implementing AI solutions, including Generative AI models, in real-world projects.

Click here to access this YouTube channel to learn Generative AI

AI Explained

@aiexplained-official | 280K subscribers | 84 videos

Key Features:

  • In-Depth Explanations: Offers detailed, technical breakdowns of how AI models, particularly Generative AI systems, work.
  • Focus on Ethics: Highlights AI’s ethical issues and societal impact, such as misinformation and bias.
  • Emerging Trends: Explores the latest trends and advancements in Generative AI and their applications in creative fields.

Click here to access this YouTube channel to learn Generative AI

Olivio Sarikas

@OlivioSarikas | 232K subscribers | 1K videos

YouTube Channels to Learn Generative AI

Olivio Sarikas provides in-depth tutorials and analyses of AI and machine learning technologies, including Generative AI. His platform is recognized for its hands-on teaching style, concentrating on practical learning and coding guides that aid viewers in comprehending and constructing AI models starting from the basics. This YouTube channel provides detailed tutorials for creating and implementing Generative AI models, such as neural networks and deep learning structures. It also delves into hands-on coding examples and project-based learning to apply AI solutions.

Key Features:

  • Hands-On Tutorials: Emphasizes practical coding exercises and real-world projects.
  • Technical Depth: Provides detailed explanations of complex AI concepts and algorithms.
  • Project-Based Learning: Focuses on building and deploying AI models, allowing viewers to apply what they’ve learned.

Click here to access this YouTube channel to learn Generative AI

All About AI

@AllAboutAI | 164K subscribers | 450 videos

Key Features:

  • Hands-On Tutorials: Provides practical, step-by-step tutorials on using various AI tools and models.
  • Real-World Applications: Focuses on applying AI in creative fields such as art, text generation, and multimedia.
  • Beginner-Friendly: Content is approachable for beginners who want to explore AI tools and learn how to apply them quickly.

Click here to access this YouTube channel to learn Generative AI

Robert Miles AI Safety

@RobertMilesAI | 156K subscribers | 48 videos

YouTube Channels to Learn Generative AI

Key Features:

  • Focus on AI Safety: This section provides in-depth discussions of the safety concerns associated with AI systems, including Generative AI.
  • Ethical Perspective: Emphasizes AI’s moral and ethical implications, offering insights on responsible AI development.
  • Expert-Led Discussions: Features interviews and conversations with AI researchers and experts on AI safety and governance.

Click here to access this YouTube channel to learn Generative AI

Aitrepreneur

@Aitrepreneur | 153K subscribers | 160 videos

Key Features:

  • AI in Business: Focuses on the entrepreneurial aspects of AI, mainly how to build businesses and services around Generative AI.
  • Tool Reviews: This site provides reviews and tutorials on AI tools for entrepreneurs, including those used for content generation, automation, and marketing.
  • Innovation Insights: This site offers insights into the future of AI-driven innovation, covering emerging trends in Generative AI and their impact on industries.

Click here to access this YouTube channel to learn Generative AI

Analytics Vidhya

@Analytics Vidhya | 78.1K subscribers | 1.2K videos

YouTube Channels to Learn Generative AI

Key Features:

  • Industry-Relevant Content: Covers practical applications of AI and case studies relevant to the industry.
  • In-Depth Tutorials: Provides comprehensive tutorials on various AI and machine learning topics, including Generative AI.
  • Expert Insights: Features insights from industry experts and thought leaders in data science and AI.

Click here to access this YouTube channel to learn Generative AI.

Conclusion

Generative AIGenerative AI youtubeYouTubeYouTube Channels
Yana Khare
Yana Khare 09 Sep, 2024

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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