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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

50+ Machine Learning Resources for Self Study in 2025

Pankaj Singh
Pankaj Singh Last Updated : 13 Dec, 2024
10 min read

Introduction

Are you following the trend or genuinely interested in Machine Learning?

Either way, you will need the right resources to TRUST, LEARN and SUCCEED.

If you are unable to find the right Machine Learning resource in 2025? We are here to help.

Let’s reiterate the definition of Machine Learning…

Machine learning is an exciting field that combines computer science, statistics, and mathematics to enable machines to learn from data and make predictions or decisions without being explicitly programmed. As the demand for machine learning skills continues to rise across various industries, it’s essential to have a comprehensive guide to the best resources for learning this powerful technology. 

In this article, we’ll explore a curated list of courses, tutorials, and materials that will help you kickstart your machine-learning journey, whether you’re a complete beginner or an experienced professional looking to deepen your knowledge.

Machine Learning Resources

Table of contents

Here’s what you will get from the article:

  • Basic and Specialized Online Courses on Machine Learning
  • Book on Machine Learning
  • Events or Conferences Related to Machine Learning
  • YouTube Channels on Machine Learning

Free advice to get expertise in Machine Learning…

Why Would You Need Machine Learning Resources?

Machine learning resources are crucial for learning, research, development, and implementation purposes. Individuals and organizations require access to online courses, textbooks, tutorials, research papers, datasets, libraries, toolkits, and community platforms to build knowledge, develop cutting-edge models, integrate machine learning capabilities, teach and train others, benchmark performance, and stay updated with the latest advancements in this rapidly evolving field. These resources enable effective learning, exploration, prototyping, deployment, and understanding of machine learning concepts and techniques across various domains and applications.

The Beginner Course on Machine Learning

Beginner-Friendly Courses For those new to machine learning, starting with a foundational course is crucial. 

Here are some highly recommended options:

  1. Google’s Machine Learning Crash Course: This free course from Google offers a practical introduction to machine learning, featuring video lectures, case studies, and hands-on exercises. It’s an excellent resource for those who learn best through theory and practice.

    Link: Machine Learning Crash Course with TensorFlow APIs
  2. Machine Learning Certification Course for Beginners by Analytics Vidhya: In this complimentary course on machine learning certification, participants will delve into Python programming, grasp fundamental concepts of machine learning, acquire skills in constructing machine learning models, and explore techniques in feature engineering aimed at enhancing the efficacy of these models.

    Link: Machine Learning Certification Course for Beginners by Analytics Vidhya
  3. HarvardX: CS50’s Introduction to Artificial Intelligence with Python: Led by the dynamic David Malan, CS50 is Harvard’s premier offering on EdX, boasting an audience exceeding one million eager learners. Malan’s ability to distill complex concepts into captivating and accessible narratives makes this course a must for anyone seeking an engaging introduction to machine learning. Whether you’re looking to bolster your technical prowess or simply want to delve into the exciting realm of AI, CS50 promises an enjoyable learning journey.

    Link: HarvardX: CS50’s Introduction to Artificial Intelligence with Python
  4. IBM Machine Learning with Python: Machine learning presents an invaluable opportunity to unearth concealed insights and forecast forthcoming trends. This Python-based machine learning course equips you with the essential toolkit to initiate your journey into supervised and unsupervised learning methodologies.

    Link: IBM Machne Learning with Python

Specialization Course on Machine Learning

Specialized Courses and Resources Once you’ve grasped the fundamentals, you can explore more advanced and specialized topics in machine learning:

  1. deeplearning.ai Specializations: Taught by Andrew Ng and his team, these Coursera specializations provide in-depth coverage of deep learning, convolutional neural networks, sequence models, and other cutting-edge techniques.

    Link: deeplearning.ai Specializations

    You can also explore more courses on the website.
  2. Certified AI & ML BlackBelt PlusProgram: This comprehensive certified program combines the power of data science, machine learning, and deep learning to help you become an AI & ML Blackbelt! Go from a complete beginner to gaining in-demand industry-relevant AI skills.

    Link: Certified AI & ML BlackBelt PlusProgram
  3. Machine Learning Specialization by University of Washington: This Specialization was crafted by prominent scholars at the University of Washington. Embark on a journey through practical case studies designed to provide hands-on experience in pivotal facets of Machine Learning such as Prediction, Classification, Clustering, and Information Retrieval.

    Link: Machine Learning Specialization by University of Washington
  4. AWS Machine Learning Learning Path: A Learning Plan pulls together training content for a particular role or solution and organizes those assets from foundational to advanced. Use Learning Plans as a starting point to discover training that matters to you. This Learning Plan is designed to help Data Scientists and Developers integrate machine learning (ML) and artificial intelligence (AI) into tools and applications.

    Link: AWS Machine Learning Learning Path

    Here are More Courses by DeepLearning.AI and Others:
  1. Supervised Machine Learning: Regression and Classification: DeepLearning.AI
  2. AI For Everyone: DeepLearning.AI
  3. Generative AI for Everyone: DeepLearning.AI
  4. Advanced Learning Algorithms: DeepLearning.AI
  5. Calculus for Machine Learning and Data Science: DeepLearning.AI
  6. Structuring Machine Learning Projects: DeepLearning.AI
  7. Machine Learning Modeling Pipelines in Production: DeepLearning.AI
  8. Unsupervised Learning, Recommenders, Reinforcement Learning: DeepLearning.AI
  9. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: DeepLearning.AI
  10. Neural Networks and Deep Learning: DeepLearning.AI
  11. Mathematics for Machine Learning: Imperial College London
  12. Introduction to Statistics: Stanford University
  13. Machine Learning and Reinforcement Learning in Finance: New York University
  14. Data Structures and Algorithms: University of California San Diego

For Practice, You can Refer to the Kaggle Competitions

The theory is great, but nothing beats rolling up your sleeves and getting your hands dirty with real-world problems. Enter Kaggle, a platform that hosts data science competitions and provides a wealth of datasets to practice on. Start with beginner-friendly challenges like “Cats vs Dogs” or “Titanic” to get a feel for Exploratory Data Analysis (EDA) and use libraries like Scikit-Learn and TensorFlow/Keras. This practical experience will solidify your understanding and prepare you for more complex tasks.

By now, you should have a solid grasp of ML fundamentals and some practical experience. It’s time to start specializing in areas that pique your interest. If computer vision captivates you, dive into more advanced Kaggle notebooks, read relevant research papers, and experiment with open-source projects. If Natural Language Processing (NLP) is your jam, study transformer architectures like the Linformer or Performer and explore cutting-edge techniques like contrastive or self-supervised learning.

Books on Machine Learning

Here are the books on Machine Learning that you must keep handy:

  1. Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis

    Amazon Link: Click Here
ML Resources

This book is a must-read if you’re looking for a unified perspective on probabilistic and deterministic machine learning approaches. It presents major ML methods and their practical applications in statistics, signal processing, and computer science, supported by examples and problem sets.

  1. Hands-On Machine Learning with Scikit-Learn & TensorFlow by Aurélien Géron

    Amazon Link: Click Here
ML Resources

This book helps you understand machine learning concepts and tools for building intelligent systems. It covers various techniques, from simple linear regression to deep neural networks, with hands-on exercises to reinforce your learning. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow is the go-to resource for diving into practical implementation. Its thorough and hands-on approach makes it indispensable for getting started and proficiently building intelligent systems.

  1. Python Data Science Handbook: Essential Tools for Working with Data

    Amazon Link: Click Here
Machine Learning Resources

The “Python Data Science Handbook” is an essential resource for researchers, scientists, and data analysts using Python for data manipulation and analysis. It covers all key components of the data science stack, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn, providing comprehensive guidance on storing, manipulating, visualizing, and modeling data. Whether cleaning data, building statistical models, or implementing machine learning algorithms, this handbook offers practical insights and solutions for day-to-day challenges in scientific computing with Python.

  1. You Can Also Read: SuperIntelligence, The Master Algorithm, Life 3.0, and more. 

For more books: Must Read Books for Beginners on Machine Learning.

Here are Books on Mathematics for Machine Learning:

  1. The Elements of Statistical Learning

    Amazon Link: Click Here
ML Resources
  1. The Matrix Calculus You Need For Deep Learning by Terence Parr & Jeremy Howard

    Paper Link: Click Here
ML Resources
  1. Applied Math and Machine Learning Basics by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

    Amazon Link: Click Here
ML Resources
  1. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

    Amazon Link: Click Here
Machine Learning Resources

This is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.

  1. Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy

    Amazon Link: Click Here
Machine Learning Resources

Here’s the GitHub Link for more books: GitHub Mathematics for ML

Understand and learn Machine Learning for Maths here: How to Learn Mathematics For Machine Learning?

Tools to Use for Machine Learning

The usual suspects for tools to learn ML are the following:

  1. Firstly, Python for high-level programming
  2. Pandas for dataset manipulation
  3. Numpy for numerical computing on CPU
  4. Scikit-learn for non-deep learning machine learning models
  5. Tensorflow or Pytorch for Deep Learning machine learning models
  6. Higher-level wrapper Deep Learning libraries like Keras and fast.ai
  7. Basics of Git for working on your project
  8. Last but not least, Jupyter Notebook or Google Colab for code experimentation

Here are more tools: Here are 9 Must Need Machine Learning Tools for Your ML Project

Want More?

Here is the GitHub link.

Machine Learning Blogs

Here are the Machine Learning blogs:

  1. Distill.pub, a meticulously crafted journal showcasing visually captivating content on machine learning topics, appears to be taking a one-year break due to the team experiencing burnout. Nonetheless, the platform hosts top-notch ML material.
  2. Analytics Vidhya, often appearing as the second search result on Google, offers abundant valuable content on Machine Learning and relevant fields.
  3. Machine Learning Mastery consistently emerges as a go-to resource for those who frequently turn to Google during projects. The blog’s well-written articles and remarkable SEO prowess in ML-related subjects are noteworthy.

Machine Learning Community

Here are the communities you can reach for updates on Machine Learning:

  1. r/LearnMachineLearning serves as an exceptional Reddit community (401k members) tailored for novices seeking guidance, sharing their projects, or finding inspiration from the endeavors of fellow members.
  2. r/MachineLearning stands out as a valuable community (2.9M members) for staying updated with the latest advancements in machine learning and gaining insightful perspectives on current events within the ML community. The subreddit offers high-quality content and allows one to understand the prevailing sentiments and opinions within the field through observation.
  3. The Analytics Vidhya Community provides another avenue for engaging with like-minded individuals interested in analytics and machine learning. It offers a platform for discussions, collaborations, and knowledge sharing.

Machine Learning Events

Here are the current and upcoming events on Machine Learning:

  1. Data Hack Summit 2024: The Data Hack Summit 2024, proudly presented by Analytics Vidhya, promises to be an immersive and enlightening experience for data enthusiasts worldwide. As one of the premier events in data science and analytics, this summit brings together industry leaders, seasoned professionals, and aspiring data scientists for a collaborative exploration of the latest trends, technologies, and best practices shaping the future of data-driven innovation.
  2. NeurIPS (Neural Information Processing Systems) Conference: This is the mythical machine learning conference on neural networks. It has become overcrowded recently, and its usefulness has been questioned. Still, if you can’t attend, it’s a good idea to check what the researchers who get accepted work on.

There are a lot more out there; for more conferences like this, explore – 24 GenAI Conferences that you can’t MISS in 2025

YouTube Channels to Follow in 2025

  1. Sentdex: Python Programming tutorials go beyond the basics. Learn about machine learning, finance, data analysis, robotics, web development, and game development.
  2. Deep Learning AI: Welcome to the official DeepLearning.AI YouTube channel! Here, you can find videos from our Coursera programs on machine learning and recorded events.
  3. Two-Minute Paper: Keeping abreast of machine learning research can be challenging. Two Minute Paper steps in, condensing intricate research papers into easily digestible video snippets.
  4. Kaggle: Kaggle is the largest global community of data scientists, providing a platform for collaboration, competition, and learning in data science and machine learning.
  5. 3Blue1Brown: Embracing the adage that a single image can convey myriad meanings, 3Blue1Brown employs captivating visualizations to elucidate intricate mathematical and machine-learning principles.
  6. StatQuest with Josh Starmer: Short, engaging videos that demystify complex statistical concepts crucial for ML.
  7. FreeCodeCamp’s Machine Learning Tutorials on YouTube.

You can also follow other YouTube channels: Siraj Raval, Krish Naik, Jeremy Howard, and Data School.

Research Papers and GitHub Repositories

As you progress in your machine learning journey, staying up-to-date with the latest research and exploring open-source repositories can be invaluable:

  1. ArXiv: This repository for electronic preprints is a treasure trove of cutting-edge research papers in machine learning, artificial intelligence, and related fields.
  2. GitHub: Many researchers and developers share their code and implementations on GitHub. Exploring popular repositories can help you understand how to implement complex algorithms and techniques.
  3. Conference Proceedings: Major machine learning conferences like DHS 2024, NeurIPS, ICML, and ICLR publish their proceedings, which can be a valuable resource for staying informed about the latest breakthroughs and advancements.

Bonus Point Chimed-in For You

Building Your Network

Collaboration and Mentorship: While independent learning is great, don’t underestimate the power of collaboration and mentorship:

  • Join Online Communities and Forums: Connect with like-minded individuals, exchange ideas, and gain new perspectives.
  • Find a Mentor: Having an experienced guide who can provide feedback, insights, and career advice can be invaluable in navigating the professional landscape of machine learning.

Embrace the Journey

A Lifelong Pursuit Machine learning is a rapidly evolving field, with new breakthroughs and advancements happening constantly. To truly thrive, you need to embrace a lifelong learning mindset:

  • Stay Curious: Follow industry leaders and researchers, attend conferences and workshops, and continuously seek out new resources and challenges.
  • Treat it as an Ongoing Adventure: Machine learning isn’t a destination; it’s a journey. Approach it with patience, dedication, and an insatiable thirst for knowledge.

Mastering machine learning won’t be easy, but it’s an incredible, rewarding path. With the right resources, guidance, and mindset, you’ll be well on your way to becoming a machine learning pro, solving complex problems, and driving innovation. Just take it one step at a time, and never stop learning!

HackerRank: Sharpen your Python skills with a vast collection of coding challenges from beginner to expert level.

Conclusion

Learning machine learning is a continuous journey that requires dedication, practice, and an insatiable curiosity. By leveraging the resources outlined in this article, you’ll be well-equipped to navigate the exciting world of machine learning and unlock its full potential. Remember, the key to success is to start with a solid foundation, consistently practice and apply your knowledge, and stay up-to-date with the latest developments in this rapidly evolving field.

I hope you found this article helpful in getting the right Machine Learning Resources. Feel free to comment if you have any suggestions or want to add something I missed.

For more articles on Machine learning, explore our Machine learning blogs.

Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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