Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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 Free Data Science Courses to Kick Start your Data Science Journey!

Harshit Ahluwalia
Harshit Ahluwalia Last Updated : 22 Dec, 2020
10 min read

Overview

  • Here is a list of 15 Free Data Science Courses to get you going initially
  • These are well-curated courses. Please probe the resources attached to these free data science courses to understand them better

Introduction

It is Data Science, not Rocket Science.

Due to the democratization of AI and ML, the data science field is undergoing massive growth. A lot of long shot applications like self-driven cars, smart AI assistants have come to life. It is really exciting!

I have come across hundreds of data science aspirants who really want to pursue this field but aren’t able to navigate their way through this uncertain path. It is not their fault. The majority of people haven’t graduated in this field. So getting back to the main question – How do build a successful career in data science and more importantly, what are the necessary resources to do so?

In this article, I am listing down 15 free courses, starting with beginner courses that will help you navigate your way through a data science career and then jump into each important machine learning algorithm. I have also mentioned a few project-based courses, this will surely help you in practical learning.

However, These free data science courses are not a substitute for a well-guided course. The AI and ML Blackbelt+ program is the leading industry course for data science. Along with 14+ courses and 39+ projects, it offers you – 

  • 1:1 Mentorships with Industry Practitioners
  • Comprehensive & Personalised Learning Path
  • Dedicated Interview Preparation & Support

You can check the entire program here.

 

List of Free Data Science Courses

  1. Introduction to AI and ML
  2. Python for Data Science
  3. Pandas for Data Analysis
  4. Sklearn
  5. KNN
  6. Regression
  7. Decision Trees
  8. Ensemble Learning
  9. Naive Bayes
  10. SVM
  11. Evaluation Metrics
  12. Introduction to NLP
  13. Getting started with Neural Networks
  14. Loan Prediction Problem
  15. Winning Data Science Competitions

 

1. Introduction to AI and ML

“The AI revolution is here – are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?”

Free Data Science Courses - Introduction to AI and ML

Artificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function – from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.

And this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn’t been a better time to get into this field!

This course helps you answer all the conceptual questions you might have about building a successful career in data science and machine learning.

You can find the course material here.

2. Introduction to Python

Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?

Free Data Science Courses - Introduction to Python

You have come to just the right place!

  • Most industry experts recommend starting your Data Science journey with Python
  • Across the biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects
  • Stackoverflow survey for 2019 had Python outrank Java in the list of most loved languages

Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don’t need to be well-versed with them all.

  • Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set.
  • It consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.
  • Over the years, with strong community support, this language has obtained a dedicated library for data analysis and predictive modeling.

You can find the course material here.

Now that we have the basics cleared up – Let’s move to specialized courses for machine learning and its libraries in Python.

 

3. Pandas for Data Science

Free Data Science Courses - Pandas For Data Science

Pandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.

According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world!

This free course will introduce you to the world of Pandas in Python, how you can use Pandas to perform data analysis and data manipulation. The perfect starting course for Python and Pandas beginners!

 

4. Getting started with Scikit Learn for Machine Learning

Free Data Science Courses - Getting started with scikit-learn

Scikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:

  • Linear Regression using sklearn
  • Logistic Regression using sklearn, and so on.

There’s no question – scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.

We love the clean, uniform code, and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.

In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree,s or a random forest, sklearn is your go-to library.

You can find the course material here.

 

5. K – Nearest Neighbors Algorithm in Python and R

Free Data Science Courses - KNN Algorithm

K-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.

And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning. 

The KNN algorithm is simple to understand, easy to explain, and perfect to demonstrate to a non-technical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works.

You can find the course material here.

 

6. Fundamentals of Regression Analysis

Free Data Science Courses - Regression Analysis

Linear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.

Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.

The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:

  • Ridge regression
  • Lasso regression
  • Polynomial regression
  • Stepwise regression, among others.

Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.

Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they’re easy to understand and implement using a programming language of your choice.

You can find the course material here.

 

7. Getting started with Decision Trees

Getting Started with Decision Trees

Decision Trees are the most vital and important concept in machine learning. This course will set the basis for the advanced ensemble learning concepts.

Bonus: This free course comes with a degree as well.

A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction/label.

This course covers the following topics –

  1. Introduction
  2. Terminologies
  3. The different splitting criterion for decision tree-like Gini, chi-square
  4. Implementation of the decision tree in Python

You can access the course here.

 

8. Ensemble learning and ensemble learning techniques

Ensemble learning and ensemble learning techniques

Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models. You must have used or come across several of these ensemble learning techniques in your machine learning journey:

  • Bagging
  • Boosting
  • Stacking
  • Blending, etc. 

These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!

You can find the course material here.

9. Naive Bayes from Scratch

Naive Bayes from Scratch

Naive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.

The beauty of Naive Bayes lies in its incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!

So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default – the Naive Bayes algorithm will get you on your way.

You can find the course material here.

 

10. Support Vector Machine in Python and R

SVM

Want to learn the popular machine learning algorithm – Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.

This free course will not only teach you the basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.

This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.

You can find the course material here.

11. Evaluation Metrics for machine learning

Evaluation Metrics for machine learning

Evaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores – unable to understand which model is performing well.

Wondering where evaluation metrics fit in? Here’s how the typical machine learning model building process works:

  • We build a machine learning model (both regression and classification included)
  • Get feedback from the evaluation metric(s)
  • Make improvements to the model
  • Use the evaluation metric to gauge the model’s performance, and
  • Continue until you achieve a desirable accuracy

Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.

If you’ve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well – you’ve come to the right course!

You can find the course material here.

 

12. Introduction to NLP

Introduction to NLP

Natural Language Processing is expected to be worth 30 Billion USD by 2024 with the past few years seeing immense improvements in terms of how well it is solving industry problems at scale.

Natural Language has gained importance in the last few years due to recent advancements. This course will help you start your journey in the NLP space. You can take up this free course without any prerequisites except Python.

This free course will guide you to take your first step into the world of natural language processing with Python and build your first sentiment analysis Model using machine learning.

 

13. Getting started with Neural Networks

Getting started with Neural Networks

From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us.

Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.

This free course will give you a taste of what a neural network is, how it works, what are the building blocks of a neural network, and where you can use neural networks.

 

14. Loan Prediction Problem

Loan Prediction Problem

Do you need a free course which can help you solve data science problems practically? This amazing course will guide you in solving a real-life project.

This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in. In this course, you will get to solve a real-life case study of Dream Housing Finance.

 

15. Wining Data Science  – Learn from Elite Data Scientist

Wining Data Science  - Learn from Elite Data Scientist

There is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.

This course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks. 

This course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R, and many more!

From effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!

You can find the course material here.

End Notes

It is exciting to be in the data science industry. These free courses cover almost all the basics you will require to kickstart your career in data science.

I hope this helps you clear all the concepts. If you want to learn data science comprehensively then I have a great suggestion for you guys! The AI and ML Blackbelt+ program the industry leader in data science programs. Here you will not only get access to 14+ courses and 39+ projects but 1:1 mentorship sessions. The mentor will help you customize the learning path according to your career goals and make sure that you achieve them!

Harshit Ahluwalia
Harshit Ahluwalia

Growth Hacker | Generative AI | LLMs | RAGs | FineTuning | 62K+ Followers https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Vidhya
Vidhya

Very nice article, well defined and picture perfect thought process.this course is Very much needed for the present market, marketing and independent organisational and industrial revolution and analysis. I'm very much interested in this course, kindly keep us updated about this trending course.

123

Harshit Ahluwalia
Harshit Ahluwalia

Thanks for your appreciation, I'll surely update the list of free courses.

123

Nidhi Kumari
Nidhi Kumari

I want a free short term courses from data science related topics.

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details