How to Learn Machine Learning Online Free in 2024?- [Free Resources Included]

So you want to learn machine learning but are stuck with a question, “How to Learn Machine Learning Online Free?“. Then don’t worry. In this article, I will discuss the complete machine learning roadmap with some FREE online resources. And you don’t need to pay $$$$ amount to any course to learn machine learning.

Are you excited to know “How to Learn Machine Learning Online Free?“.

I know you are 🙂

So without any further ado, let’s get started-

How to Learn Machine Learning Online Free?

First, let’s understand what is machine learning and why you need to learn machine learning?

What is Machine Learning?

As the name sounds ”Machine Learning“. That means Machines are Learning something.

Right?.

Machine Learning (ML) algorithms allow machines to learn in the same way a human learns. And Machine learning models learn from the training data or from self-experiences.

You can consider the machine learning model as a “Newborn child”. The newborn child learns from his parent’s instructions or by his self-experiences. He tries to walk but he falls. And then again tries to walk, similarly Machine Learning Works.

Machine learning models learn from training data and predict the output. Based on the predicted output, it improves model accuracy by predicting again.

I hope now you have a basic understanding of machine learning. Now let’s see What is the use of Machine learning or Why Machine learning?

What is the Use of Machine Learning Algorithms?

There are a lot of data available in today’s world. In fact, we are living in the Data Age. This Data is generated not only by humans but also by computers. A huge amount of data is generated daily.

According to one report, By 2025, it’s estimated that 463 exabytes of data will be created each day globally. That’s the equivalent of 212,765,957 DVDs per day!

So, What is the use of this Huge amount of Data?.

Is it garbage?

No!

This huge amount of data contains various useful pieces of information. But, the next question is how to find useful information from the vast amount of Data?

And the answer is-

With the help of Machine Learning algorithms. That’s why Machine learning is very powerful and popular. Many people are shifting their careers into the ML field. And the future of Machine learning is very bright.

Now you understood the importance of machine learning. I know now you are ready to know, “How to Learn Machine Learning Online Free?”.

Right?

So let’s see the machine learning self-starter way–

Machine Learning Self-Starter Way

Learning machine learning by self is almost similar to machine learning algorithms functionality.

Do you know, why?

Because, in the self-starter way, we learn machine learning concepts by ourselves, then we try to implement our learning by working on hands-on projects. We do some mistakes, then we work on our mistakes, and implement them again.

So let’s see what steps are required in the machine learning self-starter way–

Steps Required in Machine Learning Self-Starter Way

The following steps are necessary for machine learning self-starter way-

1. Understand Prerequisites
2. Learning
3. Practicing

Now let’s dive into each step in detail-

1. Understand Prerequisites for Machine Learning

The first step is to understand the prerequisites for machine learning. In this first step, you need to find out what topics or skills are mandatory for machine learning. So the Prerequisites for Machine Learning are-

1. Programming Language

Machine Learning is all about implementation. And if you don’t have programming knowledge, you can’t implement anything. That’s why knowledge of programming languages is compulsory for machine learning. For Machine Learning, the most popular programming languages are Python, R, Java, and C++. As a beginner, you can start with Python.

Python and R are the most wanted languages for machine learning engineers. R Programming language is good for statistical operations.

2. Mathematics

Knowledge of Mathematics is very important in order to understand how machine learning and its algorithms work. In math, the most important topics are-

• Probability and Statistics
• Linear Algebra
• Calculus

Now, let’s have a detailed look at all of them-

a). Probability and Statistics

Probability and statistics are used in Bayes’ Theorem, Probability Distribution, Sampling, and Hypothesis Testing.

b). Linear Algebra

Linear Algebra has two important terms- Matrices and Vectors. They are both used widely in Machine Learning. Matrices are used in Image Recognition.

c). Calculus

In Calculus, you have Differential Calculus and Integral Calculus. These terms help you to determine the probability of events. For example, finding the posterior probability in the Naive Bayes model.

3. Machine Learning Algorithms

You need to know the basics of Machine Learning like- Types of Machine Learning algorithms( Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning), then the detail of each Machine Learning algorithm, and other concepts. For eg-

• Supervised Learning Algorithms
  • Logistic Regression.
  • K-Nearest Neighbors(K-NN)
  • Support Vector Machine(SVM)
  • Kernel SVM.
  • Naive Bayes
  • Decision Tree Classification.
  • Random Forest Classification
• Unsupervised Learning Algorithms
  • K-Means Clustering
  • Hierarchical Clustering.
  • Probabilistic Clustering
• Reinforcement Learning Algorithms
  • Policy Optimization.
  • Q-Learning
  • Learn the Model
  • Given the Model.

So these are the Prerequisites for Machine Learning. Now after understanding the prerequisites, the next step is-

2. Learning

So this is the time to learn all the prerequisites listed here. At this stage, you might have a question, “Where to learn these machine learning prerequisites?”

Right?

For learning these prerequisites, there are various resources available online. But most of the courses are very expensive. And we don’t want to spend such a huge amount on learning when there are various free resources available too.

That’s why I have filtered some of the best Free resources for learning machine learning prerequisites. So the first prerequisite is programming languages(Python or R). You can learn Python and R Programming with these-

Free Python and R Programming Online Tutorials-

1. The Python Tutorial (PYTHON.ORG)
2. Python 3 Tutorial (SOLOLEARN)
3. R Tutorial- Tutorials Point
4. R Tutorial- Statmethods

YouTube Tutorials on Python and R Programming–

1. CS DOJO
2. Programming with Mosh
3. Telusko
4. Clever Programmer
5. Corey Schafer
6. R Programming Tutorial– freeCodeCamp.org
7. R Programming Full Course– Simplilearn

Free Python and R Programming Ebooks-

1. Python and R Programming
2. Head First Python A Brain-Friendly Guide
3. R for Data Science
4. The Book of R

The next prerequisite for machine learning is Mathematics. So you can learn Mathematics (Probability and Statistics, Linear Algebra, and Calculus) with these Free online resources–

Free Mathematics Online Tutorials-

1. Statistics and probability– Khan Academy
2. Probability – Khan Academy
3. Statistics – Probability (TutorialsPoint)
4. Probability Tutorial (Stat Trek)
5. Probability and Statistics (MathisFun)
6. Learn Linear Algebra-Khan Academy
7. Probability theory (Wikipedia)
8. Multivariable calculus– Khan Academy

YouTube Tutorials on Mathematics–

1. Mathematics for Machine Learning [Full Course]– Edureka
2. Statistics for Data Science– Great Learning
3. Mathematics For Machine Learning-Simplilearn
4. Mathematics for Machine Learning– My CS

Free Mathematics Ebooks-

1. An Introduction To Statistical Learning with Applications in R
2. Introduction to Probability 
3. Mathematics For Machine Learning
4. Linear Algebra and Optimization for Machine Learning

The next important prerequisite for machine learning is Machine learning algorithms knowledge. And you can learn the machine learning basics and its algorithms with these Free online resources-

Free Machine learning Online tutorials-

1. Machine Learning– Stanford University
2. Machine Learning for All by University of London
3. Intro to Machine Learning– Udacity
4. Machine Learning Fundamentals– edX
5. What is Machine Learning?– Udemy
6. Machine Learning with Python– Coursera
7. Intro to Machine Learning– Kaggle
8. Machine Learning with Python Tutorial- Tutorials Point
9. Machine Learning Tutorial- Javatpoint

YouTube Tutorials on Machine Learning-

Machine Learning with Python– Great Learning
Machine Learning Tutorial Python– codebasics
Python Machine Learning Tutorial- Programming with Mosh
Machine Learning by Krish Naik

Free Machine Learning Ebooks-

1. Machine Learning for Absolute Beginners
2. The hundred-page machine learning book
3. Hands-On Machine Learning with Scikit-Learn and TensorFlow

So after learning the prerequisites from these Free online resources, the next and most important step is-

3. Practicing

Now it’s time to get your hands dirty and start practicing. Now you might be thinking, “how to begin practice?”. So for practicing machine learning, you need to know a few more things.

The first and most important thing is to be comfortable with machine learning tools– Jupyter and Anaconda. Spend your few hours and play with these tools. Understand what they’re for and why you should use them. For installing and getting the basics of these tools, you can use these tutorials-

Resources for Learning-

1. Anaconda Tutorial by Corey Schafer– This YouTube Tutorial will help you to install Anaconda.
2. Jupyter Notebook for Beginners Tutorial by Dataquest– This is a complete tutorial that will guide you to install Jupyter Notebook and provide basics.

Once you are comfortable with Jupyter and Anaconda, then you need to learn Machine learning libraries depending upon your programming language.

• If you know Python Programming, then you need to learn the Scikit-Learn library. Scikit-learn contains many useful machine learning algorithms built in and ready for you to use. By using Scikit-learn, you can perform data manipulation, analysis, and visualization.
• But if you know R programming, then learn Caret. Caret provides a unified interface for many different model packages in R. By using Caret you can perform data preprocessing, data splitting, and model evaluation.

So after learning machine learning libraries, it’s time to find out the machine learning problem. If you are having difficulty in choosing the machine learning project, then you can work on these Machine Learning projects-

Machine Learning Projects–

1. Recommendation System– As a beginner in machine learning, you can start your first project as a Recommendation system. Where you have to build a system that will recommend the products based on user history. Something like Amazon or Netflix. You can build a Music recommendation system, movie recommendation system, etc.
2. Improve Health Care– The Healthcare industry is widely using machine learning. So you can work on a project that is related to health care such as disease prediction, Diagnostic care, etc. With the help of machine learning, you can reduce doctors’ workload and improve the overall efficiency of the health care system.
3. Stock Price Predictor– This is another Best machine learning project for beginners. Various companies and businesses are looking for software that can monitor and analyze the company’s performance and predict future prices of various stocks. As a beginner, you can develop a machine learning project that predicts the stock price for the upcoming months.
4. Build a Sentiment Analyzer– Sentiment Analysis is one of the interesting projects in machine learning. You can use social media posts and tweets to analyze the sentiment. Social media has lots of user-generated content that you can use for your project. You can check this tutorial for the Sentiment Analysis Project in R.

So there are a few machine learning project ideas, which you can use for your practice. You can choose any other project idea based on your interest.

You can also check this article for more Machine Learning project ideas- Best Machine Learning Projects for Beginners- You Need to Know in 2024

After finalizing the project, the next important step is to understand the entire machine learning workflow. In one machine learning project, the following steps are involved- Data collection, cleaning, and preprocessing. Model building, tuning, and evaluation.

So the first step is data collection. And there are various Free public datasets available from which you can download the dataset for your machine learning project. I would recommend the following portals to download the dataset for your machine learning project-

Free Public Datasets for Your Machine Learning Project-

1. UCI Machine Learning Repository– The UCI Repository has public datasets available for machine learning and data science. The best thing about UCI Repository is that datasets are tagged with different categories such as classification, regression, recommender system, etc.
2. Kaggle- Kaggle is one of the famous platforms for data science, and you can download approx 68,000 public datasets on Kaggle for free. In Kaggle you need to create an account and then you can search for any specific dataset in the search bar. 
3. Data.gov- Data.gov is the repository of the US government which you can use for your research and data science projects such as data visualization, mobile applications, etc. You can directly use some of the datasets without even registering on the site. But some datasets require licensing agreements before downloading the dataset. 
4. The World Bank- The World Bank is a global development organization that provides open datasets. In the World Bank, you will find several resources for datasets such as DataBank, Open Data Catalog, Microdata library, etc.

So these are a few free public dataset portals from which you can download the dataset for your machine learning problem. Now you have finalized your machine learning problem and downloaded the dataset, it’s time to experiment with the data.

Always try to finish the project from one end to another end. I mean following all the steps- Data collection, cleaning, and preprocessing, Model building, tuning, and evaluation.

You can also take part in Kaggle competitions. Competitions will make you even more proficient in Machine Learning. These are some Kaggle Competitions, in which you should participate-

1. Titanic: Machine Learning from Disaster– Start with this competition. This Competition is good for the beginner.
2. Predict Future Sales– This challenge serves as a final project for the “How to win a data science competition” Coursera course.
3. House Prices: Advanced Regression Techniques– This is another beginner-friendly competition for those who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition. 
4. Digit Recognizer– This competition is for those who have knowledge of R or Python and machine learning basic, but are new to computer vision.

The list is long, for more Kaggle Competitions, you can check here.

Good Job!

Congratulations! You have learned machine learning Online Free by yourself. But at this stage, most people do one mistake and they stop learning and practicing. But in machine learning, there is much more to learn. For eg- deep learning, computer vision, natural language processing, etc.

So I would say never stop learning. This is not the end, this is your beginning in the world of machine learning. I hope you will continue your learning 🙂

Conclusion

In this article, you have discovered, “How to Learn Machine Learning Online Free?” along with free resources, machine learning project ideas, and free public datasets. I hope you have found this article helpful for your machine learning journey. If you have any doubts or queries feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

Thank YOU!

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Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford