Roadmap to Learn Machine Learning FREE in 2025

First, let's understand what is machine learning and why you need to learn machine learning (ML)?

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

ML algorithms allow machines to learn in the same way a human learns.

And Machine learning models learn from training data or from self-experiences.

You can consider the machine learning model as a "Newborn child".

This child learns from his parent's instructions or by his self-experiences. He tried 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.

There are a lot of data available in today's world. We are living in the Data Age. 

This Data is generated not only by human 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? 

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?

That's why Machine learning is very popular and powerful. 

With the help of Machine Learning algorithms, we can find ...

Many people are shifting their careers into the ML field. And the future of Machine Learning is very bright.

Right?

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.

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

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

1. Understand Prerequisites
2. Learning
3. Practicing

What are topics mandatory for ML? 

1. Programming Language
   ML is all about implementation. And if you don't have programming knowledge, you can't implement anything. The most popular programming languages (for ML) are Python, R, Java, and C++. As a beginner, you can start with Python.
   Python and R are the most wanted languages for ML engineers. R is good for statistical operations.
2. Mathematics
   Knowledge of Mathematics is very important in order to understand how ML and its algorithms work. 
   In math, the most important topics are:
   
   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 ML. 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 ML like Types of ML algorithms
   - Supervised Learning
   - Unsupervised Learning
   - Semi-Supervised Learning
   - Reinforcement 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 ML 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.

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 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. Linear Algebra and Optimization for Machine Learning

The next important prerequisite for ML is ML algorithms knowledge. You can learn the ML basics and its algorithms with these Free online resources:

Free ML 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 ML:

1. Machine Learning with Python– Great Learning
2. Machine Learning Tutorial Python– codebasics
3. Python Machine Learning Tutorial- Programming with Mosh
4. Machine Learning by Krish Naik

Next, the most important step is "Practicing"

Now it's time to get your hands dirty and start practicing.

The first and most important thing is to be comfortable with ML tools - Jupyter and Anaconda.

Spend your few hours and play with these tools, you can use these tutorials:

Once you are comfortable with  Jupyter and Anaconda. , then you need to learn ML 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 ML 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, you can work on these ML 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.
    

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.

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!