How to learn Machine Learning from scratch? A step-by-step guide.

If you are new to Bioinformatics and want to learn Machine Learning (ML), here’s how you kickstart your own learning process!?

A roadmap to learn ML from the very beginning! Here's a suggested plan that includes learning Linux, Python, and basic math:

Step 1: Learn Linux

Install a Linux distribution such as Ubuntu or Fedora on a virtual machine or on your computer. You can find plenty of tutorials on how to do this online. If you are using a latest version of Windows, there is an option to activate linux environment inside your windows. Try a simple search on the internet on using Windows Subsystem Linux or WSL.

Familiarize yourself with basic Linux commands such as ls, cd, mkdir, and chmod. There is a whole world of resources and tutorials online for learning linux. Start with simple youtube videos from freecodecamp or other channels. You can find online tutorials or even take free courses on sites like Udemy or Coursera.

Learn how to use the Linux package manager to install software packages. The package manager will make it easier for you to install Python and other necessary tools for ML.

Step 2: Learn Python

Start with learning the basics of Python such as data types, operators, loops, and functions. You can use online courses or textbooks to get started.

Learn how to use Python libraries such as NumPy, Pandas, and Matplotlib. These libraries are essential for data manipulation, visualization, and analysis.

Learn how to use Jupyter notebooks to write and run Python code. Jupyter notebooks provide an interactive environment that allows you to run Python code, write notes, and visualize data in the same document.

Step 3: Learn Basic Math

Refresh your knowledge of linear algebra, calculus, and probability theory. These topics will help you understand the algorithms and concepts behind ML.

You cannot avoid Mathematics if you want to learn Machine Learning. However, if you are allergic to calculus and afraid of differential equations, try to build a strong theoretical foundation in statistical concepts like correlation, regression, tests of significance, hypothesis testing, clustering, etc. This should be a good start but as you progress further into more complex topics like model development, simulation, classification and prediction, you must seriously consider putting in solid hours refreshing your math.

Learn how to use math libraries in Python such as SciPy and NumPy to perform basic mathematical operations.

Step 4: Learn Machine Learning

Start by learning the fundamental concepts of ML such as supervised learning, unsupervised learning, and reinforcement learning. You can use online courses, textbooks, or MOOCs to get started.

Learn how to implement ML algorithms in Python using libraries such as Scikit-Learn and TensorFlow. These libraries will allow you to create models and evaluate their performance.

Learn how to evaluate ML models and choose the appropriate metrics to assess their performance. You will need to learn concepts such as precision, recall, and F1 score.

Practice by working on real-world ML problems such as predicting housing prices or classifying images. You can find plenty of datasets and projects online to get started.

Step 5: Keep Learning and Practicing

Keep up with the latest developments in ML by reading research papers, blogs, and attending conferences.

Join ML communities such as Kaggle or GitHub to collaborate with other practitioners and learn from their projects.

Continuously practice and work on new projects to improve your skills and knowledge.

Remember, understanding Machine Learning is a continuous process, so don't get discouraged if you face challenges along the way. With patience, practice, and dedication, you can become a proficient machine learning practitioner. Good luck!