Machine learning is an area of artificial intelligence and computer science that covers topics such supervised learning and unsupervised learning and includes the development of software and algorithms that can make predictions based on data.
Programming knowledge is a must obviously.
Learn Python 🐍
- Python basics: variables, conditions, loops..etc..etc..etc
- But most importantly installing packages, importing, using.
- Writing methods and using decorators.
- Object-oriented Python: class, inheritance.
💡 The book automate the boring stuff is a good resource.
🔮 Articles and Youtube videos by Sentdex is awesome!
Install and setup stuffs 🚧
Things you need and need not.
- A 🖥️ running any OS ✔️
- A high-end GPU ❌
- Python installed directly or using anaconda/miniconda ✔️
- A heavy IDE like PyCharm or Spyder ⁉️
- Use light weight IDEs like VScode ✔️
💡 Make sure you integrated installed python with your IDE and intellisense works fine.
🌝 For god sake don’t use notepad to code!
Next steps with programming (recommended)
Do the following to take python learning to the next level
- Build a python project and opensource.
- Learn building a backend web app using flask/django/responder
ML algorithms and its Mathematics 🔢
Truth about the maths
You need basic knowledge in the following mathematics areas for ML
💡 Use your school/college textbooks to brush up on maths skills.
Use various resources on the internet to understand the concepts of following machine learning algorithms
- Linear regression
- Logistic regression.
- Kmeans and KNN.
- Neural networks.
- Deep neural networks.
And the list goes on…
💡 Check out this awesome machine learning tutorials repo.
🔮 Checkout d2l for learning algorithm implementation
Pro tip : Learn maths on the go. This means check out the maths part when you get stuck instead of going through maths first.
Implementing the algorithms 👩💻
This is an important part. Learning how to implement ML algorithms 💥
- Try to implement the ML algorithms with vanilla python and the following libraries.
Packages to use
- Numpy : For nuemerical computations
- Pandas : For data preproessing
- Matplotlib : For plotting the data
ML libraries and doing projects
Now you can learn an ML library and start doing projects in ML.
Various ML libraries
- Sklearn : ML library with classic machine learning algorithms
- Tensorflow & keras : DL libary with keras as high level API
- Pytorch : DL library built focusing on research.
and the list goes on…
💡 You can start with any of the libraries. I would recommend using official docs and tutorials to learn!
Youtube Playlists to learn ML