There are different programming languages in the tech-sphere, but most preferred programming language for ML are Python and R.
Python is more recommended. Why? Because its popular, easy to learn and future-ready
With Python, you can switch domains easily too. Python offers popular frameworks like Django and Flask for backend development, Tkinter for GUI development, Pygames for Game development, etc.
If you go with Python, you must learn sklearn for Machine Learning. Sklearn is a modern machine learning library written in Python.
The best thing about sklearn is that most of the Machine learning algorithms are written for you already. It has a lot of useful classes for preprocessing your data for further analysis
You should also look into the Tensorflow module, which can help you build a neural network without many efforts.
Yes, there's maths here too, particularly linear algebra and statistics. But don't get scared, the maths required here is easier than you think. The math here is pretty much equivalent to math in high school.
This is essential because if you want to tune your models with maximum flexibility, you need to know how they work, and knowing linear algebra is a must for that!
One of the resources very helpful for revisiting linear algebra concepts is this pdf note.
Once you have some idea of using sklearn after learning python, you should start looking into how these machine learning algorithms work.
While using sklearn, an ML Algorithm is a black box written by the sklearn developers.
In order to get an idea of how these Machine learning algorithms work from within, look into:
- Gradient Descent
- Slope
- Supervised vs Unsupervised learning
- Reinforcement Learning
- Basic Linear Regression
- Working of all such similar models
- Clustering
An amazing resource to learn about all this is a book called “Hands-on ML with Scikit learn and tensorflow.”
Try to grab a copy of this book. It will help you a lot.
There are few other resources too that are worth looking into:
- Learn Numpy
- Learn Pandas
- All this will be helpful to debug the python/sklearn code
To host your machine learning models with a powerful backend, you will need to learn frameworks like Django and Flask.
Docker and Kubernetes can be of great help if you want to ship and deploy your models quickly.
Streamlit is worthy of looking into if you wish to build custom web apps for machine learning and data science.