How to move properly in ML?
Rustam ZokirovBefore taking a deep dive into an amazing world of Machine Learning, most learners like me, like us, need to have a clear plan or so-called "roadmap" that directs us through our journey and aids in following a concrete plan.
Personally, I do not recommend and do not follow the idea of being in a "hurry" and jumping from one topic to another in learning ML. However, that does not mean being slow and giving yourself delays.
While I was learning what you are going to learn right now, I have had similar questions, intentions, and confusion of course. You should understand why you should learn Machine Learning and find out the main differences between Data Science, Machine Learning, and Artificial Intelligence. Machine Learning is making computers do things that we’ve never made computers do before and what about others? Do you see? These are crucial before starting out, OK?
Do you know how to study?
- First, learn to learn, and for more information, you can refer to the following sources since the information and tips given here are really helpful
Sources:
Thinking of Self-Studying Machine Learning? Remind yourself of these 6 things
- Personally, I like this picture
Do you read books on ML?
Well, the answer could be "sometimes" or something else. However, books and even documentations are definitely helpful. (I have experienced this :) ) Just, read some of the pages, and you can see how this is different and even better than video tutorials. I highly recommend you to read the book that is named as
You can get as much significant knowledge from this book as I could whenever I was reading it. You can build and structure your own path based on your interests, which is great!
A little spoiler from the Roadmap of the book:
What Machine Learning is, what problems it tries to solve, and the main categories and fundamental concepts of its systems
The steps in a typical Machine Learning project
Learning by fitting a model to data
Optimizing a cost function
Handling, cleaning, and preparing data
Selecting and engineering features
Selecting a model and tuning hyperparameters using cross-validation
The challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance trade-off)
The most common learning algorithms: Linear and PolynomialRegression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods
Reducing the dimensionality of the training data to fight the “curse of dimensionality”
Other unsupervised learning techniques, including clustering, density estimation, and anomaly detection
Still cannot get what I mean or your main route? I think it can become a little easier with the following roadmap that I have organized, enjoy it!
Roadmap on GitHub: ML RoadMap
P.s If you have interested in learning more stuff and exploring other parts of python, consider looking at my GitHub repositories where I have detailed information and roadmaps in various paths.
I think I could give you some helpful information and you've spent your time wisely. When you have further questions, try to "google" it or find it on the internet. If you are really stuck at some point, you are welcome to ask me questions.
Thank you, guys 👍🏻
Keep learning and exploring 🚀