can anyone guide me for machine learning roadmap?
Answers
Answer:
1. Learn a Programming Language
It's unnecessary to explain why learning a programming language is an absolute must for anyone wanting to start a career in ML. But while many programming languages provide frameworks to work with ML, Python and R have the richest and most constantly growing ecosystems. Python and R have been competing as the primary languages for ML for a while, but Python has been gaining momentum and is currently the most used language in ML. Since it has an easier syntax and a smoother learning curve, we recommend Python as a starting language.
1. It's easy to find Python courses on the internet. If you're willing to pay, one of the best for beginners is the course offered by DataCamp.
2. Codecademy also offers a friendly introductory course, but again, you will have to pay if you want to access more advanced topics.
3. Learn Python offers a thorough course, and the best part is that it's free.
4. We also highly recommend the book Learn Python the Hard Way:
It's important to note that even though Python is the most broadly adopted language (and in my opinion should be the first language you learn), R is another very good asset to have. It's particularly powerful for exploratory analysis and data visualization, but it also has strong frameworks for ML and Statistics. DataCamp and Leada offer great courses on R.
2. Learn and Apply the Basics
Now that you know basic theory, it's time to apply your knowledge. The best way to do this is to use existing datasets from well known use cases. Python sklearn framework provides a datasets package with many “toy” datasets that can be used for training and learning purposes. R also provides a package with many famous datasets that can be used for training. Another good place to find datasets for many different problems and domains is Kaggle.
The main advantage of applying your knowledge in these training environments is that, while you're creating a machine learning tool from the ground up, you still don’t have to worry about data handling, which can be a very time-consuming step, letting you focus more on technique. Since most of these datasets are famous, it's also easy to find internet examples that can provide some guidance on how to get started.