How do I study data science and machine learning?

Question

Could someone tell me how do I study data science and machine-learning?

Answer 1

There are many online resources available on the internet in the form of blogs and tutorials to start learning Data Science and Machine Learning. While there may be numerous places with excellent materials on certain topics, I would suggest picking a complete tutorial that covers all modules in a proper sequence for systematic and hassle-free learning. 

While going through the individual topics, you can then access these other sources and materials for comprehensive study. This way, not only will you be studying in an orderly manner, but you will also be able to learn each topic in detail from various sources and experts. 

Here is a full playlist on Data Science that has been created by Intellipaat for beginners. The tutorials teach everything from a complete Data Science introduction to learning all about Machine Learning. 

There is also this Data Science tutorial and Machine Learning tutorial for those who prefer reading material. 

As much as these blogs and tutorials cover everything in detail, it is always better to train under an expert at some point as they are more experienced in the field. With self-learning, there is always a high possibility of missing out on crucial lessons and tips. Plus, these professionals can also share their helpful hacks from their experiences as well as clarify any queries that you may have.

Study under the industry experts and proper guidance of Intellipaat’s seasoned trainers. You can check out the full Data Science online course and Machine Learning course before enrolling. 

Answer 2

Here’s a more efficient and academically sound schedule which incorporates Self Learning alongside taking the usual courses to ensure that you don’t lag behind anyone. Machine Learning is a subfield of Data Science if I refer to the two as the same.

Mathematics & Statistics: Firstly, learn probability, statistics, and linear algebra as they are fundamental for data analysis as well as designing algorithms. Other relevant data sources can include Intellipaat and StatQuest.

Programming: Python should be a sought out programming language as well and take full advantage of popular libraries such as Pandas (data manipulation), NumPy (numerical operations) and Matplotlib/Seaborn (visualisation). DataCamp or even freeCodeCamp allow people to learn more interactively.

Data Science Basics: As for data once again it is highly critical to enhance ones skills when it comes to Data Preparation, EDA and Visualisation. In this way ideally you are mastering datasets that you aim to work on. Kaggle projects can be ideal fitting your learning purpose.

Machine Learning Algorithms: When familiar with Databases starts with regression as well as Decision trees and Clustering algorithms and supervised and unsupervised algorithms. Andrew Ng’s Coursera ML course is a great fit and Hands-On Machine Learning book is also crucial.

Deep Learning: Once ML is mastered one can now effectively target insightful tools such as TensorFlow or Pytorch for Neural Networks

Projects: Start building portfolio with proper real communication projects on Kaggle or Git hub. Your skills will speak volumes for you.

Stay Updated: Blogs, research papers or online Medium and LinkedIn communities can help you in securing a more updated version of yourself. 

I guarantee that this region promises to be structured and working for a promising mastering of Data Science as well as ML.