Intermediate Level 👇

Quick Remember, going into ML is a lifelong learning commitment - you are going to learn new things time after time no matter how far you are in your career.

Getting start with machine learning the first thing is to study math as much as you can !

• Mathematics for Machine Learning Specialization

• The essence of calculus

• Essence of linear algebra

• Multivariable Calculus

• Intro to Inferential Statistics

Questions about Machine Learning?

• How do I choose which attributes of my data to include in the model?

• How do I choose which model to use?

• How do I optimize this model for best performance?

• How do I ensure that I'm building a model that will generalize to unseen data?

• Can I estimate how well my model is likely to perform on unseen data?

To learn theory:

• Machine Learning

• Machine Learning with Python

Hands On:

• Hands-On Machine Learning with Scikit-Learn Book

• Applied Machine Learning

Advanced statistics :

• Think Bayes

• Think Stats

• Practical Statistics for Data Scientists

last but not least everything you need to know about statistics feel free to go ahead to this Amazing YouTupe chanel BrandonFoltz

Time Series Analysis :

• fbprohet

• playlist

• Case-study

Feature Engineering :

• kaggle

• Article

Join Data Science Communities :

• *******

• *******

Instagram Pages :

• *******

• *******

Reddit communities :

• r/datascience

• r/learnmachinelearning

• r/MachineLearning

Twitter Accounts :

• Andrew Ng

• KDnuggets

• Yann LeCun

Useful Links :

• Kaggle Competitions

• The Top 15 Places to Find Datasets 📊

• Deploying Machine Learning Models in Production

Next:

In the upcomming years i will update the roadmap with the Advanced Level

Happy learning.