Navigating my way around Machine Learning

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The plan for the next few weeks is to take a deep dive into machine learning. After scouring the internet on how to proceed with this idea, the first steps are getting familiar with statistics, then basics of python and then the more complex machine learning algorithms.  

 So first off, its time to list my handy friends in this process:

 1) Think Stats: Probability and Statistics for Programmers
Book by Allen B. Downey

2) Data Camp - Data Scientist Track

3) Udemy - Machine Learning A-Z

4) github.com - to post my repository of codes

5) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Book by Aurélien Géron

It feels quite intimidating because I feel like I lack the bigger picture of things when it comes to how statistics and machine learning fit together. Having studied Statistical learning and analysis in school, I know I do have the statistical foundation for this but at the same time, I know that there are gaps in what I have learnt so far. I know the statistical theories, but I don't know how exactly does it all come together in a model. That is the key learning objective for this exercise. To have a cohesive knowledge of what I learnt and how do I use that in building machine learning models. 
I am hoping to have a mix of everything on this blog, the code, the stat and the mental tug of war of war it took to understand them. 


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