<jupyter_start><jupyter_text>Part 1: Awake to your true powerit is all about avoiding brught force * chapter 1: ML is a database (avoid ML BF, you can answer varius kind of questions not only regression, you put schema ask questions, bayesian, what is ml, how i get into AI secondery school, etc.)* chapter 2: Optimization algorithms * chapter 3: The dauntless algorithms Part 2: Became a data hacker* chapter 1: Baysian modeling examples* chapter 2: Data analyics examples* chapter 3: Mixture models* chapter 4: Latent variable models* chapter 5: time series * chapter 6: the model zoo  Part 2.5: Became a problem solving master (optimization, meta-heuristics)  Part 3: Face your worst nightmares * chapter 1: Show me the money! (construct dataset from noisy heuriscs)* chapter 2: Learn with limited lables (simi-supervised)             Learn from synthetic* chapter 3: What we are talking about (explainable ML)<jupyter_code>### Part 4: Arm your self
* chapter 1: Neural networks architectures
* chapter 2: Techniques: Few shot learning
* chapter  : Imatation learning, reinfrocement learning
* chapter 3: NN index 
* chapter 4: Graph neural networks
### Part 5: Become a trickster
* chapter 1: Alpha zero
* chapter 2: Algorithm configuration
### To be considered
* chapter 4: Optimize with experimentation (Bandits, GP, TS, BNN, Tree based for speed, etc.)
* Applications: QU, learning2rank
* causality and econometrics
* bayesian none parametrics
* advances in certain fields
* knowladge graph, atomic, etc
* ML systems (docker, k8s, sagemaker, etc.)<jupyter_output><empty_output>
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