Natural Language Processing (NLP)(15%)

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Essential Machine Learning(13%)

~Machine Learning and Python
~Supervised Learning: Classification and Regression
~Unsupervised Learning: Detecting Patterns
~Dimensionality Reduction
~Visualizing Data for Machine Learning

Reinforcement Learning (RL)(15%)

~Reinforcement Learning
~Bellman Equation and Dynamic Programming
~Monte Carlo (MC) Methods
~Temporal Difference Learning
~Multi-Armed Bandit(MAB) Problem

Deep Learning(17%)

~TensorFlow and its Functionalities
~Deep Learning with TensorFlow on the Cloud
~TensorFlow for Mobile and IoT
~Life Cycle of Model Creation
~Deep Learning with KERAS
~Activation Functions
~Confusion Matrix
~Underfitting and Overfitting

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