Machine learning
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Regression
Regression is a statistical technique that inspects the relationship between two or more variables: dependent and independent variables.
Introduction to Regression ✔✔✔
Linear Regression
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Multiple Linear Regression ✔✔✔
Polynomial Regression
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Lasso Regression
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Generalized Linear Regression ✔✔✔
Bayesian Regression
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Step wise Regression ✔✔✔
How to evaluate Regression model ✔✔✔
Classification
Classification comes under the category of supervised learning i.e it learns from a given set of inputs and makes predictions on unseen data.
Introduction to Classification
Decision Tree
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Multilayer perceptron classifier
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Gaussian Process
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Stochastic Gradient Descent ✔✔✔
Clustering
Clustering means bunching similar items together. It means to keep similar points in one group and dissimilar points in different groups.
Introduction to clustering
Hierarchical clustering
How to evaluate Clustering algorithms
Dimensionality reduction
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Association rule
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Introduction to Assoiciation rule
Apriori Algorithm
Eclat Algorithm
Reinforcement Learning
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Introduction to Reinforcement learning
Basics of Markov Decision Processes
Exploration vs Exploitation Trade-off
Exploration vs Exploitation Trade-off
Bellman Equation and Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning
Function Approximation
Deep Q-Learning
(DQN)
Introduction to Policy Gradient Methods
Advanced Exploration Strategies
Deep Deterministic Policy Gradient
Proximal Policy Optimization
Multi-Agent Reinforcement Learning
Inverse Reinforcement Learning
Hierarchical Reinforcement Learning
Meta-Learning in Reinforcement Learning
Safe Reinforcement Learning
Advance Machine learning concepts
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Hyperparameter tunning
Imbalanced classification
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Imbalanced classification
Interview Questions
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