Machine Learning
In this tutorial, you will find comprehensive guides on Machine Learning concepts,
algorithms, and real-world applications.
Machine Learning
In this tutorial, you will find comprehensive guides on Machine Learning concepts, algorithms, and real-world applications.
Regression
Regression is a statistical technique that inspects the relationship between two or more variables: dependent and independent variables.
Below you will get list of regression algorithm along with the project in Python Programming Language.
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.
Below you will get list of classification algorithm along with the project in Python Programming Language.
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
Mean Shift clustering
DBSCAN clustering
How to evaluate Clustering algorithms
Association rule
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Introduction to Assoiciation rule
Apriori Algorithm
Eclat Algorithm
Reinforcement Learning
In this section you will learn about the reinforcement learning and what are the technical concepts present inside it with their practical implementation
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
Some Advance Concepts
In this you will learn about the advance concepts used by the Machine learning Engineer
Hyperparameter tunning
Imbalanced classification
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Time Series Analysis
Interview Questions and Supplementary
In this you will find the interview question related to the Machine learning Engineer Role and some supplementary materials.