ML Outline

Machine learning is a type of artificial intelligence that allows computers to automatically learn from data without being explicitly programmed. It involves creating mathematical models that can analyze large amounts of data to identify patterns and make predictions or decisions. Machine learning has become increasingly popular in recent years due to its ability to solve complex problems across a variety of industries, including finance, healthcare, and marketing.

Types of Machine Learning

There are 4 types of Machine Learning

1. Supervised ML

2. Unsupervised ML

3. Semi-Supervised ML

4. Reinforcement ML

Under Supervised Machine Learning, there are further 2 kinds:

1. Regression

2. Classification

In Regression there are a few ML Algorithms which are;

1. Linear Regression

2. Polynomial Regression

3. Ridge Regression

4. Lasso Regression

5. Elastic Net Regression

6. Support Vector Regression

7. Decision Tree Regression

8. Random Forest Regression

9. Gradient Boosting Regression

10. XGBoost Regression

In Classification there are a few ML Algorithms which are;

1. Logistic Regression

2. k-Nearest Neighbors (k-NN)

3. Support Vector Machines (SVM)

4. Decision Trees

5. Random Forest Classifier

6. Naive Bayes

7. Gradient Boosting Classifier

8. XGBoost Classifier

9. Neural Networks

10. AdaBoost Classifier

Under Unsupervised Machine Learning, there are further 2 kinds:

1. Clustering

2. Dimensionality Reduction

A few ML Algorithms under Clustering are;

1. K-Means Clustering

2. Hierarchical Clustering

3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

4. Mean Shift Clustering

5. Spectral Clustering

6. Gaussian Mixture Model (GMM) Clustering

A few Algorithms under Dimensionality Reduction are;

1. Principal Component Analysis (PCA)

2. t-Distributed Stochastic Neighbor Embedding (t-SNE)

3. Autoencoders

4. Independent Component Analysis (ICA)

5. Factor Analysis (FA)

6. Linear Discriminant Analysis (LDA)

7. Non-negative Matrix Factorization (NMF)

Unlike Supervised and Unsupervised ML techniques, Semi-Supervised ML is not divided into any sub-categories

However, a few of the Algorithm techniques used under Semi-Supervised ML Techniques are;

1. Label Propagation

2. Co-Training

3. Self-Training

4. Generative Models

5. Semi-Supervised Deep Learning

Unlike Supervised and Unsupervised ML techniques, Reinforcement ML is not divided into any sub-categories

However, a few of the Algorithm techniques used under Reinforcement ML Techniques are;

1. Q-Learning

2. Deep Q-Networks (DQNs)

3. Policy Gradient Methods

4. Actor-Critic Methods

5. Monte Carlo Tree Search (MCTS)

6. Proximal Policy Optimization (PPO)

I hope you all will have got a brief understanding of Machine Learning and how to move further in this domain!

"Technology is anything that wasn't around when you were born." - Alan Kay

Do subscribe and keep supporting! 😊