Categorization of Machine Learning Algorithms

Rohit Prasain — Sat, 16 Nov 2024 21:58:15 +0000

Throughout our academic or tech career, we have encountered different AI and ML algorithms. Terms such as Supervised Learning, Reinforcement Learning, K-Means algorithm, K-Nearest Neighbor algorithm, DBSCAN algorithm, etc. repeatedly show up. However, we often fail to organize all these terms into a cohesive framework.

This article organizes and categorizes most of the widely used machine learning algorithms.

Artificial Intelligence (AI) has six subsets:

Machine Learning
Natural Language Processing
Deep Learning
Robotics
Speech Recognition
Expert Systems

Machine Learning (ML)

it is one of the subsets of AI
consists of 4 types

Supervised learning
Unsupervised learning
Reinforcement learning
Semi-Supervised learning

Each of these learning types use various algorithms and they are listed below.

1. Supervised Learning

used in Classification and Regression problems.

Under Classification domain, some popularly used algorithms are:

i. Logistic regression algorithm
ii. Naive Bayes algorithm
iii. K-Nearest Neighbor algorithm
iv. Support Vector Machine
v. Decision Tree
vi. Random Forest
vii. Gradient Boosting Machines (XGBoost, LightGBM)
viii. Neural Network algorithms

Under Regression domain, some popularly used algorithms are:

i. Linear Regression
ii. Polynomial Regression
iii. Support Vector Machine
iv. Decision Tree
v. Random Forest
vi. Gradient boosting

Note: Some algorithms like Decision tree, Random forest, Support Vector and Gradient boosting can be used for both classification and regression problems.

2. Unsupervised Learning

used in Clustering, Dimensionality Reduction and Association Rule Learning problems.

Under Clustering domain, some popular algorithms are:

i. K-Means algorithm
ii. Hierarchal clustering
iii. DBSCAN algorithm
iv. Gaussian Mixture Models
v. Mean Shift

Under Dimensionality Reduction domain, some popular algorithms are:

i. Principal Component Analysis (PCA)
ii. Autoencoders
iii. tSNE algorithm
iv. UMAP algorithm

Under Association Rule Learning domain, some popular algorithms are:

i. Apriori algorithm
ii. FPGrowth algorithm

3. Semi-Supervised Learning

some of the popular algorithms under this learning are:

i. Self-training algorithm
ii. Mixture Models
iii. Graph based methods
iv. Transductive support vector machines

4. Reinforcement Learning

consists of four methods:

a. Valued based method
b. Policy based method
c. Model based method
d. Actor-Critic method

a. Value based method

consists of following algorithms:

i. Q Learning
ii. Deep Q-Networks
iii. Double DQN
iv. Dueling DQN

b. Policy based method

consists of following algorithms:

i. Reinforce algorithm
ii. Deterministic policy gradient

c. Model based method

consists of following algorithms:

i. Model predictive control
ii. Monte Carlo tree search

d. Actor-Critic method

consists of following algorithms:

i. Advantage actor-critic (A2C)
ii. Asynchronous advantage actor-critic (A3C)
iii. Soft actor-critic (SAC)

Visualizing the AI/ML terminologies in a broader picture helps to better understand the algorithms and its use cases.

Basics for backend development - Roadmap

Rohit Prasain — Thu, 05 Sep 2024 10:01:16 +0000

Every web development enthusiast trying to learn backend development often get stuck on what to learn as a beginner. They tend to get confused about the project to build and backend concepts to implement on that project.

Having worked as backend developer for two years and guided half a dozen fresh interns, I have a roadmap for you. From my experience, to start the backend engineering journey, initially focus on building endpoints, no more than that.

learn basic commands of git
create GitHub repository for your project
have an adequate grasp on a backend programming language
search for the popular and market demanding framework for the language
do not think of difficult projects, just start with simple TODO app and build upon it (this is where most beginners get stuck)
create REST endpoints for CRUD operations in Todo app without connecting to database first.
test your endpoints in postman
now connect your app with database and perform CRUD operations. Start with any database, do not think much (I recommend PostgreSQL)
find a way to deploy your project online (up and running app gives you a good vibes)

Now you have built APIs and it is deployed (amazing feeling).But do not stop here.

Learn more backend concepts and after learning any one concept, TRY implementing that on your TODO app.

You can further explore the followings:

database design concepts like ER diagrams, relationships, normalization and crow foot notation.
HTTP status codes.
concepts of authentication and authorization(ROLE-BASED).
learn about cookie, session and token (inclusively REFRESH TOKEN).
try uploading files and images into your server.
learn and implement the concepts of database migrations, transactions, seeders and factories.

After implementing any above points, please DEPLOY your project (it boosts your confidence)

Implement the above concepts as many as you can. If you cannot implement any of these concepts at the moment, know about the concept and move on to implementing another point, don't get stuck.

Note: Please keep in mind, you are starting and you are not supposed to know everything in this phase.

Everything I mentioned above in a picture:

Forem: Rohit Prasain

Categorization of Machine Learning Algorithms

Basics for backend development - Roadmap