Data Science
Hands-on Data Science tutorials, guides and career insight from the team that teaches it.
207 articles
Probability for Machine Learning: Bayes Theorem, Distributions and Rules Explained (Updated August 2026)
Probability is the mathematical language of uncertainty — and machine learning is built on it. This guide covers probability types, addition and multiplication rules, distributions and Bayes Theorem with practical ML examples.
2 Aug 2026
Types of Statistics Explained: Descriptive vs Inferential with Examples (Updated August 2026)
There are two core types of statistics used in machine learning — descriptive and inferential. This guide explains both with practical examples, covering mean, skewness, hypothesis testing and ANOVA.
2 Aug 2026
What Is Statistics in Data Science? Population, Sample, Sampling Methods and Core Concepts Explained (Updated August 2026)
Statistics is the mathematical backbone of every machine learning algorithm. This guide covers what statistics is, its key terminologies, sampling techniques and why no ML career can skip it.
2 Aug 2026
Statistics and Probability for Machine Learning: Complete Beginner Guide (Updated August 2026)
Statistics and probability are the mathematical foundation every ML model rests on. Without them, you cannot understand why models fail, how confident a prediction is, or how to improve accuracy. This guide covers the essentials as taught in ABC's Proficient ML course.
2 Aug 2026
Machine Learning Clustering Algorithms Explained: K-Means, Hierarchical and DBSCAN Guide (Updated August 2026)
Clustering algorithms are the backbone of unsupervised machine learning. This guide covers K-Means, hierarchical clustering, DBSCAN and their real-world applications — explained the way our trainers teach it at ABC Trainings.
2 Aug 2026
Anomaly Detection in Machine Learning: Isolation Forest and One-Class SVM Guide
Learn anomaly detection in machine learning: how Isolation Forest and One-Class SVM work, when to use each, Python implementation with scikit-learn, and real applications in fraud detection, manufacturing, and IT ops in India.
1 Aug 2026
Machine Learning Algorithm Selection Guide: Which Algorithm to Use and When
A practical guide to choosing the right machine learning algorithm — classification vs regression vs clustering, Random Forest vs XGBoost vs SVM — with Python examples and guidance for Indian IT job seekers.
1 Aug 2026
How to Install Jupyter Notebook for Machine Learning: Step-by-Step Guide
Step-by-step guide to installing Jupyter Notebook for machine learning in 2026: from Python setup to launching in browser, creating notebooks, running code, and fixing common errors.
1 Aug 2026
Machine Learning Problem Solving Approach and Execution Time Explained
Understand the complete machine learning problem solving framework: how to manage execution time, reduce it with dimensionality reduction, and interpret your model results using SHAP and feature importance.
1 Aug 2026
Jupyter Notebook for Machine Learning: Complete Beginner's Guide
Learn what Jupyter Notebook is, why it is the standard tool for machine learning in India, and how to use its cell-by-cell workflow to train models, visualize data, and build your first ML project.
1 Aug 2026
Machine Learning Hardware Requirements: GPU, RAM and Compute Needs Explained (2026)
Machine learning and deep learning have real hardware needs. Understand what compute resources you actually need — GPU, RAM, storage — and how students can get started without expensive hardware.
31 Jul 2026
Machine Learning vs Deep Learning: Key Differences, Use Cases and Career Impact in 2026
Machine learning and deep learning are often confused. Understand the real technical differences, when to use each, and what career implications each path has in India in 2026.
31 Jul 2026