Data Science
Hands-on Data Science tutorials, guides and career insight from the team that teaches it.
248 articles
Random Forest Algorithm Explained: How It Works vs Decision Trees (2026)
Random Forest builds hundreds of decision trees and averages their predictions — learn how bagging and feature randomness eliminate overfitting, and how to tune it in Python at ABC Trainings Pune and Chhatrapati Sambhajinagar.
3 Aug 2026
Machine Learning Decision Tree Tutorial for Beginners — How It Works (2026)
Decision trees are one of the most intuitive machine learning algorithms — learn how they work, splitting criteria, overfitting prevention, and how to implement them in Python at ABC Trainings Pune and Chhatrapati Sambhajinagar.
3 Aug 2026
Logistic Regression Explained: Sigmoid Function, Binary Classification and When to Use It (Updated August 2026)
Logistic regression is the standard starting point for classification in machine learning. This guide explains the sigmoid function, binary vs multinomial types, key assumptions, maximum likelihood estimation and when to choose logistic regression over other classifiers.
2 Aug 2026
Linear Regression in Machine Learning: How It Works, Formula and Step-by-Step Examples (Updated August 2026)
Linear regression is the most fundamental supervised machine learning algorithm. This guide explains the formula, slope, intercept, best fit line, least squares method and how to evaluate your model with RMSE and R-squared.
2 Aug 2026
Regression Algorithm in Machine Learning: Types, Use Cases and How to Choose (Updated August 2026)
Regression algorithms predict continuous numerical values by modelling the relationship between dependent and independent variables. This guide covers all major regression types, key terminology and how to select the right algorithm for your dataset.
2 Aug 2026
Inferential Statistics: Hypothesis Testing, Confidence Intervals and ANOVA Explained (Updated August 2026)
Inferential statistics lets you draw conclusions about a population from sample data. This guide covers hypothesis testing, p-values, confidence intervals, regression analysis and ANOVA — the tools behind every serious ML experiment.
2 Aug 2026
Supervised Learning in Machine Learning: How It Works, Types and Key Algorithms (Updated August 2026)
Supervised learning is the starting point for most real-world ML projects. This guide explains labeled datasets, features vs labels, the difference between regression and classification, and which algorithms to learn first.
2 Aug 2026
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