Data Science
Hands-on Data Science tutorials, guides and career insight from the team that teaches it.
241 articles
Blender 3D for Engineers: Free Complete Course 2026 -- Modeling, Animation, CAD Import and AI Tools
Blender is 100% free and packs everything engineers need -- photorealistic rendering, mechanical animation, and AI-scripted 3D generation. This comprehensive guide is grounded in ABC Trainings' 8-episode Blender + AI course taught in Pune and Sambhajinagar, covering interface navigation, mesh modeling, SolidWorks/CATIA import, and using ChatGPT to write Python scripts that generate 3D parts automatically.
8 Aug 2026
AI Basics for Beginners: Complete Course Guide 2026 — Understanding Artificial Intelligence, Machine Learning and Career Paths in India
Build your AI foundation with ABC Trainings' free YouTube series — taught by Amit Kulkarni — covering what AI is, how machine learning works, neural networks, NLP, generative AI tools like ChatGPT and practical career paths in India. AI literacy is now a baseline requirement for every engineering and IT role in Pune and across India in 2026.
7 Aug 2026
Data Science & Analytics Full Course 2026: Statistics to Machine Learning — Complete Free Series
Free Data Science & Analytics course from ABC Trainings — 15 episodes covering statistical foundations, data management, EDA, Python analytics, machine learning, NLP, time series, and career building.
6 Aug 2026
Q-Learning Reinforcement Learning Tutorial for Beginners (2026)
Q-Learning is the foundational reinforcement learning algorithm that trains an agent to maximise rewards by building a Q-table — learn how it works, the Bellman equation, exploration vs exploitation, and how ABC Trainings teaches AI and reinforcement learning in Pune.
3 Aug 2026
Naïve Bayes Classifier in Machine Learning: Concepts and Applications (2026)
Naïve Bayes is one of the fastest and most accurate text classification algorithms — learn Bayes' Theorem, the independence assumption, Gaussian vs Multinomial vs Bernoulli types, and real-world applications at ABC Trainings Pune and Chhatrapati Sambhajinagar.
3 Aug 2026
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