Course Overview

Machine Learning Intro is your first step into predictive modeling and intelligent systems. This course is designed for people who already understand data cleaning, pandas and basic visualization, and now want to learn how to build models that make predictions or discover patterns.

You will implement classical machine learning algorithms using scikit-learn, understand how they work mathematically (at intuitive level), train models, evaluate them properly and avoid the most common beginner mistakes.

The course covers supervised learning (regression & classification), model validation techniques, overfitting/underfitting, simple ensemble methods and a gentle introduction to neural networks with PyTorch / TensorFlow (very basic level).

By the end you will be able to build, tune and correctly interpret ML models on real tabular and simple image/text datasets — and you will know what to learn next.

What You'll Learn

Understand how machine learning really works under the hood

Build regression and classification models with scikit-learn

Use proper validation strategies (train/test split, cross-validation)

Evaluate models correctly using metrics for regression and classification

Recognize and fight overfitting — the #1 beginner mistake

Take first steps with neural networks and deep learning libraries

Course Curriculum

Module 1: Introduction to Machine Learning

What is Machine Learning? Types of ML (supervised / unsupervised / reinforcement)
ML vs Rule-based systems vs Deep Learning
The ML Workflow: data → model → prediction → evaluation
Setting up the environment: scikit-learn, Jupyter, optional PyTorch/TF
Realistic expectations & common myths in 2026

Module 2: Regression — Predicting Numbers

Linear Regression — math & intuition
Gradient Descent and learning rate (visual explanation)
Polynomial regression & underfitting/overfitting
Regularization: Ridge, Lasso, ElasticNet
Evaluation metrics: MSE, RMSE, MAE, R²

Module 3: Classification — Predicting Categories

Logistic Regression & sigmoid function
Decision Trees — how they split & why they overfit
Random Forest — first ensemble method
Evaluation metrics: accuracy, precision, recall, F1, ROC-AUC, confusion matrix
Class imbalance — what to do

Module 4: Model Validation & Hyperparameter Tuning

Train / Validation / Test split — why 3 sets?
Cross-validation (K-Fold, Stratified)
Overfitting vs Underfitting — learning curves
Grid Search & Random Search
Feature importance & basic feature selection

Module 5: First Steps with Neural Networks

Perceptron → Multi-Layer Perceptron
Activation functions, backpropagation (intuition)
Simple feed-forward network in PyTorch / Keras
Basic image classification (MNIST example)
When to use neural nets vs classical ML in 2026

Module 6: Real-World ML & Next Steps

End-to-end project walkthrough (house prices or customer churn)
Common pitfalls in production ML
Ethics & bias in ML models
MLOps basics — what changes after Jupyter
Next topics: gradient boosting, NLP intro, computer vision intro, AutoML
Building ML portfolio in 2026

This Course Is For You If:

You finished basic data science course or know pandas & matplotlib
You want to start building predictive models
You are curious how ChatGPT-like models evolved from simple algorithms
You plan to move toward data scientist / ML engineer role
You want to understand — not just copy-paste — code

This Course May Not Be Ideal If:

You have zero Python / pandas experience
You want only deep learning & transformers from day 1
You expect to become ML engineer in 2 months
You already train XGBoost / LGBM / simple neural nets daily
You want guaranteed job after course

Prerequisites

Recommended before starting:

Python basics: variables, functions, lists, loops
pandas & numpy: reading data, filtering, groupby, basic calculations
Visualization: matplotlib/seaborn plots (hist, scatter, box)
High school math: linear equations, percentages, basic probability
Time commitment: 6–10 hours per week for 9–12 weeks

No advanced math or previous ML experience required — we explain concepts step by step.

Important: What This Course Does NOT Guarantee

This course is educational and provides foundational knowledge. We do NOT promise:

Job as ML engineer after completion
Ability to build production-grade systems immediately
Certification recognized by employers
Instant mastery of deep learning or LLMs

Real progress requires practice, personal projects, reading documentation and continuous learning.

Machine Learning Intro