ML Fundamentals | NetrAInsights

Level

Beginner

Duration

3 Weeks

Hands-On Labs

Format

Self-paced

What You'll Learn

Build a rock-solid foundation in machine learning. This course covers the essential algorithms, math, and practical skills that every ML practitioner needs — from data preprocessing to model deployment.

Math Foundations: Linear algebra, probability, statistics, calculus
Supervised Learning: Regression, classification, decision trees, SVMs
Unsupervised Learning: Clustering, PCA, anomaly detection
Model Evaluation: Cross-validation, metrics, bias-variance tradeoff
Feature Engineering: Encoding, scaling, selection, imputation
Ensemble Methods: Random forests, gradient boosting, XGBoost

Course Modules

📐 Week 1: Math, Data & Supervised Learning▼

Linear algebra refresher: vectors, matrices, operations
Probability and Bayes' theorem
Statistics: mean, variance, distributions
Gradient descent from scratch
Linear and logistic regression
Lab 1: Exploratory data analysis with pandas
Lab 2: Linear regression from scratch
Lab 3: Logistic regression for classification
Lab 4: Data preprocessing pipeline

🌳 Week 2: Tree Models & Feature Engineering▼

Decision trees — splitting criteria, depth, pruning
Random forests and bagging
Gradient boosting, XGBoost, LightGBM
Feature engineering (encoding, scaling)
Handling missing data and imbalanced datasets
Lab 5: Train decision tree and visualize splits
Lab 6: Random forest feature importance
Lab 7: XGBoost on tabular dataset
Lab 8: Feature engineering pipeline

🔍 Week 3: Evaluation, Unsupervised & Deployment▼

Cross-validation and model selection
Metrics: accuracy, precision, recall, AUC-ROC
K-Means, DBSCAN clustering
PCA and dimensionality reduction
Model serialization and deployment basics
Lab 9: K-fold cross-validation
Lab 10: K-Means clustering on customer data
Lab 11: PCA for visualization
Lab 12 (Capstone): End-to-end ML pipeline from EDA to deployment

Prerequisites

Python basics (variables, loops, functions)
High school math (algebra, basic statistics)
No ML experience required

Who Should Take This?

Beginners entering the AI/ML field
Software Developers pivoting to ML
Data Analysts leveling up to ML
Students in CS, math, or science programs

Tools & Tech Stack

Python, NumPy, pandas, Matplotlib, Seaborn
Scikit-learn, XGBoost, LightGBM
Jupyter Notebooks, Google Colab
Pickle, joblib for model persistence

Ready to Start?

Start your machine learning journey with a course designed for clarity, depth, and hands-on practice.

📧 Enroll Now

📊 ML Fundamentals