MACHINELEARNING Course
Machine Learning Tutorial
Learn machine learning from fundamentals to building predictive models and neural networks.
Master ML: statistics, algorithms, data preprocessing, supervised/unsupervised learning, deep learning, and practical projects.
This is the AI basics path for learners who want to understand how models learn from data. You will start with datasets, features, training, testing, and evaluation before moving into common algorithms.
What you will build and understand
You will prepare data, split it safely, train simple models, compare metrics, and explain when a model should not be trusted.
- Understand model training
- Prepare data safely
- Evaluate predictions
- Explain AI limitations
Beginner mistakes to avoid
- Training and testing on the same data.
- Chasing high accuracy without checking bias or imbalance.
- Using AI output without human review.
Who this course is for
Structured Course Path
Follow this roadmap from basics to projects. Every topic includes a code example, output preview, FAQ, and tool integration.
Foundations
Machine Learning Introduction
6 min - beginner
Start lesson
Types of ML
7 min - beginner
Start lesson
ML Workflow
8 min - beginner
Start lesson
Tools and Libraries
6 min - beginner
Start lesson
NumPy Basics
7 min - beginner
Start lesson
Pandas Basics
8 min - beginner
Start lesson
Matplotlib Introduction
6 min - beginner
Start lesson
Statistics Basics
Probability Fundamentals
7 min - beginner
Start lesson
Probability Distributions
8 min - beginner
Start lesson
Descriptive Statistics
6 min - beginner
Start lesson
Hypothesis Testing
7 min - beginner
Start lesson
Correlation and Causation
8 min - beginner
Start lesson
Regression Basics
6 min - beginner
Start lesson
Statistical Tests
7 min - beginner
Start lesson
Data Preprocessing
Data Collection
10 min - intermediate
Start lesson
Data Cleaning
8 min - intermediate
Start lesson
Handling Missing Data
9 min - intermediate
Start lesson
Outlier Detection
10 min - intermediate
Start lesson
Normalization and Scaling
8 min - intermediate
Start lesson
Feature Engineering
9 min - intermediate
Start lesson
Train-Test Split
10 min - intermediate
Start lesson
Class Imbalance
8 min - intermediate
Start lesson
Supervised Learning
Regression Models
9 min - intermediate
Start lesson
Linear Regression
10 min - intermediate
Start lesson
Classification
8 min - intermediate
Start lesson
Logistic Regression
9 min - intermediate
Start lesson
Decision Trees
10 min - intermediate
Start lesson
Random Forest
8 min - intermediate
Start lesson
Support Vector Machines
9 min - intermediate
Start lesson
Naive Bayes
10 min - intermediate
Start lesson
Unsupervised Learning
Clustering
10 min - advanced
Start lesson
K-Means Clustering
11 min - advanced
Start lesson
Hierarchical Clustering
12 min - advanced
Start lesson
DBSCAN
10 min - advanced
Start lesson
Dimensionality Reduction
11 min - advanced
Start lesson
PCA
12 min - advanced
Start lesson
Anomaly Detection
10 min - advanced
Start lesson
Association Rules
11 min - advanced
Start lesson
Deep Learning Basics
Neural Networks
12 min - advanced
Start lesson
Perceptron
10 min - advanced
Start lesson
Activation Functions
11 min - advanced
Start lesson
Backpropagation
12 min - advanced
Start lesson
TensorFlow Introduction
10 min - advanced
Start lesson
Keras API
11 min - advanced
Start lesson
Convolutional Neural Networks
12 min - advanced
Start lesson
Recurrent Neural Networks
10 min - advanced
Start lesson