Machine Learning Academy

Name: Machine Learning Academy
Availability: InStock

PYTHONPythonAiData_science

Learn machine learning from first principles. Build and evaluate models with scikit-learn, master ensemble methods, and scale to deep learning with PyTorch.

🤖 AI-Powered📚 30 courses👥 100,000+ learners⭐ 4.9 rating

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Course Overview

Machine Learning with Python

Learn machine learning from first principles. Build and evaluate models with scikit-learn, master ensemble methods, and scale to deep learning with PyTorch. This track covers 30 progressive mini-courses from absolute beginner (A1) through advanced (B2), with short focused lessons and quick quizzes to lock in each concept.

What You Will Learn

You will start with the fundamentals and build up through intermediate and advanced topics, each course building on the last. Every lesson is practical, with real examples and an AI tutor available when you need help.

How It Works

Each course is broken into four focused lessons. Complete a few lessons a day and you will master the full track in weeks, not months.

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Tools

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How You'll Learn

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Interactive Lessons

Hands-on coding exercises with real-time feedback

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Built-in Editor

Write and run code directly in your browser

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Certificate

Earn a certificate when you complete the course

Curriculum

30 Courses

Every course in the Machine Learning Academy learning path.

What Is Machine Learning?

A14 lessons

Discover what machine learning is and how it differs from traditional programming. Explore the three core learning paradigms and see real-w…

Python for Machine Learning Setup

A14 lessonsPRO

Configure a professional ML development environment with Anaconda, Jupyter, and the core scientific Python stack. Learn to manipulate array…

Your First Model: Linear Regression

A14 lessonsPRO

Build intuition for supervised learning by training your first predictive model from scratch. Learn how linear regression finds the best-fi…

Classification Fundamentals

A14 lessonsPRO

Cross the boundary from predicting numbers to predicting categories. Explore logistic regression as your first classifier and learn how a s…

Train, Test, Split, and Evaluate

A14 lessonsPRO

Discover why evaluating a model on its own training data gives falsely optimistic results and how to prevent this with proper data splittin…

Data Preprocessing Pipeline

A24 lessonsPRO

Raw data is almost never ready for a model: values are missing, scales differ wildly, and categorical strings need encoding. This course gi…

K-Nearest Neighbors

A24 lessonsPRO

Learn one of the most intuitive classification and regression algorithms: predict the label of a new point by majority-vote among its k clo…

Decision Trees

A24 lessonsPRO

Decision trees split data by asking yes/no questions, building a flowchart that is easy to visualise and explain to non-technical stakehold…

Naive Bayes and Text Classification

A24 lessonsPRO

Naive Bayes applies Bayes' theorem with a strong independence assumption, making it blazing fast on high-dimensional text data. Build a spa…

Model Evaluation Metrics

A24 lessonsPRO

Accuracy alone is a misleading metric when classes are imbalanced or misclassification costs differ. Master the full evaluation toolkit: RO…

Random Forest and Ensemble Methods

B14 lessonsPRO

A single decision tree overfits; a forest of diverse trees generalises far better. Learn how random forests aggregate hundreds of trees tra…

Support Vector Machines

B14 lessonsPRO

SVMs find the maximum-margin hyperplane that separates classes, giving them strong theoretical generalisation guarantees. Learn the kernel…

Gradient Boosting: XGBoost and LightGBM

B14 lessonsPRO

Gradient boosting sequentially trains weak learners, each correcting the residual errors of the previous one, producing state-of-the-art pe…

Cross-Validation and Hyperparameter Tuning

B14 lessonsPRO

A single train-test split can be misleading due to random variation in the data partition. Cross-validation gives a more reliable estimate…

Feature Engineering

B14 lessonsPRO

The quality of your features determines the ceiling of your model's performance, regardless of which algorithm you use. Learn to create new…

Clustering: K-Means and DBSCAN

B14 lessonsPRO

Clustering is unsupervised learning that finds natural groupings in data without labels. Master K-Means for globular clusters and DBSCAN fo…

Dimensionality Reduction: PCA and t-SNE

B14 lessonsPRO

High-dimensional data is hard to visualise and often contains redundant features that slow training. PCA compresses information into orthog…

Pipelines in scikit-learn

B14 lessonsPRO

Scikit-learn Pipelines chain preprocessing and modelling steps into a single object that prevents data leakage and simplifies deployment. L…

Handling Imbalanced Data

B14 lessonsPRO

In fraud detection, medical diagnosis, and many other critical applications, one class may represent less than 1% of examples. Training on…

Model Persistence and Deployment Basics

B14 lessonsPRO

A model is only useful when it can make predictions on new data outside your Jupyter notebook. Learn to save and version scikit-learn pipel…

Neural Networks with PyTorch

B24 lessonsPRO

PyTorch is the dominant research and production deep-learning framework. Build your first feedforward neural network from tensors upward, u…

Training Neural Networks

B24 lessonsPRO

Good architecture is only half the battle; training dynamics determine whether the network converges to a useful solution. Master learning…

Convolutional Neural Networks

B24 lessonsPRO

CNNs exploit spatial structure in images through local weight sharing and pooling, reducing parameter count while improving translational i…

Recurrent Networks and LSTMs

B24 lessonsPRO

Sequential data like time series, text, and audio has temporal dependencies that standard feedforward networks ignore. RNNs process sequenc…

Transfer Learning

B24 lessonsPRO

Training large networks from scratch requires enormous datasets and compute. Transfer learning reuses weights from models pre-trained on mi…

NLP: Text Classification with BERT

B24 lessonsPRO

BERT's bidirectional transformer architecture understands context in both directions, outperforming older sequential models on nearly every…

MLOps Fundamentals

B24 lessonsPRO

MLOps bridges machine learning research and reliable production software engineering. Learn experiment tracking with MLflow, reproducible e…

Model Monitoring and Drift Detection

B24 lessonsPRO

Models degrade silently in production as the real world shifts away from training conditions. Learn to monitor prediction distributions, de…

Responsible AI and Explainability

B24 lessonsPRO

As ML systems make high-stakes decisions in hiring, lending, and healthcare, understanding and auditing model behaviour is both an ethical…

Capstone: End-to-End ML Project

B24 lessonsPRO

Bring together every skill from this track in one complete project: acquire and clean a real dataset, engineer features, train and tune mul…

FAQ

Frequently Asked Questions

Is the Machine Learning Academy course free?

Yes. You can start the Machine Learning Academy course for free and complete its interactive lessons at no cost. An optional PRO subscription unlocks advanced AI tools and a shareable certificate.

Do I need prior experience to learn PYTHON?

No. The course begins with the fundamentals and gradually moves to more advanced topics, so you can start even with no prior PYTHON experience.

How will I learn PYTHON on CoddyKit?

You learn by doing. Short interactive lessons pair a clear explanation with a hands-on coding exercise that runs in real time, and a 24/7 AI tutor gives personalized help whenever you get stuck.

Do I get a certificate for completing Machine Learning Academy?

Yes. PRO learners can take an exam and earn a shareable certificate of completion with a verifiable code for the Machine Learning Academy course.

Can I learn PYTHON on my phone?

Yes. CoddyKit is available on the web and as native iOS and Android apps, so you can learn PYTHON on any device and your progress syncs across them.

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