Machine Learning for Beginners

Machine Learning for Beginners on Breni is a hands-on path for building models that learn from data, using Python and scikit-learn. It has 8 courses: concepts, the maths behind training, data preparation, regression, classification, clustering, neural networks and machine learning in practice. By the last course you can train a model, measure it on data it has not seen, and spot overfitting and data leakage. Basic Python helps, and the maths course sits early so that every algorithm after it, unlocked in turn, makes sense.

• 8 courses
• 15 learning objectives
• Level: beginner
• Language: English
• Price: Subscription, free trial

What you will be able to do

• Prepare a dataset for training by scaling numbers and encoding categories
• Train regression and classification models with scikit-learn
• Evaluate a model with a confusion matrix, precision and recall
• Group unlabelled data with k-means and reduce features with PCA
• Train a small neural network and compare it with a simpler model
• Tune a model with cross-validation while avoiding data leakage

Full curriculum

Part 1 · Foundations

1. Machine Learning Concepts: From Data to Models

Explain what machine learning is and how a typical project runs from data to model. Units, in order: Supervised, unsupervised and reinforcement learning; Features and labels; Training, validation and test data; The machine learning workflow; When machine learning is the wrong tool.

• — Core Types of Learning
  Learn supervised, unsupervised, and reinforcement learning basics.
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• — Features and Labels
  Understand inputs and outputs in machine learning.
  • What Are Features
  • What Are Labels
  • How They Work Together
• — Data Splits
  Split data into training, validation, and test sets.
  • Training Data
  • Validation Data
  • Test Data
• — The ML Workflow
  Follow steps from raw data to a working model.
  • Data Collection and Cleaning
  • Training and Evaluation
  • Deployment and Monitoring
• — When ML Fails
  Identify problems where machine learning is the wrong tool.
  • Simple Rules Work Better
  • Lack of Data
  • Need for Exact Logic

2. Maths for Machine Learning

Use the core maths ideas that explain how models train. Units, in order: Vectors and matrices as tables of numbers; Slopes and gradients; Probability basics; Loss functions: measuring how wrong a model is; Gradient descent step by step.

3. Preparing Data for Models

Turn a raw dataset into model-ready features with numpy, pandas and scikit-learn. Units, in order: numpy arrays and pandas tables; Scaling numeric features; Encoding categories as numbers; Dealing with missing values; Train and test splits in scikit-learn.

Part 2 · Core algorithms

4. Regression Models

Predict a number from data and measure how far off the predictions are. Units, in order: Simple linear regression; Regression with many features; Polynomial features for curved patterns; Regularisation to keep models simple; MAE and RMSE.

5. Classification Models

Predict a category and judge a classifier by more than accuracy. Units, in order: Logistic regression; k-nearest neighbours; Decision trees; Random forests; Confusion matrix, precision and recall.

6. Unsupervised Learning

Find structure in data that has no labels. Units, in order: k-means clustering; Choosing the number of clusters; Principal component analysis; Finding anomalies.

Part 3 · Going further

7. Neural Networks and Deep Learning

Build and train a small neural network and know when deep learning is worth it. Units, in order: Neurons, layers and activation functions; How backpropagation adjusts weights; Training a small network in Keras; Convolutional networks for images; Deep learning compared with simpler models.

8. Machine Learning in Practice

Make a model reliable enough to use outside a notebook. Units, in order: Cross-validation; Hyperparameter tuning; Data leakage and how to prevent it; Fairness and bias checks; Serving a model behind a simple API.

Sample: Machine Learning Concepts: From Data to Models

The opening tier, (Core Types of Learning). Learn supervised, unsupervised, and reinforcement learning basics.

Supervised Learning

Learn models using input data paired with correct labels.

Unsupervised Learning

Find hidden patterns in data without any labels.

Reinforcement Learning

Train agents using rewards and punishments for actions.

Questions about Machine Learning for Beginners

Do I need to know Python before this machine learning course?

Basic Python helps a lot. If you are new to it, take Python for Beginners on Breni first, since this path uses Python, pandas and scikit-learn throughout.

How much maths do I need for machine learning?

Less than most people expect at this level. The second course teaches the vectors, gradients and probability the rest of the path relies on, with pictures before formulas.

What is the difference between machine learning and AI?

Machine learning is the part of AI where models learn patterns from data. AI for Beginners explains AI in general, while this path is about building and testing the models yourself.

How long does it take to learn machine learning?

It depends on your starting point and how often you practise. Each lesson takes about three minutes, so the 8 courses fit around other commitments.

Is the certificate a professional credential?

No. Completing the path earns a Breni certificate of completion, which shows you finished the 8 courses.

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