Machine Learning Fundamentals

As the use of machine learning algorithms becomes popular for solving problems in a number of industries, so does the development of new tools for optimizing the process of programming such algorithms.

This course aims to explain the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the difference between supervised and unsupervised models, as well as by applying algorithms to real-life datasets, this course will help beginners to start programming machine learning algorithms.

Što ćete naučiti


Kome je namijenjeno

  • This course is perfect for beginners in the field of machine learning. No prior knowledge of the use of scikit-learn or machine learning algorithms is required.


  • The students must have prior knowledge and experience of Python programming.

Nastavni plan

  • Introduction to scikit-learn
    • scikit-learn
    • Data Representation
    • Data Preprocessing
    • scikit-learn API
    • Supervised and Unsupervised Learning
  • Unsupervised Learning: Real-life Applications
    • Clustering
    • Exploring a Dataset: Wholesale Customers Dataset
    • Data Visualization
    • k-means Algorithm
    • Mean-Shift Algorithm
    • DBSCAN Algorithm
    • Evaluating the Performance of Clusters
  • Supervised Learning: Key Steps
    • Model Validation and Testing
    • Evaluation Metrics
    • Error Analysis
  • Supervised Learning Algorithms: Predict Annual Income
    • Exploring the Dataset
    • Naïve Bayes Algorithm
    • Decision Tree Algorithm
    • Support Vector Machine Algorithm
    • Error Analysis
  • Artificial Neural Networks: Predict Annual Income
    • Artificial Neural Networks
    • Applying an Artificial Neural Network
    • Performance Analysis
  • Building your own Program
    • Program Definition
    • Saving and Loading a Trained Model
    • Interacting with a Trained Model