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.
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.
Lesson 1: Introduction to scikit-learn
• scikit-learn
• Data Representation
• Data Preprocessing
• scikit-learn API
• Supervised and Unsupervised Learning
Lesson 2: 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
Lesson 3: Supervised Learning: Key Steps
• Model Validation and Testing
• Evaluation Metrics
• Error Analysis
Lesson 4: Supervised Learning Algorithms: Predict Annual Income
• Exploring the Dataset
• Naïve Bayes Algorithm
• Decision Tree Algorithm
• Support Vector Machine Algorithm
• Error Analysis
Lesson 5: Artificial Neural Networks: Predict Annual Income
• Artificial Neural Networks
• Applying an Artificial Neural Network
• Performance Analysis
Lesson 6: Building your own Program
• Program Definition
• Saving and Loading a Trained Model
• Interacting with a Trained Mode