Highlights
• Learn about framing a business application as a Machine Learning task
• Understand the role of labelled data, data cleaning and data transformation in Machine Learning systems
• Explore engineering techniques to extract useful attributes from your data
• Implement supervised and unsupervised learning algorithms using Python
• Evaluate the quality of your models, using evaluation metrics, model introspection and error analysis
• Understand the concepts of Deep Learning & Neural networks
Course Details
Overview
• What is Artificial Intelligence? What's up with the hype?
• Data Science vs. Data Mining vs. Machine Learning
• Machine Learning Problems and Applications
• Python Environment Set-up with Anaconda Python
◦ Jupyter Notebooks
◦ Python Ecosystem for Data Science and Machine Learning
Machine Learning Overview
• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting
Supervised Learning Problems
• Classification: predicting a label
• Algorithms for classification: k-Nearest Neighbours, Support Vector Machine and Naive Bayes
• Regression: predicting a quantity
• Algorithms for regression: Linear Regression and Polynomial Regression
Unsupervised Learning Problems
• Clustering: grouping similar items
• Algorithms for clustering: k-Means, Hierarchical Clustering and DBSCAN
• Dimensionality Reduction
• Algorithms for dimensionality reduction: Principal Component Analysis
Evaluation of Machine Learning algorithms
• Evaluation metrics for machine learning
• Planning an evaluation campaign on your data
Deep Learning & Neural Network Overview
• Intro to Artificial Neural Networks
• Neural Network concepts
◦ Neural Network Types
◦ Gradient Descend
◦ Back-propagation
◦ Activation Functions
◦ Loss Functions
◦ Hyper-parameters
• Neural Networks in the Wild: examples of successful applications
• Deep Network Architectures
• Deep Learning Libraries
Who should attend
Developers, engineers, researchers and analysts who want to start learning about Artificial Intelligence and related concepts, including Data Science, Data Mining, Machine Learning and Deep Learning.
Some background in Mathematics (e.g. Statistics and Probability, Linear Algebra, Calculus, etc) will be beneficial, but not strictly required.
Feedback
4.8 out of 5 average
"The course was professionally run and I liked that it is interactive with exercises of how AI is used. The instructor is very knowledgeable on the subject and enthusiastic about machine learning"
YZ, Software developer, Python AI & ML, May 2021
