• 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 feature 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
"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
The Python Machine Learning syllabus is designed to help analysts, researchers, BI experts and developers becoming familiar with the implementation of Machine Learning solutions, through the
use of tools in the Python programming language ecosystem.
Using a mix of frontal presentation and interactive examples, the course provides a comparison between Supervised and Unsupervised Learning, and offers an overview on core algorithms for predictive analytics, tackling tasks such as classification, clustering, regression analysis and dimensionality reduction. Notions of Neural Networks and latest developments in Deep Learning are also discussed.
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.
• 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
• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting
• 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
• Clustering: grouping similar items
• Algorithms for clustering: k-Means, Hierarchical Clustering and DBSCAN
• Dimensionality Reduction
• Algorithms for dimensionality reduction: Principal Component Analysis
• Evaluation metrics for machine learning
• Planning an evaluation campaign on your data
• 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
Classroom or Remote
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