This training course provides an introduction to the core concepts of the Python language, ultimately focusing on Big Data Analytics and Machine Learning applications.
The first three days of the course introduce you to Python tools for data science, including how best to manipulate and visualise your data with Python's excellent library support.
The last two days move one step forward, providing an overview of Artificial Intelligence and Machine Learning with the purpose of implementing predictive analytics applications.
Practical exercises and interactive walk-throughs are used throughout, so attendees have the opportunity to apply the proposed concepts on real data science applications, from exploratory data analysis to predictive analytics.
Software developers and software engineers with a basic knowledge of Python. Data Scientists, Data analysts and Business Intelligence professionals who are new to Python.
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.
Working with data:
• 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
• 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
◦ Activation Functions
◦ Loss Functions
• Neural Networks in the Wild: examples of successful applications
• Deep Network Architectures
• Deep Learning Libraries
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