Python Machine Learning training course

Machine Learning and Predictive Analytics

• Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine Learning algorithms can learn from, and make predictions on data.
• With the wealth of data available today, companies can take advantage of Machine
• Learning techniques to gain actionable insights and ultimately improve their business.
• Using scikit-learn, the core Machine Learning library for Python, attendees will learn how to implement Machine Learning systems to perform predictions on their data.
• We will also examine and use tools and frameworks such as the Open Source TensorFlow.

Data Exploration and Pre-processing

• Data sets come in all sorts of formats and flavours. The first part of a Machine Learning project is understanding the data and the problem at hand.
• Data cleaning, data transformation, and in general data pre-processing are the steps to perform in order to get the data sets in the right shape, so Machine Learning algorithms can learn from them.
• Python makes data exploration and pre-processing relatively easy.

Feature Engineering

• By injecting domain knowledge in the process, attendees will learn how to extract attributes from the data and how to encode them into features that make Machine Learning algorithms work.
• One of the core aspects of applied Machine Learning, feature engineering is difficult and time consuming.
• The quality and quantity of features can have a great impact on how Machine Learning algorithms can work.

Supervised Learning

• In supervised learning, the training data consist of a set of training samples associated with a desired output label. Supervised learning algorithms can learn the desired output from the training data, and make a prediction on new, unseen data.
• We'll approach supervised learning from two different directions: classification, the task of predicting a category, and regression, the task of predicting a quantity.
• Examples of applications include price prediction, spam detection and sentiment analysis.

Unsupervised Learning

• In unsupervised learning, the training data is not labelled. Unsupervised learning algorithms analyse the data and find hidden structures within the data.
• We'll approach unsupervised learning in particular from the point of view of a clustering application.
• Examples of applications include social network analysis, customer segmentation or product recommendation.

Machine Learning Evolution

• Using the proper evaluation metrics, we can understand how well our algorithms are performing and we can compare the performances of different algorithms.
• Attendees will learn about error analysis and model introspection, in order to “debug” and improve Machine Learning algorithms

Neural Networks & Deep Learning

• Introduction to Artificial Neural Networks, a family of algorithms applicable to many Machine Learning problems, and relevant mathematical concepts. Discussion on Neural Network concepts like gradient descend, activation functions, loss functions and hyper-parameters.