Python Machine Learning

Python Machine Learning

§ Learn how to implement Python functions for machine learning and code and implement algorithms to predict future data.

Machine Learning and Predictive Analytics

§ Machine Learning gives computers the ability to learn without being explicitly programmed. Machine Learning algorithms can learn from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of a wealth of available data and 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.

Data Exploration and Preprocessing

§ The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are the steps to perform in order to get the data sets in the right shape, to enable Machine Learning algorithms to record trends and predict future data. Python functions are pre-programmed algorithms, that help programmers and makes data exploration and preprocessing relatively easy.

Feature Engineering

§ By injecting domain knowledge in the process, attributes are extracted from the data and how to encode and engineer them into features that make Machine Learning algorithms work.

Supervised Learning

§ In supervised learning, the “training data” consist of a set of “training” samples of data that is associated with a desired output label. Supervised learning algorithms learn a desired output from the training data and make a prediction on new, unseen data. Supervised learning has 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. ( clustering ). Examples of applications include social network analysis, customer segmentation or product recommendation.

Machine Learning Evaluation

§ Understand how well our algorithms are performing and compare the performances of different algorithms, by using the evaluation metrics. Error analysis and model introspection, “debug” and improve Machine Learning algorithms.