Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

This training is an introduction to the concept of machine learning, its algorithms and application using Python.

The training will include the following;

• What is Machine Learning? (Intro – why its used, Data Science defined)
• Analytics Defined (Predictive, Prescriptive etc.,)
• Data Mining Flow(Phases defined – with MOdeling phase that involves ML)
• Explanation on Data Set
• Supervised Learning
• Unsupervised Learning
• Classification Algorithms
• Regression Algorithms
• Linear Regression
• Logistic Regression
• Naive Bayes Classifier
• Anonymous Detection
• Decision Trees
• Random Forest
• Neural Networks
• K-Means Clustering
• Apriori algorithm
• Feature Selection
• Support Ventor Machine
• Basic explanation on Use Cases
• Basic Functions defines (Cost function, likelihood function, normalization, trade off etc.,)
• Primary tools/ Softwares used for ML
• Python Packages for Machine Learning