Brief Introduction
This training is an introduction to the concept of machine learning, its algorithms and application using Python.Description
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
Requirements
- Requirements
- No prior knowledge of machine learning required
- Basic knowledge of Python
EDU CBA
3.7 Raiting
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