Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.31 GB | Duration: 3h 18m
Learn to create Machine Learning Algorithms in Python from Data Science experts. Machine Learning simplified with python
What you’ll learn
The students will learn What is Machine Learning. Its Intro – why its used, Data Science defined), Analytics Defined (Predictive, Prescriptive etc.,), Data Mining Flow(Phases defined – with Modeling phase that involves ML).
Also Learn the explanation on Data Set Supervised Learning, Unsupervised Learning, Classification Algorithms, Regression Algorithms.
Learn about Linear Regression, Logistic Regression, Naive Bayes Classifier, Anonymous Detection, Decision Trees, Random Forest, Neural Networks, K-Means Clustering Apriori algorithm.
Learn Feature Selection, Support Ventor Machine, Basic explanation on Use Cases Basic Functions defines (Cost function, likelihood function, normalization, trade off etc.,)
Learn Primary tools/ Softwares used for ML Python Packages for Machine Learning
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
The target customers for this course are anyone who wants to learn about data and analytics, Data Engineers, Analysts, Architects, Software Engineers, IT operations and Technical managers. There is as such no Pre-Requisites. No prior knowledge of machine learning required. Basic knowledge of Python will be an added advantage.
Who this course is for:
Anyone who wants to learn about data and analytics. Highly recommended for Data Engineers, Analysts, Architects, Software Engineers, IT operations and Technical managers
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