MACHINE LEARNING

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OBJECTIVE 

This course introduces several fundamental concepts and methods for machine learning including how to discover patterns in the user data and then make predictions, and intricate patterns for answering  business questions to solve business problems.  

EXIT PROFILE 

Knowledge in Machine Learning 

Knowledge Artificial Intelligence 

Data Analysis using Algorithms 

Knowledge in Python Programming 

CAREER PATH 

Programmer in AI 

Programmer in Machine Learning 

Data Analyst 

FACULTY SKILL SET 

Programmer in AI 

Knowledge in Machine Learning 

Knowledge in Data Analysis 

Knowledge Data structure & algorithms  

Knowledge in Python 

HARDWARE AND SOFTWARE REQUIREMENTS 

Operating System: Windows 7/8/10  

Minimum Memory: 1 GB  

Recommended Memory: 4 GB / 8 GB  

Minimum Disk Space: 160 GB  

Recommended Disk Space: 1 TB 

PREREQUISITE FOR STUDENTS  

Before attending this course, students must have:  

Knowledge in programming will be an added advantage 

Basic Knowledge in Data structure & algorithms  

COURSE OUTLINE 

Introduction to Machine Learning 

Linear regression; SSE; gradient descent; closed form; normal equations 

Classification problems; decision boundaries; nearest neighbor methods 

Probability and classification, Bayes optimal decisions 

Naive Bayes and Gaussian class-conditional distribution 

Linear classifiers 

Bayes’ Rule and Naive Bayes Model

Logistic regression, online gradient descent, Neural Networks Decision tree and Mid-term 

Ensemble methods: Bagging, random forests, boosting 

Unsupervised learning: clustering, k-means, hierarchical agglomeration Text representations; naive Bayes and multinomial models; clustering and latent space models 

Vector machines (SVM) 

Course Features

  • Lectures 70
  • Quizzes 0
  • Duration 72 hours
  • Skill level All levels
  • Language English
  • Students 0
  • Assessments Yes

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