Machine Learning

Course Features

Course Details





MACHINE LEARNING TRAINING



Validate your skills as an MACHINE LEARNING expert

Machine learning certification is rated as the most valued IT Certification. Enrol Today!

We designed this cloud architect certification training for anyone seeking to learn the major components of Amazon Web Services (AWS). By the end of the course, you'll be prepared to pass the associate-level AWS Certified Solutions Architect certification exam. The AWS certification is a must-have for any IT professional, and an AWS certified solution architects take home about $120,000 per year.

This course emphasizes AWS cloud best practices and recommended design patterns to help you think through the process of architecting optimal IT solutions on AWS. Case studies throughout the course showcase how some AWS customers have designed their infrastructures and the strategies and services they implemented.

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Training Features

Instructor-led Sessions

36 Hours of Online Live Instructor-Led Classes. Weekend Class : 12 sessions of 3 hours each. Weekday Class : 18 sessions of 2 hours each.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Machine Learning services.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Assignments

Live project based on any of the selected use cases, involving implementation of the various Machine Learning services.

Certification

Towards the end of the course, you will be working on a project. myTectra certifies you as an Machine Learning Expert based on the project.

Course Outline



Chapter 1:Installation and configuration

Chapter 2:Data Preprocessing

Chapter 3:Regression Techniques

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Linear Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Model Performance

Chapter 4:Classification Techniques

  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naïve Bayes Classification
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classification Model Performance

Chapter 5:Natural Language Processing (NLP)

  • Basic of NLP
  • Language preprocessing Techniques
  • Auto summarizing the given text document

Chapter 6:Clustering Techniques

  • K-Means Clustering
  • K-mini Batch Clustering
  • Hierarchical Clustering

Chapter 7:Elbow Method

Chapter 8:Curve Smoothening Techniques

Chapter 9:Association Rule Learning

Chapter 10:Reinforcement Learning

Chapter 11:Basics of Numpy and panda

Chapter 12:Deep Learning

  • Basics/what is Deep Learning

Chapter 13:Artificial Neural Networks

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)

Chapter 14:Statistics Basics

  • Standard Deviation
  • Variance
  • Co-Variance
  • T-distribution
  • Pearson Correlation Coefficient (PCC)/ Correlation Coefficient

Chapter 15:Model Selection



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More About ML


What is Machine Learning?

Machine Learning is a new trending field these days and is an application of artificial intelligence. It uses certain statistical algorithms to make computers work

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How Machine Learning


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