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Machine Learning

Non Certification Program

Schedules Play Course Overview

Overview


This course provides advanced-level training on Machine Learning applications and algorithms. It will give you hands-on experience in multiple, highly sought-after machine learning skills in both supervised and unsupervised learning. This machine learning training ensures you can apply machine learning algorithms like regression, clustering, classification, and recommendation.

Course Curriculum


Module 1: Introduction to Machine Learning

  • Introduction to Big Data and Machine Learning

Module 2: Walking with Python or R

  • Understanding Python or R

Module 3: Machine Learning Techniques

  • Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Advice for Applying Machine Learning
  • Machine Learning System Design

Module 4: Supervised Learning

  • Regression
  • Classification

Module 5: Supervised Learning - Regression

  • Predicting house prices: A case study in Regression
  • Linear Regression & Logistic: A Model-Based Approach
  • Regression fundamentals : Data and Models
  • Feature selection in Model building
  • Evaluating over fitting via training/test split
  • Training/ Test curves
  • Adding other features
  • Regression ML block diagram

Module 6: Supervised Learning - Classification

  • Analyzing the sentiment of reviews: A case study in Classification
  • Classification fundamentals : Data and Models
  • Understanding Decision Trees and Naive Bayes
  • Feature selection in Model building
  • Linear classifiers
  • Decision boundaries
  • Training and evaluating a classifier
  • False positives, false negatives, and confusion matrices
  • Classification ML block diagram

Module 7: Unsupervised Learning

  • Clustering
  • Recommendation
  • Deep Learning

Module 8: Unsupervised Learning - Clustering

  • Document retrieval: A case study in clustering and measuring similarity
  • Clustering System Overview
  • Clustering fundamentals : Data and Models
  • Feature selection in Model building
  • Prioritizing important words with tf-idf
  • Clustering and similarity ML block diagram

Module 9: Unsupervised Learning - Recommendation

  • Recommending Products
  • Recommender systems overview
  • Collaborative filtering
  • Understanding Collaborative Filtering and Support Vector Machine
  • Effect of popular items
  • Normalizing co-occurrence matrices and leveraging purchase histories
  • The matrix completion task
  • Recommendations from known user/item features
  • Recommender systems ML block diagram

Module 10: Unsupervised Learning – Deep Learning

  • Deep Learning: Searching for Images
  • Searching for images: A case study in deep learning
  • Learning very non-linear features with neural networks
  • Application of deep learning to computer vision
  • Deep learning performance
  • Demo of deep learning model on ImageNet data
  • Deep learning ML block diagram

Module 11: Spark Core and MLLib

  • Spark Core
  • Spark Architecture
  • Working with RDDs
  • Machine learning with Spark – Mllib

 

Prerequisite


Participants in this Machine Learning online course should have:

  • Familiarity with the fundamentals of Python programming 
  • Basic high school mathematics
  • Understanding of the basics of statistics

The course covers concepts of mathematics & statistics required for machine learning and we will provide you with a free Python course when you purchase our Machine Learning course. 

Certification


On successful completion of the training program, participants will receive the certificate of participation.

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