Machine Learning Online Training

Machine Learning Course Content :

 1.Introduction to Machine Learning

  • Introduction to Big Data and Machine Learning

2. Walking with Python or R

  • Understanding Python or R

3.Machine Learning Techniques

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

4.Supervised Learning

  • Regression
  • Classification

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

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

7.Unsupervised Learning

  • Clustering
  • Recommendation
  • Deep Learning

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
  • Unsupervised Learning – Recommendation

9.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

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

11.Spark Core and MLLib

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

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