Advanced Machine
Learning

Learn from Industry experts

Project : SWATCLOUD

Machine Learning Advanced 

Lesson 1: Introduction to Machine Learning
 Introduction to Big Data and Machine Learning

Lesson 2: Walking with Python or R
 Understanding Python or R

Lesson 3: Machine Learning Techniques
 Types of Learning
 Supervised Learning
 Unsupervised Learning
 Advice for Applying Machine Learning
 Machine Learning System Design

Lesson 4: Supervised Learning
 Regression
 Classification

Lesson 5: Supervised Learning - Regression
 Predicting house prices: A case study in Regression
 Linear Regression & Logistic: A Model-Based Approach
 Regression fundamentals : Data and Models

Lesson 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

Lesson 7: Unsupervised Learning
 Clustering
 Recommendation
 Deep Learning

Lesson 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

Lesson 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

Lesson 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

Lesson 11: Spark Core and MLLib
 Spark Core
 Spark Architecture
 Working with RDDs
 Machine learning with Spark – Mllib