Objectives

By the end of the course one should be able to:

• Use tensorflow for Machine learning
• Work with supervised learning for ML
• Do data mining using the K-means, The Hierarchal Based Method and Density-Based Clustering

Module 1: Introduction to machine learning and Tensor flow

• What is TensorFlow?
• TensorFlow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
• Tensorflow Basic Operations
• Working with missing data and categorical data in Tensor flow
• Applications of Machine Learning
• Supervised vs Unsupervised Learning
• Python libraries suitable for Machine Learning
• Logistic Regression with Tensor Flow

Module 2: Introduction to Supervised and Unsupervised learning

• Understanding linear and logistic regression
• Linear Discriminant Analysis
• Working with K-Nearest Neighbors
• Working with Decision trees and Support Vectors Machine
• What is cluster analysis and its applications

Module 3: The K-mean clustering method of data mining

• Initialization of the K-mean clustering method
• Work with K-mean as coordinate descent
• Clustering text data with K-means
• Smart initialization via k-means++
• Assessing the quality and choosing the number of clusters
• The general MapReduce motivation, abstraction, execution overview and combiners
• Understand Mapreduce in K-mean
• K-means on the Geyser’s Eruptions Segmentations
• K-means on image processing
• The elbow algorithm
• Silhouette Analysis
• The drawbacks of K-means

Module 4: The Hierarchal Based Method of data mining

• Agglomerative clustering
• Understanding Divisive clustering
• Understand the Multiphase Hierarchal Clustering
• Elbow method of evaluation
• Silhouette Analysis evaluation method
• Hierarcal Based clustering on the Geyser’s Eruptions Segmentations
• Hierarcal Based clustering on image processing

Module 5: Density-Based Clustering Data mining method

• Work with the DBSCAN Algorithm
• Understand the Optic based Algorithm
• Work with the DENCLUE Algorithm
• Evaluation using the Elbow method
• Silhouette Analysis evaluation method
• Density-Based Clustering on the Geyser’s Eruptions Segmentations
• Density-Based Clustering on image processing

REQUIREMENTS

Participants should be reasonably proficient in English. Applicants must live up to Indepth Research Services (IRES) admission criteria.

METHODOLOGY

The instructor led trainings are delivered using a blended learning approach and comprises of presentations, guided sessions of practical exercise, web based tutorials and group work. Our facilitators are seasoned industry experts with years of experience, working as professional and trainers in these fields.

All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.

ACCREDITATION

Upon successful completion of this training, participants will be issued with an Indepth Research Services (IRES) certificate certified by the National Industrial Training Authority (NITA).

TRAINING VENUE

The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments and lunch.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.