Unsupervised Learning for Data Analytics using Tensor flow course

Introduction

While machine learning has blown the minds of many, it comes with varios efficient components such as neural networks and Clustering hence efficient. This course is designed to help one understand unsupervised learning in ML.

Duration

5 days

Who should attend this course

  • Data analysts
  • Financial Analysts
  • Programmers
  • Business Administrators
COURSE LEVEL:

Register for the course


Face to Face Schedules By Location
Dubai Schedules:
Arusha Schedules:
Johannesburg Schedules:
Kampala Schedules:
Pretoria Schedules:
Cape Town Schedules:
Dar es Salaam Schedules:
Zanzibar Schedules:
Accra Schedules:
Nairobi Schedules:
Code Date Duration Location Fees
ULD08 4 Mar 2024 - 8 Mar 2024 5 days Nairobi, Kenya KES 75,000 | USD 1,100 Register
ULD08 1 Apr 2024 - 5 Apr 2024 5 days Nairobi, Kenya KES 75,000 | USD 1,100 Register
ULD08 6 May 2024 - 10 May 2024 5 days Nairobi, Kenya KES 75,000 | USD 1,100 Register
ULD08 3 Jun 2024 - 7 Jun 2024 5 days Nairobi, Kenya KES 75,000 | USD 1,100 Register
Kigali Schedules:
Mombasa Schedules:
Nakuru Schedules:
Kisumu Schedules:
Naivasha Schedules:
Virtual Trainer Led Schedules
Contact Us on (+254) 715 077 817 / (+254) 792 516 000 or email us [email protected] for a virtual schedule.
Code Date Duration Period Fees
E-Learning

Contact Us on (+254) 715 077 817 / (+254) 792 516 000 or email us [email protected] for E-Learning course.


Course 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

Course Outline

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

Course Administration Details:

METHODOLOGY

The instructor-led training is 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 Institute (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.

ACCOMMODATION AND AIRPORT PICKUP

Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer.

Email:[email protected].  

Mob: +254 715 077 817

TAILOR- MADE

This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location.

For further inquiries, please contact us on Tel: +254 715 077 817.

Mob: +254 792516000+254 792516010 or mail [email protected]

PAYMENT

Payment should be transferred to IRES account through bank before the course start date

Send proof of payment to [email protected]


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