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It will give you hands-on experience in multiple, highly sought-after machine learning skills in both supervised and unsupervised learning. This instructor-led course provides advanced-level training on Machine Learning applications and algorithms. The unique case study approach ensures you are working hands-on with data while you learn. This machine learning training ensures you can apply machine learning algorithms like regression, clustering, classification, and recommendation. You’ll also receive training in deep learning and Spark Machine learning—skills which are in great demand today.

Objectives of the course :

After completing this course, you will be able to :

-Understand classification data and models
-Perform supervised learning techniques: linear and logistic regression
-Use machine learning with Spark
-Classify the types of learning including supervised and unsupervised
-Identify the various applications of machine learning algorithms
-Use unsupervised learning algorithms including deep learning, clustering, and recommendation systems

Who should take this course :

The work for this course will be performed in Python/R. We have an introduction to these languages as part of the course. This course is best suited for:

-Professionals working in eCommerce, search, and other online consumer based organizations
-Analytics professionals who want to work in machine learning or artificial intelligence
-Experienced professionals who would like to harness machine learning in their fields to get more insight about customers
-Graduates looking to build a career in Data Science and machine learning
-Data Science professionals who already have experience in R or Python
-Software professionals looking for a career switch into the field of analytics

Prerequisites: This course will be performed in python / R hence we have an introduction to these languages in the program itself.

Course starts from 24th July 2017

Lesson 1: Introduction to Machine Learning
Lesson 2: Walking with Python or R
Lesson 3: Machine Learning Techniques
Lesson 4: Supervised Learning
Lesson 5: Supervised Learning - Regression
Lesson 6: Supervised Learning - Classification
Lesson 7: Unsupervised Learning
Lesson 8: Unsupervised Learning - Clustering
Lesson 9: Unsupervised Learning - Recommendation
Lesson 10: Unsupervised Learning – Deep Learning
Lesson 11: Spark Core and MLLib

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  • 13,999.00
  • 365 Days
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