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Certified Machine Learning Professional (CMLP)

About Certification

GSDC's Certified Machine Learning Professional gives learners a broad introduction and all aspects of machine learning. Machine learning is everywhere nowadays because we are businesses wants the machine to learn to behave like human but in an efficient way.

After getting this qualification you will be able to use the probability concepts and statistical methods to design and build a core machine learning algorithm, Also candidates will learn two types of ML which are supervised and unsupervised.

Human beings are facing both types of ML algorithms every day like a spam filter in your mail or voice recognition from your Smartphone which comes under supervised ML. In unsupervised ML you will learn how ecommerce platform shows you products you want to buy.

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Objectives

Clear understanding of:

  • You will learn Machine Learning concepts and future scope.
  • Learn about deep learning fundamentals.
  • Learn Supervised and Unsupervised learning.
  • You will learn to design Machine Learning algorithms.

 

Target Audience

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IT Professionals

Software Developers

Process Managers

Project Managers

Data Analysis Professionals

Web Developers

Application Developers

 

Benefits

Showcase your expertise.

Prove your Machine Learning skills and understanding.

Implement your skills in your organization.

Scale up your career.

You will become a part of this industrial revolution.

 

Pre-requisites

  • There are no pre-requisites for getting this certification.
  • Basic knowledge of programming and statistics will be beneficial.
 

Examination

Multiple-choice exam of 40 marks.
You need to acquire 26+ marks to clear the exam.
In case the Participant failed then they will be free 2nd attempt.
Re-examination can be taken up to 30 days from the date of the 1st exam attempt.
 

Sample Certificate

Sample Certificate

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Exam Syllabus

1. Introduction to Machine Learning:

  • What is Machine Learning
    • Iterative learning from data
    • What s old is new again
  • Definition of Big Data
  • Big Data in Context with Machine Learning
  • The Need to Understand and Trust your Data
  • Hybrid Cloud And Its importance
  • Leveraging the Power of Machine Learning
    • Descriptive analytics
    • Predictive Analytics
  • When Statistics and Data Mining Teams Up with Machine Learning
  • Machine Learning in Context
  • Approaches towards Machine Learning
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Neural networks and deep learning
2. Applying Machine Learning to Business Needs:
  • Understanding of customer dissatisfaction
  • Recognizing the reason behind poor customer satisfaction
  • Preventing Accidents from happening
  • Advice for Applying Machine Learning

3.Looking Inside Machine Learning:

  • The Impact of Machine Learning on Applications
    • Algorithm s role
    • Categories of the machine learning algorithm
    • Training machine learning systems
  • Data Preparation
    • Identifying Relevant Data
    • Governing Data
  • The Machine Learning Cycle
  • Application Example: Photo OCR
4. Getting Started With Machine Learning
  • Understanding How Machine Learning Can Help
  • Focus On The Business Problem
    • Bringing data silos together
    • Avoiding troubles to occur
    • Getting the focus of customers
  • Machine Learning for Business
5. Learning Machine Skills
  • Determining the skill that you need
  • Getting educated
  • IBM-Recommended Resources
6. Using Machine Learning To Provide Solutions With Business Problems
  • Applying Machine Learning To Patient Health
  • Leveraging IoT to create more predictable outcomes
  • Proactively Responding To IT Issues
  • Protecting against fraud
7.Ten Predictions On The Future Machine Learning


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