Certified Machine Learning Professional (CMLP)
Course Overview
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. Candidates will also 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.
Course Fee: $200
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Participants will get a clear understanding of:
• Machine Learning concepts and future scope.
• Deep learning fundamentals.
• Supervised and Unsupervised learning.
• Designing Machine Learning algorithms.
• IT Professionals
• Software Developers
• Process Managers
• Project Managers
• Data Analysis Professionals
• Web Developers
• Application Developers
• Showcase your expertise.
• Prove your Machine Learning skills and understanding.
• Implement your skills in your organization.
• Scale up your career.
• Become a part of this industrial revolution.
• There are no pre-requisites for getting this certification.
• Basic knowledge of programming and statistics will be beneficial.
• 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.
Examination Syllabus
Section 1
• 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
◦ 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
Section 2
• Understanding of customer dissatisfaction
• Recognizing the reason behind poor customer satisfaction
• Preventing Accidents from happening
• Advice for Applying Machine Learning
• Recognizing the reason behind poor customer satisfaction
• Preventing Accidents from happening
• Advice for Applying Machine Learning
Section 3
• 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
◦ 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
Section 4
• 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
• Focus On The Business Problem
◦ Bringing data silos together
◦ Avoiding troubles to occur
◦ Getting the focus of customers
• Machine Learning for Business
Section 5
• Determining the skill that you need
• Getting educated
• IBM-Recommended Resources
• Getting educated
• IBM-Recommended Resources
Section 6
• Applying Machine Learning To Patient Health
• Leveraging IoT to create more predictable outcomes
• Proactively Responding To IT Issues
• Protecting against fraud
• Leveraging IoT to create more predictable outcomes
• Proactively Responding To IT Issues
• Protecting against fraud
Section 7
Ten Predictions On The Future Machine Learning
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