Certified Machine Learning Professional (CMLP)

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