Topic outline

  • Objectives

    • Master essential machine learning concepts and techniques.
    • Develop predictive models using machine learning algorithms.
    • Analyze and interpret large datasets for insights.
    • Implement machine learning solutions in various domains.
    • Optimize and fine-tune machine learning models.
    • Evaluate and validate the performance of models.
    • Apply ethical considerations in machine learning applications.
    • Stay updated with the latest machine learning trends.
  • 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.Business Problems Can Be Solved Using Machine Learning:
    • 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

    • Benefits

      •  Proficient in optimizing and fine-tuning ML models.
      •  Knowledge of ethical considerations in ML applications.
      •  Strong foundation in the latest ML techniques and algorithms.
      •  Skills to implement ML solutions across diverse domains.
      •  Confidence in evaluating and validating model performance.
      •  Opportunity to contribute to groundbreaking ML advancements.
      •  Continuous professional growth and learning opportunities.
      •  In-depth understanding of machine learning principles.
      •  Ability to develop advanced predictive models.