Machine Learning for Data Science Projects

Machine Learning for Data Science Projects

Course provided by IBM


Summary overview

  • Online anytime
  • 30 hours study time
  • Information technology
  • £379.00
  • Advanced

About this course

Learning outcomes

  1. Understand the use of AI automation to accelerate the data model management lifecycle
  2. Understand linear algebra principles for machine learning
  3. Understand different modelling techniques
  4. Understand model validation and selection techniques
  5. Communicate results translating insight into business value
  6. Hands-on experience on IBM AutoAI, and IBM Watson Visual Recognition

How does it work?

  1. This course is divided into two practice levels to progress through at your own pace.

  2. Each level covers more advanced topics and builds up on top of the concepts, practice and skills addressed on the previous practice levels.

Who should take this course

  1. If you want to gain insights on how to use AI and Machine learning low-code technologies to automate part of the data science methodology, this course is great for you.

  2. If you want to join a wave of new professionals with access to millions of jobs available in the market, this course is perfect for you.

  3. If you already have a lot of experience in data science, but desire to dig deeper into advanced concepts such as machine learning and deep learning, this course is right for you.

  4. If youve completed our Getting Started with Enterprise Data Science and Enterprise Data Science in Practice foundation and intermediate courses, this is the logical next step for your knowledge building.


  1. EITHER Complete the Enterprise Data Science in Practice course from the Data Science Series.

  2. OR You will need prior knowledge and skills on the following topics:

  3. The composition and working of a data science team, including the different roles, processes and tools.

  4. Key statistics concepts and methods essential to finding structure in data and making predictions.

  5. Data science methodologies: characterise a business problem, formulate a hypothesis, demonstrate the use of methodologies in the analytics cycle, and plan for execution.

  6. Construct usable data sets by identifying and collecting the data required, and manipulating, transforming and cleaning the data; demonstrating the ability to deal with data anomalies such as missing values, outliers, unbalanced data and data normalisation.

  7. Hands-on experience with IBM Watson Studio, Data Refinery Spark, Jupyter Notebooks and Python libraries.

  8. Visualise statistical analysis, identify patterns, and effectively communicate findings to executive sponsors for business-driven decision making.