Machine Learning for Data Science Projects
Course provided by IBM
About this course
- Understand the use of AI automation to accelerate the data model management lifecycle
- Understand linear algebra principles for machine learning
- Understand different modelling techniques
- Understand model validation and selection techniques
- Communicate results translating insight into business value
- Hands-on experience on IBM AutoAI, and IBM Watson Visual Recognition
How does it work?
- This course is divided into two practice levels to progress through at your own pace.
- 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
- 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.
- 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.
- 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.
- 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.
- EITHER Complete the Enterprise Data Science in Practice course from the Data Science Series.
- OR You will need prior knowledge and skills on the following topics:
- The composition and working of a data science team, including the different roles, processes and tools.
- Key statistics concepts and methods essential to finding structure in data and making predictions.
- Data science methodologies: characterise a business problem, formulate a hypothesis, demonstrate the use of methodologies in the analytics cycle, and plan for execution.
- 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.
- Hands-on experience with IBM Watson Studio, Data Refinery Spark, Jupyter Notebooks and Python libraries.
- Visualise statistical analysis, identify patterns, and effectively communicate findings to executive sponsors for business-driven decision making.