Machine learning for data science projects
Rapid growth of AI in business presents unprecedented opportunities on one hand and the risk of legal exposure on the other. The new wave of professionals conversant in Data science, ML, and AI techniques, play a pivotal role in helping enterprises navigate these uncharted waters.
IBM SkillsBuild for Academia
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Gain insights on how to use AI and Machine learning low-code technologies to automate part of the Data science methodology and join a wave of new professionals with access to millions of jobs available in the market.
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Use advanced Data science methods and tools, leveraging statistical sciences, machine learning technologies, and industry-specific datasets, to implement unique data models that can solve challenging problems across all industries.
This course introduces advanced topics core to the Data science profession.
- Data modeling
- Machine learning
- Deep learning
- Real-world use cases.
- Understand the use of AI automation to accelerate the data model management lifecycle
- Understanding of linear algebra principles for machine learning
- Understanding of different modeling techniques
- Understanding of model validation and selection techniques
- Communicate results translating insight into business value
- Demonstrate through a project the ability to test different models on a dataset, validate and select the best model, and communicate results
- Hands-on experience on IBM AutoAI, and IBM Watson Visual Recognition
- Understand the inner dynamics of an auto insurance company and use Data science and AI to improve business outcomes.
About this course
This course is divided into two practice levels. Each level covers more advanced topics and builds up on top of the concepts, practice, and skills addressed in the previous practice levels.
Level 1 — Data modeling and machine learning
Advanced data analytics through the adoption of Machine Learning.
- 1. Data modeling*
- 2. Machine learning algorithms*
Level 2 — AI data science automation
Automate the data model management process using advanced AI tools.
- 1. Predict fraud using AutoAI(interactive case study)
- 2. Fraud detection using Visual Recognition(interactive case study)
*Depending on your current level of expertise, a thorough understanding of these concepts may require additional self-study on advanced statistical methods and algorithms
Skills you must have before joining this course offering:
Complete the Enterprise Data science in practice course from the Data Science Practitioner series.
Alternatively, 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: (a) Characterize a business problem; (b) Formulate a hypothesis; (c) Demonstrate the use of methodologies in the analytics cycle; (d) 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 normalization
- Hands-on experience with IBM Watson Studio, Data Refinery Spark, Jupyter Notebooks, and Python libraries
- Visualize statistical analysis, identify patterns, and effectively communicate findings to executive sponsors for business-driven decision-making.
Machine learning for Data science projectsSee badge
About this badge
This earner completed all learning activities included in this online learning experience related to advanced topics core to the data science profession including Data modeling techniques; Machine learning; Deep learning algorithms; Data science automation; demonstration of advanced skills application in the field of Data Science by role-playing critical roles in a data science team using latest AI tools for analytics /automation to address real problems.
AI automation, AI-on-AI, AutoAI, Data modeling, Data science, Feature engineering, Fraud analytics, Hyperparameter optimization, Machine learning, ML algorithms, Model deployment, Model training, and Visual recognition.
- Must attend a training session at a higher education institution implementing the IBM Skills Academy program.
- Must have completed the self-paced online course activities, and knowledge checks validating understanding of the covered topics.