Machine Learning Rapid Prototyping with IBM Watson Studio from Coursera

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.

This course is intended for practicing Data Scientists. While it showcases the automated AI capabilies of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts.

In order to be successful, you should have knowledge of:

Data Science workflow

Data Preprocessing

Feature Engineering

Machine Learning Algorithms

Hyperparameter Optimization

Evaluation measures for models

Python and scikit-learn library (including Pipeline class)

What's inside

Syllabus

Building a Rapid Prototype with Watson Studio AutoAI

In this module, you'll learn about the developing landscape of AutoAI technologies. You'll also become familiar with the Watson Studio platform in order to be able to perform your own AutoAI Experiments. After observing the AutoAI tool build prototypes for two use cases, you will try out the tool for yourself to build additional prototypes.

Automated Data Preparation and Model Selection

In this module, you will learn about the automated data preparation techniques performed by AutoAI and get a chance to experiment with different settings for data preprocessing in the AutoAI-generated Python notebook. You'll also learn about the procedure for automated model selection and experiment using different models on the datasets.

Automated Feature Engineering and Hyperparameter Optimization

In this module, you will learn about the algorithm for automated feature engineering and perform some exploratory data analysis to try to understand why the algorithm performed particular feature transformations. You'll also learn about sophisticated methods for optimizing hyperparameters and explore hyperparameter tuning on the datasets using the AutoAI-generated Python notebook.

Evaluation and Deployment of AutoAI-generated Solutions

In this module, you will evaluate prototypes using the different evaluation metrics calculated by the AutoAI tool. You will also deploy the prototype for testing using the Watson Machine Learning API.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to a rapidly growing trend in AI with AutoML—automating the ML process

Targets practicing Data Scientists looking to move into automated AI, a desirable skill in the industry

Showcases real-life use cases of AutoAI, providing learners with applicable examples

Emphasizes feature engineering techniques, an often overlooked skill in ML but one that is vital for Data Scientists

Useful for Data Scientists who wish to focus more on domain knowledge, rather than model optimization and feature engineering

Lacks discussion of important concepts like Machine Learning and Data Science

Reviews summary

Highly rated machine learning course

Students largely praise this machine learning course and its AutoAI toolset. They report that the course is useful, informative, and effective for rapid prototyping.

Students find this course effective for rapid prototyping with AutoAI.

"The objective of this course is to create quick prototype with AutoAI"

"I was able to apply my learning to a totally different problem in real estate domain helping a startup."

Students find this course informative and useful.

"Very much informative and useful with hands on excercise"

"This course taught me many aspects of machine learning that I was not aware of."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning Rapid Prototyping with IBM Watson Studio with these activities:

Review concepts of data science workflow and evaluation measures

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Ensure a strong foundation by refreshing your knowledge of data science workflow and evaluation measures before diving into AutoAI

Browse courses on Data Science Workflow

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Revisit articles, textbooks, or online resources on data science workflow
Review common evaluation metrics and their significance in measuring model performance

Review Python and scikit-learn library basics

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Review the fundamentals of Python and the scikit-learn library to strengthen your foundation for the course

Browse courses on Python

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Create a Python environment and install the scikit-learn library
Practice loading and cleaning data using scikit-learn's preprocessing module
Experiment with scikit-learn's machine learning algorithms for classification and regression

Explore IBM Watson Studio's AutoAI documentation and tutorials

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Become familiar with the capabilities and functionalities of IBM Watson Studio's AutoAI by exploring its documentation and tutorials

Browse courses on IBM Watson Studio

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Visit the IBM Watson Studio website and explore the AutoAI documentation
Complete the AutoAI tutorials to gain hands-on experience

Five other activities

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Solve coding exercises on data preprocessing and feature engineering

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Sharpen your skills in data preprocessing and feature engineering through coding exercises

Browse courses on Data Preprocessing

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Find coding exercises on platforms like LeetCode or HackerRank
Solve exercises that focus on data cleaning, feature selection, and dimensionality reduction

Build a simple AutoAI pipeline for a classification problem

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Apply your knowledge by creating an AutoAI pipeline to solve a real-world classification problem

Browse courses on Classification

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Choose a dataset and define the classification task
Create an AutoAI experiment and train a model
Evaluate the model's performance and identify areas for improvement

Join a study group to discuss AutoAI concepts and use cases

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Engage with peers to exchange knowledge, learn from different perspectives, and solidify your understanding of AutoAI

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Find or create a study group with fellow students
Meet regularly to discuss concepts, solve problems, and share insights

Participate in Kaggle competitions related to AutoAI

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Challenge yourself and apply your skills in a competitive environment to further develop your expertise in AutoAI

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Identify relevant Kaggle competitions that focus on AutoAI
Form a team or participate individually
Build and optimize AutoAI pipelines to achieve competitive results

Seek guidance from an experienced Data Scientist or AutoAI expert

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Connect with an experienced professional who can provide personalized guidance, feedback, and support for your AutoAI journey

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Identify potential mentors through professional networking, industry events, or online platforms
Reach out to prospective mentors and explain your interest in AutoAI

Career center

Learners who complete Machine Learning Rapid Prototyping with IBM Watson Studio will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists leverage large datasets to extract insights that help businesses make strategic decisions. Being able to rapidly prototype can help Data Scientists deliver faster. This course helps build the skills needed to rapidly prototype, which would help a Data Scientist to quickly test and refine ideas.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models to solve real-world problems This course's focus on rapid prototyping can help Machine Learning Engineers to iterate quickly on models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts explore and analyze data to help businesses make informed decisions. This course's focus on automation can help Data Analysts save time and focus on more strategic tasks.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts help businesses understand their needs and develop solutions to improve performance. This course's focus on rapid prototyping can help Business Analysts quickly test and refine ideas.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. This course's focus on automation can help Product Managers save time and focus on more strategic tasks.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course's focus on automation can help Software Engineers save time and focus on more strategic tasks.

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IT Architect

IT Architects design and maintain the infrastructure of an organization's IT systems. This course's focus on automation can help IT Architects save time and focus on more strategic tasks.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course's focus on automation can help Quantitative Analysts save time and focus on more strategic tasks.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course's focus on automation can help Operations Research Analysts save time and focus on more strategic tasks.

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Management Consultant

Management Consultants help organizations improve their performance. This course's focus on rapid prototyping can help Management Consultants quickly test and refine ideas.

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Market Researcher

Market Researchers study consumer behavior and trends to help businesses make informed decisions. This course's focus on rapid prototyping can help Market Researchers quickly test and refine ideas.

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Financial Analyst

Financial Analysts analyze financial data to help businesses make informed decisions. This course's focus on automation can help Financial Analysts save time and focus on more strategic tasks.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course's focus on automation can help Actuaries save time and focus on more strategic tasks.

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Statistician

Statisticians collect, analyze, and interpret data to help businesses make informed decisions. This course's focus on automation can help Statisticians save time and focus on more strategic tasks.

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Data Engineer

Data Engineers design and build the infrastructure that stores and processes data. This course's focus on automation can help Data Engineers save time and focus on more strategic tasks.

See salaries and explore the career path for Data Engineer