Automate Machine Learning Using Databricks AutoML from Pluralsight

This course will teach you how you can build and train regression, classification, and forecasting models using Databricks AutoML. AutoML automates data preparation and model training thus allowing you to build models with little to no code.

Databricks AutoML is an important step towards the democratization of machine learning. AutoML makes it easy for anyone to build and train robust models with little to no code.

In this course, Automate Machine Learning Using Databricks AutoML, you will be introduced to the basic concepts of Databricks AutoML.

First, you will see how AutoML automates every step of the machine learning process from data preparation, and data preprocessing to model training and evaluation.

Next, you will first train regression and classification models using the AutoML user interface to configure your model training, and you can configure settings to impute missing values, choose model frameworks and evaluate models.

Then, you will learn to use the AutoML Python API to train regression and classification models. The Python API offers simple regress() and classify() functions which you can configure using input parameters.

Finally, you will work with time series datasets and train forecasting models using both the AutoML UI and the AutoML Python API.

When you are finished with this course you will be able to confidently use AutoML to train regression, classification, and forecasting models and deploy them to production.

What's inside

Syllabus

Course Overview

Training Regression and Classification Models Using the AutoML UI

Training Regression and Classification Models Using the AutoML Python API

Training Forecasting Models Using AutoML

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores Databricks AutoML, which automates machine learning tasks in cloud computing

Taught by Janani Ravi, a recognized expert on automating machine building and training

Provides hands-on labs and interactive materials for practice

Useful for professionals seeking to automate machine learning tasks in cloud computing

Requires learners to come in with some basic knowledge of machine learning concepts

May benefit learners with an interest in cloud computing and data analytics

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 Automate Machine Learning Using Databricks AutoML with these activities:

Explore AutoML tutorials and documentation

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Familiarizing yourself with AutoML tutorials and documentation will provide a practical understanding of its capabilities and how to use it effectively.

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Go through the official Databricks AutoML tutorial
Read AutoML documentation on regression, classification, and forecasting tasks

Review Python basics

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Python is the language used in this course, reviewing the basics will help ensure a strong foundation.

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Review variables, data types, and operators
Review control flow (if-else, loops)
Review functions and modules

Mentor a junior data scientist or student

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Mentoring someone less experienced in machine learning can reinforce your own understanding and help others grow in the field.

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Identify a junior data scientist or student who could benefit from your guidance
Establish a regular meeting schedule and provide support and advice
Encourage your mentee to ask questions and share their progress

14 other activities

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Attend an AutoML workshop

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Attending a workshop will provide an opportunity to learn from experts and ask questions.

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Find a relevant workshop
Register for the workshop
Attend the workshop and take notes

Join a study group

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Discussing course material with peers will help reinforce understanding and identify areas for improvement.

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Find a study group or start one
Meet regularly to discuss course material
Work together on practice problems

Participate in a Study Group

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Peer learning can enhance retention and understanding.

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Find a study group or create your own.
Meet regularly to discuss course material.
Work together on assignments and projects.

Practice data preparation and feature engineering

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Hands-on practice with data preparation and feature engineering will enhance your understanding of these crucial steps in machine learning model building.

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Use a real-world dataset to perform data cleaning and transformation
Engineer new features from existing ones to improve model performance
Document and evaluate the effectiveness of your data preparation and feature engineering techniques

Practice Regression and Classification Model Building

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Practice will solidify understanding of the techniques covered in this course.

Browse courses on Regression Models

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Build a regression model using the AutoML UI.
Build a classification model using the AutoML Python API.

Build a regression model using AutoML UI

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Creating a regression model using the AutoML UI will provide practical experience with the tool and reinforce the concepts learned in the course.

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Import a dataset with numerical target variable
Configure AutoML settings, such as training time and evaluation metrics
Interpret the results and evaluate model performance
Deploy the model for real-world use

Follow Tutorials on Forecasting Model Building

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This will extend and support the instruction in the course.

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Find a tutorial on forecasting model building using AutoML.
Follow the tutorial and build a forecasting model.

Practice regression modeling

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Regression models are a core component of this course, practicing with them will strengthen understanding.

Browse courses on Regression

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Review regression concepts
Practice with linear regression
Practice with logistic regression

Create a cheat sheet

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Creating a cheat sheet will help summarize key concepts and make them easily accessible.

Browse courses on AutoML

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Identify key concepts from course material
Create a cheat sheet using notes or online resources
Review the cheat sheet regularly

Start a Machine Learning Project

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This activity will provide students with hands-on experience in applying machine learning techniques.

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Choose a project idea.
Gather data and prepare it for analysis.
Build a machine learning model.
Evaluate the model's performance.
Deploy the model.

Build and Deploy a Machine Learning Model

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This activity will test a student's ability to apply course concepts to a real-world problem.

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Choose a dataset and a machine learning task.
Build a machine learning model using AutoML.
Deploy the model to a production environment.

Participate in a machine learning hackathon

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Participating in a machine learning hackathon will challenge you to apply your skills, collaborate with others, and potentially win prizes.

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Find a hackathon that aligns with your interests and skill level
Form a team or work independently
Develop a solution to the hackathon challenge
Present your solution and compete for prizes

Build a machine learning model

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Applying the concepts of the course to a real-world problem will help reinforce understanding.

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Define the problem and gather data
Train and evaluate the model
Deploy the model

Create a forecasting model using AutoML

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Building a forecasting model using AutoML will provide valuable experience in handling time series data and predicting future trends.

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Acquire a time series dataset
Prepare the data for forecasting, including handling missing values and outliers
Configure AutoML settings for forecasting tasks
Deploy the model and monitor its performance over time

Career center

Learners who complete Automate Machine Learning Using Databricks AutoML will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. They may work on a variety of projects, such as developing new algorithms, improving the performance of existing models, or deploying models to production. Machine Learning Engineers typically have a strong background in computer science and mathematics.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, mathematics, and computer science to solve business problems. They may use a variety of tools and techniques to collect, clean, and analyze data. Data Scientists often work with other people in their organization to help them make better decisions.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use various tools and techniques to analyze data, ranging from spreadsheets and databases to more specialized software. They may use statistical techniques as well as machine learning to find patterns and derive insights from the data. Data Analysts often work with other people in their organization to help them make better decisions.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use their knowledge of business and technology to help organizations improve their performance. They may work on a variety of projects, such as developing new products or services, improving customer service, or streamlining operations. Business Analysts typically have a strong background in business and analytics.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They may work on a variety of projects, such as developing new features, fixing bugs, or improving the performance of existing applications. Software Engineers typically have a strong background in computer science.

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Data Analytics Manager

Data Analytics Managers lead and manage teams of data analysts. They may be responsible for developing and implementing data analytics strategies, as well as managing the day-to-day operations of the data analytics team. Data Analytics Managers typically have a strong background in data analytics and management.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries. They may work on projects such as improving supply chain efficiency, optimizing production schedules, or designing new products or services. Operations Research Analysts typically have a strong background in mathematics and statistics.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They may work for hedge funds, investment banks, or other financial institutions. Quantitative Analysts typically have a strong background in mathematics and statistics.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They may work in a variety of industries, such as insurance, finance, and healthcare. Actuaries typically have a strong background in mathematics and statistics.

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Statistician

Statisticians collect, analyze, and interpret data to answer questions and make predictions. They may work in a variety of industries, such as healthcare, finance, and education. Statisticians typically have a strong background in mathematics and statistics.

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Market Research Analyst

Market Research Analysts collect and analyze data about consumers and markets. They may use this data to develop new products or services, improve marketing campaigns, or make other business decisions. Market Research Analysts typically have a strong background in marketing and research.

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Risk Analyst

Risk Analysts identify and assess risks to businesses and organizations. They may work in a variety of industries, such as finance, insurance, and healthcare. Risk Analysts typically have a strong background in risk management and finance.

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Financial Analyst

Financial Analysts analyze financial data and make investment recommendations. They may work for investment banks, hedge funds, or other financial institutions. Financial Analysts typically have a strong background in finance and accounting.

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Sales Analyst

Sales Analysts analyze sales data to identify trends and make recommendations on how to improve sales performance. They may work for a variety of companies, such as retail stores, manufacturers, and software companies. Sales Analysts typically have a strong background in sales and analytics.

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Customer Success Manager

Customer Success Managers help customers get the most value out of a product or service. They may work for a variety of companies, such as software companies, consulting firms, and financial institutions. Customer Success Managers typically have a strong background in customer service and sales.

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