Machine-Learning Beginner Premium

Automated Machine Learning Virtual Internship

In this Automated Machine Learning Virtual Internship, students will learn how to leverage AutoML tools and techniques to streamline the machine learning model development lifecycle, from data preprocessing to model deployment. They will gain hands-on experience in automating various steps of the ML pipeline, including data cleaning, feature engineering, model selection, and model tuning. By the end of the internship, students will be equipped with the skills to build and deploy efficient machine learning models without the need for extensive manual intervention.

weeks
8 tasks
0 enrolled
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Track price: $49.00

Track Overview

This track provides hands-on experience and real-world projects to build your skills.

Tasks & Milestones

Explore AutoML Landscape

Beginner

In this task, students will research and compare different AutoML tools and frameworks, including their features, strengths, and use cases.

8 hours

Hands-on with AutoML

Beginner

In this task, students will gain practical experience in using an AutoML tool to build a machine learning model.

12 hours

Automated Data Cleaning and Preprocessing

Beginner

In this task, students will use an AutoML tool to automatically clean and preprocess a dataset, exploring the tool's capabilities in handling missing values, outliers, and other data quality issues.

10 hours

Automated Feature Engineering

Beginner

In this task, students will explore the use of AutoML tools for automating the feature engineering process, including the generation and selection of relevant features.

12 hours

Automated Model Selection

Beginner

In this task, students will use an AutoML tool to automatically select the best-performing machine learning model for a given dataset and problem.

10 hours

Automated Hyperparameter Tuning

Beginner

In this task, students will use an AutoML tool to automatically tune the hyperparameters of a machine learning model, optimizing its performance.

12 hours

Automated Model Deployment

Beginner

In this task, students will use an AutoML tool to automatically deploy a machine learning model to a production environment.

12 hours

Automated Model Monitoring

Beginner

In this task, students will use an AutoML tool to automatically monitor the performance and health of a deployed machine learning model.

10 hours

Prerequisites

  • • Basic understanding of machine learning concepts
  • • Familiarity with Python programming

Certificate

Certificate of Completion

Earn a certificate upon successful completion