Machine Learning Product Management - Strategy to Deployment

Machine Learning Product Management - Strategy to Deployment

位教师：Packt - Course Instructors

访问权限由 New York State Department of Labor 提供

8个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

8个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

8 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Understand the essential responsibilities and skills of a Machine Learning Product Manager.
Learn how to evaluate when machine learning is the right tool for a product.
Develop the ability to structure ML teams and manage complex ML projects.
Gain practical knowledge in selecting the right algorithms and deploying ML models into production.

您将获得的技能

您将学习的工具

Model Deployment

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

9 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有8个模块

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive deep into machine learning product management, gaining hands-on knowledge and insights into how machine learning is integrated into products. The course explores critical roles, skills, and real-world applications of ML, offering practical exercises that reinforce concepts and strategies. Through detailed lessons, you'll explore the lifecycle of an ML product, from ideation and team structuring to deployment and monitoring. You'll also learn to make strategic decisions on when machine learning is the right tool and how to avoid common pitfalls. The journey includes a detailed exploration of data acquisition, preparation, preprocessing, and algorithm selection, helping you gain a comprehensive understanding of the full machine learning lifecycle. With an emphasis on practical applications, you'll also have the opportunity to implement various ML strategies in real-world scenarios. This course is designed for aspiring machine learning product managers, data-driven professionals, and those interested in understanding the intersection of product management and machine learning. It does not require prior technical experience but a passion for the field is essential. By the end of the course, you will be able to evaluate data needs for ML, structure ML teams, choose suitable algorithms, and deploy models into production, among other key competencies.

In this module, we will introduce you to the core concepts of machine learning product management, the essential role of an ML Product Manager, and key terminology. You'll also learn how machine learning is transforming industries and how to identify products for ML integration.

涵盖的内容

13个视频1篇阅读材料

13个视频总计61分钟

Course Overview2分钟
Understanding the Role of an ML Product Manager2分钟
Defining Machine Learning and Its Core Concepts4分钟
Get to Know Your Instructor2分钟
The Rise of Machine Learning in Industry8分钟
Exercise #1: Identify Your Product for ML Integration4分钟
How Machine Learning Algorithms Learn6分钟
Supervised, Unsupervised, and Reinforcement Learning8分钟
Exercise #2: Classify the Type of ML5分钟
Introduction to Deep Learning and Neural Networks7分钟
Real-World Applications of Machine Learning4分钟
Key Terminology Every ML Product Manager Should Know3分钟
Exercise #3: Apply Machine Learning Terminology in Context6分钟

1篇阅读材料总计10分钟

Full Course Resources10分钟

In this module, we will guide you through the decision-making process for implementing machine learning in your product. You’ll learn how to evaluate when ML is the best solution, the data needs for a project, and the common challenges to watch out for.

涵盖的内容

7个视频1个作业

7个视频总计36分钟

Understanding the AI Flywheel4分钟
Common Pitfalls in ML Product Development6分钟
When Machine Learning Is the Right Tool4分钟
When Machine Learning Is Not the Answer3分钟
Exercise #4: Do You Need Interpretability in Your Model?6分钟
Evaluating Data Requirements for ML Implementation3分钟
Exercise #5: Making the Call: ML or Not?11分钟

1个作业总计15分钟

Decision Criteria for Machine Learning Implementation - Assessment15分钟

In this module, we will explore the unique role of an ML Product Manager, how to structure teams for success, and what each phase of the ML project lifecycle entails. You'll also get hands-on experience with formulating and validating project hypotheses.

涵盖的内容

7个视频1个作业

7个视频总计44分钟

The Unique Role of an ML Product Manager3分钟
Structuring an Effective ML Team5分钟
Core Roles in a Machine Learning Project7分钟
Understanding the ML Project Lifecycle14分钟
Exercise #6: Develop and Validate Your Hypothesis8分钟
Exercise #7: Frame Your Machine Learning Challenge3分钟
Exercise #8: Define the ML Problem Statement4分钟

1个作业总计15分钟

Managing Machine Learning Projects - Assessment15分钟

In this module, we will cover strategies for acquiring and preparing data for machine learning models. You will explore data acquisition techniques, data storage options, and methods to structure data for successful ML model training.

涵盖的内容

8个视频1个作业

8个视频总计37分钟

Data Acquisition Techniques for Machine Learning10分钟
Leveraging Google reCAPTCHA for Data Collection4分钟
Exercise #9: Identify User-Generated Data Labelling3分钟
Problem Simplification in ML Data Design2分钟
Exercise #10: Structuring Data for Model Input3分钟
Top Open Datasets for Machine Learning Projects5分钟
Estimating Data Requirements for ML Models4分钟
Data Storage Options: Warehouse, Lake, and Graph7分钟

1个作业总计15分钟

Data Acquisition and Preparation for Machine Learning - Assessment15分钟

In this module, we will guide you through essential preprocessing techniques including data cleaning, transformation, and feature engineering. You’ll also learn how to split and sample data effectively for your ML models.

涵盖的内容

5个视频1个作业

5个视频总计21分钟

Data Cleaning and Scrubbing Techniques6分钟
How to Sample and Split Data for ML Models5分钟
Data Transformation Methods for Machine Learning5分钟
Introduction to Feature Engineering Techniques2分钟
Exercise #11: Brainstorming a New Feature for Your Model3分钟

1个作业总计15分钟

Preprocessing Techniques for Machine Learning - Assessment15分钟

In this module, we will dive into selecting the right machine learning algorithm for your project, exploring various types of models including regression, classification, and anomaly detection. You’ll also learn when to build, buy, or outsource solutions.

涵盖的内容

8个视频1个作业

8个视频总计41分钟

How to Choose the Right Machine Learning Algorithm2分钟
Build vs Buy vs Outsource: ML Solution Strategy4分钟
Exploring Machine Learning as a Service (MLaaS)7分钟
Regression Algorithms Explained: Linear, Polynomial, Logistic5分钟
Classification Algorithms: SVM, K-NN, Decision Trees9分钟
Clustering Algorithms: K-Means and Mean Shift6分钟
Anomaly Detection with LOF and DBSCAN5分钟
Ensemble Methods: Bagging, Boosting, and Stacking4分钟

1个作业总计15分钟

Algorithm Selection and ML Solution Development - Assessment15分钟

In this module, we will explore how to evaluate and optimize machine learning models using metrics like the confusion matrix, precision, and recall. You’ll also learn strategies for continuous performance improvement and user experience optimization.

涵盖的内容

5个视频1个作业

In this module, we will take you through the steps of deploying your machine learning model into production and discuss how to monitor its performance. You will also learn strategies for keeping your model optimized and scalable.