Build Recommender Systems Like Amazon, Netflix and Spotify

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Build Recommender Systems Like Amazon, Netflix and Spotify

Instructor: Pratiksha Bakrola

Included with Learn more

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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There are 2 modules in this course

Ever wonder how Netflix, Spotify, and Amazon seem to know exactly what you’ll like next?

Give me 1–2 hours, and I’ll take you from the basics of AI and Machine Learning to building a working recommendation engine step-by-step in hands-on labs. This course starts with clear theoretical concepts and transitions straight into guided coding exercises—making complex concepts easy to understand and execute. Perfect for beginners, self-taught coders, and aspiring analysts ready to get hands-on experience!

What's included

29 videos2 readings2 assignments1 discussion prompt

29 videosTotal 29 minutes

Introduction1 minute
Business Impact of Recommendation Engines2 minutes
Search-First and Discovery-First platforms1 minute
Breaking down recommendation thought process1 minute
Features0 minutes
Similarity Scoring & Content based filtering1 minute
User Profiles and Collaborative filtering1 minute
What is AI- Artificial Intelligence?0 minutes
What is ML- Machine Learning?0 minutes
What is a Machine Learning Model?0 minutes
What is a Generative Model & their use cases?1 minute
What is a Discriminative Model & their use cases?1 minute
How does a model Learn? (5 types of ML Algorithms)2 minutes
Types of Data0 minutes
Structured vs Unstructured Data0 minutes
Explicit vs Implicit Data1 minute
Real vs Synthetic Data1 minute
How data is collected 2 minutes
How data is ingested (Batch & Streaming Ingestion)2 minutes
Data Storage (Data Lake, Data warehouse, Data Lakehouse)1 minute
Defining the Problem1 minute
Data Selection and Data Cleaning1 minute
Feature & Feature Engineering0 minutes
Feature Selection1 minute
Feature Creation1 minute
Feature Transformation2 minutes
Encoding Techniques4 minutes
Feature Extraction0 minutes
Overview of the hands on excercise1 minute

2 readingsTotal 20 minutes

Recommendation Strategy10 minutes
ML Algorithms & their use case10 minutes

2 assignmentsTotal 32 minutes

Knowledge Check30 minutes
Course Learner Fit2 minutes

1 discussion promptTotal 10 minutes

Deconstruct how the human brain gives recommendations10 minutes

What's included

6 videos1 reading1 assignment2 ungraded labs

6 videosTotal 20 minutes

Dataset Familiarity1 minute
Guided Lab - Data Loading and Cleaning4 minutes
Guided Lab - Data Splitting4 minutes
Guided Lab - Feature Engineering and Encoding 6 minutes
Guided Lab - Model Training and Evaluation4 minutes
Closing Remarks0 minutes

1 readingTotal 10 minutes

Overview10 minutes

1 assignmentTotal 30 minutes

Reflection of the Hands-on activity30 minutes

2 ungraded labsTotal 75 minutes

Dataset Familiarity15 minutes
Building Recommendation System Hands on Activity60 minutes

Instructor

Pratiksha Bakrola

Daphli LLC

0 Courses0 learners

Offered by

Daphli LLC

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