Implementing Data Science Driven Recommender Systems For Business Applications Using Python Within Google Colab
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH PYTHON
What you’ll learn
- Learn what recommender systems are and their importance for business intelligence.
- Learn the main aspects of implementing a Python data science framework within Google Colab.
- Basic text analysis to learn more about user preferences.
- Implement practical recommender systems using Python.
Course Content
- Welcome To The Course –> 6 lectures • 25min.
- Basics Of Python For Data Science –> 12 lectures • 1hr 20min.
- Basic Statistical Concepts –> 10 lectures • 29min.
- What Are Recommender Engines? –> 1 lecture • 3min.
- Filtering Based Recommender Engines –> 5 lectures • 26min.
- Common Recommender Engines –> 6 lectures • 27min.
- Working With Text Data –> 9 lectures • 34min.
- Text Based Recommender System –> 4 lectures • 22min.
- Miscellaneous –> 1 lecture • 11min.
Requirements
- Be Able To Operate & Install Software On A Computer.
- A Gmail Account.
- Prior Exposure to the Python Will be Helpful.
- Prior Exposure to the Jupyter Notebook Ecosystem.
- An Interest in Learning About Practical Recommender Systems.
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT BUILDING PRACTICAL RECOMMENDER SYSTEMS WITH PYTHON
- Are you interested in learning how the Big Tech giants like Amazon and Netflix recommend products and services to you?
- Do you want to learn how data science is hacking the multibillion e-commerce space through recommender systems?
- Do you want to implement your own recommender systems using real-life data?
- Do you want to develop cutting edge analytics and visualisations to support business decisions?
- Are you interested in deploying machine learning and natural language processing for making recommendations based on prior choices and/or user profiles?
You Can Gain An Edge Over Other Data Scientists If You Can Apply Python Data Analysis Skills For Making Data-Driven Recommendations Based On User Preferences
- By enhancing the value of your company or business through the extraction of actionable insights from commonly used structured and unstructured data commonly found in the retail and e-commerce space
- Stand out from a pool of other data analysts by gaining proficiency in the most important pillars of developing practical recommender systems
MY COURSE IS A HANDS-ON TRAINING WITH REAL RECOMMENDATION RELATED PROBLEMS- You will learn to use important Python data science techniques to derive information and insights from both structured data (such as those obtained in typical retail and/or business context) and unstructured text data
My course provides a foundation to carry out PRACTICAL, real-life recommender systems tasks using Python. By taking this course, you are taking an important step forward in your data science journey to become an expert in deploying Python data science techniques for answering practical retail and e-commerce questions (e.g. what kind of products to recommend based on their previous purchases or their user profile).
Why Should You Take My Course?
I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real-life data from different sources and producing publications for international peer-reviewed journals.
This course will help you gain fluency in deploying data science-based BI solutions using a powerful clouded based python environment called GoogleColab. Specifically, you will
- Learn the main aspects of implementing a Python data science framework within Google Colab
- Learn what recommender systems are and why they are so vital to the retail space
- Learn to implement the common data science principles needed for building recommender systems
- Use visualisations to underpin your glean insights from structured and unstructured data
- Implement different recommender systems in Python
- Use common natural language processing (NLP) techniques to recommend products and services based on descriptions and/or titlesYou will work on practical mini case studies relating to (a) Online retail product descriptions (b) Movie ratings (c) Book ratings and descriptions to name a few
In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!
ENROLL NOW 🙂