Practical Recommender Systems For Business Applications
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
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
Description
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 titles
You 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 :)
Who this course is for:
- People Wanting To Master The Python/Google Colab Environment For Data Science
- Students Interested In Developing Powerful Data Visualisations
- Learning to Make Product and Service Recommendations Based on Prior Choices
- Make Recommendations Based On Text Descriptions
- Identify the Best Recommender System For Your Problem
Instructor
- 4.4 Instructor Rating
- 21,083 Reviews
- 117,174 Students
- 54 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).