Recommendation Engines perform a variety of tasks, but the most important one is to find products that are most relevant to the user. Follow along with this intensive Recommendation Systems in Python training course to get a firm grasp on this essential Machine Learning component.
Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.
This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).
Who is it for?
- Analytics professionals, modellers, big data professionals who haven’t had exposure to machine learning
- Engineers who want to understand or learn machine learning and apply it to problems they are solving
- Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
- Tech executives and investors who are interested in big data, machine learning or natural language processing
- MBA graduates or business professionals who are looking to move to a heavily quantitative role
This course consists of the following modules:
- Module 01: Would You Recommend to a Friend?
- Module 02: Recommendation Systems in Python
From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.
Method of Assessment:
At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.
Successful candidates will be awarded a certificate for Machine Learning – Recommendation Systems in Python.
Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
This intensive course in Python Training will help you to gain a stronger hold on the essential Machine Learning component. Although this tool tends to perform a wide variety of tasks the most essential one in this instance is to find the most relevant products to the user of the platform. A career with bright prospects await you with the completion of this course. According to www.payscale.uk, some of the key job positions along with the average UK salary per annum you can go for after completing this course will be as follows.
- Machinist – £18,311 per annum
- Quantitative Analyst – £54,517 per annum
- Research Scientist – £30,802 per annum
- Market Researcher – £23,707 per annum
- Risk Analyst – £33,665 per annum
- Quantitative Analyst – £54,517 per annum
Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum
PLEASE NOTE: We do not provide any software with this course.
|Machine Learning – Recommendation Systems in Python||FREE||00:00:00|
|1: Would You Recommend to a Friend?|
|1. Introduction: You, This Course & Us!||00:00:00|
|2. What do Amazon and Netflix have in common?||00:00:00|
|3. Recommendation Engines: a look inside||00:00:00|
|4. What are you made of? Content-Based Filtering||00:00:00|
|5. With a little help from friends: Collaborative Filtering||00:00:00|
|6. A Model for Collaborative Filtering||00:00:00|
|7. Top Picks for You! Recommendations with Neighborhood Models||00:00:00|
|8. Discover the Underlying Truth: Latent Factor Collaborative Filtering||00:00:00|
|9. Latent Factor Collaborative Filtering continued||00:00:00|
|10. Gray Sheep & Shillings: Challenges with Collaborative Filtering||00:00:00|
|11. The Apriori Algorithm for Association Rules||00:00:00|
|2: Recommendation Systems in Python|
|1. Installing Python : Anaconda & PIP||00:00:00|
|2. Back to Basics: Numpy in Python||00:00:00|
|3. Back to Basics: Numpy & Scipy in Python||00:00:00|
|4. Movielens & Pandas||00:00:00|
|5. Code Along: What’s my favorite movie? – Data Analysis with Pandas||00:00:00|
|6. Code Along: Movie Recommendation with Nearest Neighbor CF||00:00:00|
|7. Code Along: Top Movie Picks (Nearest Neighbor CF)||00:00:00|
|8. Code Along: Movie Recommendations with Matrix Factorization||00:00:00|
|9. Code Along: Association Rules with the Apriori Algorithm||00:00:00|
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