Great Learning via Great Learning Academy
Our free marketing analytics Course Offered by Great Learning gives the subscribers an opportunity to understand analytics in marketing, how it will be useful, and what is the importance of the course.
Marketing analytics is the analysis of data collected through marketing efforts in order to find trends in things like how a campaign influenced conversions, customer behavior, regional preferences, creative preferences, and so on. Marketing analytics as a practice aims to leverage these trends and discoveries to improve future campaigns based on what worked in the past.
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In this module, you will learn about general analytics and its constitutions. You will also understand the importance of analytics for businesses and finance.
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Great Learning is in Tier 4: commercial training companies and platform-made courses of our institution ranking (58/100).
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
A curated collection of videos featuring top experts in artificial intelligence.
-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…
