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September Edition: Machine Learning Case-Studies

The entire landscape of Artificial Intelligence, Machine Learning and Deep Learning have rapidly changed in the last five years. Instead of…

Image credit: Emlie Perron

The entire landscape of Artificial Intelligence, Machine Learning and Deep Learning have rapidly changed in the last five years. Instead of being confined to lab experiments or research papers, machine learning has quickly picked up pace with regard to mainstream industry adoption across diverse domains. We have also started to see and even use intelligent systems in our daily lives. Some of these include smart assistants like Google Now, Alexa, Siri and Cortana, self-driving cars, expert game-playing agents, chatbots, edge analytics and the list goes on and on. We as humans have also gradually started accepting such intelligent systems and technology in our daily lives, regardless of all the hype built up around AI vs. Humans.

AI is here to assist and enable us to automate and solve complex and often repetitive tasks seamlessly helping us boost productivity and profit. Hence, as we step into this new era of learning machines, digitization and automation, it is very important to understand not only the theoretical concepts of statistical modeling and machine learning but also how you can effectively apply these tools to solve real-world problems.

"AI is the new electricity … We have enough papers. Stop publishing, and start transforming people’s lives with technology" – Andrew Ng

Thus, the time is now to start ‘applying’ machine learning to solve both complex and simple real-world problems across different industry segments including finance, retail, tech, healthcare, logistics, transportation and even the government. The best way to learn how to apply and use machine learning is to look at proven strategies and best practices of machine learning case-studies in the industry. This edition brings you some of the best case-studies of applying machine learning to solve a wide-variety of interesting problems. We hope you learn from them and go out there and do something amazing!

Dipanjan (DJ) Sarkar, Data Scientist, Author & TDS Editor


A Machine Learning Approach – Building a Hotel Recommendation Engine

By Susan Li – 5 min read

All online travel agencies are scrambling to meet the AI driven personalization standard set by Amazon and Netflix. In addition, the world of online travel has become a highly competitive space where brands try to capture our attention (and wallet) with recommending, comparing, matching and sharing.


Spotify’s "This Is" playlists: the ultimate song analysis for 50 mainstream artists

By James Le – 15 min read

Each artist has their own unique musical styles. From Ed Sheeran who devotes his life to the acoustic guitar, to Drake who masters the art of rapping. From Adele who can belt some crazy high notes on her pop ballads, to Kygo who creates EDM magic on his DJ set.


An Examination of International Cuisines through Unsupervised Learning

By Ben Sturm – 8 min read

Like a lot of people, I’m a big fan of food. I was very lucky to be raised in a home where meals made from scratch were the norm. My mom did all of the cooking and because she immigrated to the US from Germany, I was exposed to a lot of delicious German dishes.


How To Create Data Products That Are Magical Using Sequence-to-Sequence Models

By Hamel Husain – 17 min read

I never imagined I would ever use the word "magical" to describe the output of a machine learning technique. This changed when I was introduced to deep learning, where you can accomplish things like identify objects in pictures or sort two tons of legos.


Evolution of a salesman: A complete genetic algorithm tutorial for Python

By Eric Stoltz – 8 min read

Drawing inspiration from natural selection, genetic algorithms (GA) are a fascinating approach to solving search and optimization problems. While much has been written about GA (see: [here](https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b) and here), little has been done to show a step-by-step implementation of a GA in Python for more sophisticated problems.


An End-to-End Project on Time Series Analysis and Forecasting with Python

By Susan Li – 9 min read

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.


Using Machine Learning To Simulate World Cup Matches

By Rodrigo Nader – 11 min read

The World Cup is reaching a new stage and few were those who could anticipate the group stage outcomes. Now it’s time for a much more thrilling phase, where the greatest of the world will face each other. The goal of this article is, using the power of data science with Python, try to uncover some of the statistics those games will present.


Clustering algorithms for customer segmentation

By Sowmya Vivek

In today’s competitive world, it is crucial to understand customer behavior and categorize customers based on their demography and buying behavior. This is a critical aspect of customer segmentation that allows marketers to better tailor their marketing efforts to various audience subsets in terms of promotional, marketing and product development strategies.


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