Machine Learning

Top 10 Practices to Operationalize Your Data Science Projects in the Real World

Top 10 Practices to Operationalize Your Data Science Projects in the Real World

I want to use this post to share the top 10 practices to deploy your machine learning models into production in the real world.

Decision Trees: How to Optimize My Decision-Making Process?

Decision Trees: How to Optimize My Decision-Making Process?

The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.

k-Nearest Neighbors: Who are close to you?

k-Nearest Neighbors: Who are close to you?

The k-Nearest Neighbors algorithm is a simple and effective way to classify data. It is an example of instance-based learning, where you need to have instances of data close at hand to perform the machine learning algorithm.

The 7 NLP Techniques That Will Change How You Communicate in The Future (Part II)

The 7 NLP Techniques That Will Change How You Communicate in The Future (Part II)

From machine translation that connects humans across cultures, to conversational chatbots that help with customer service; from sentiment analysis that deeply understands a human’s mood, to attention mechanisms that can mimic our visual attention, the field of NLP is too expansive to cover completely, so I’d encourage you to explore it further, whether through online courses, blog tutorials, or research papers.

The 7 NLP Techniques That Will Change How You Communicate in The Future (Part I)

The 7 NLP Techniques That Will Change How You Communicate in The Future (Part I)

NLP is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Fully understanding and representing the meaning of language is an extremely difficult goal. Why? Because the human language is quite special.

The 5 Machine Learning Use Cases that Optimize Your Airbnb Travel Experience

The 5 Machine Learning Use Cases that Optimize Your Airbnb Travel Experience

Wondering how Airbnb sorts and delivers its listings when you search for a place to stay on your next getaway? Check out 5 important use cases of machine learning that are currently being deployed by Airbnb’s engineers and data scientists to solve this problem.

The 5 Computer Vision Techniques That Will Change How You See The World

The 5 Computer Vision Techniques That Will Change How You See The World

Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).

Snapchat's Filters: How computer vision recognizes your face

Snapchat's Filters: How computer vision recognizes your face

In those moments of boredom when you're playing with Snapchat's filters - sticking your tongue out, ghoulifying your features, and working out how to get the flower crown to fit exactly on your head - surely you've had a moment where you've wondered what's going on, on a technical level - how Snapchat manages to match your face to the animations?

16 Useful Advice for Aspiring Data Scientists

16 Useful Advice for Aspiring Data Scientists

Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.

12 Useful Things to Know about Machine Learning

12 Useful Things to Know about Machine Learning

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks.

Pinterest’s Visual Lens: How computer vision explores your taste

Pinterest’s Visual Lens: How computer vision explores your taste

When it comes to looking for something you want to try — a new salad recipe, a new classy dress, a new chair for your living room — you really need to see it first. Humans are visual creatures. We use our eyes to decide if something looks good, or if it matches our style.

The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

Today, deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They’re being deployed on a large scale by companies such as Google, Microsoft, and Facebook.I hope that this post helps you learn the core concepts of neural networks, including modern techniques for deep learning.

The 10 Deep Learning Methods AI Practitioners Need to Apply

The 10 Deep Learning Methods AI Practitioners Need to Apply

Deep Learning is strongly technique-focused. There are not much concrete explanations for each of the new ideas. Most new ideas came out with experimental results attached to prove that they work. Deep Learning is like playing LEGO. Mastering LEGO is as challenging as any other arts, but getting into it is easier.

The 10 Algorithms Machine Learning Engineers Need to Know

The 10 Algorithms Machine Learning Engineers Need to Know

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data.

The 10 Statistical Techniques Data Scientists Need to Master

The 10 Statistical Techniques Data Scientists Need to Master

Data scientists live at the intersection of coding, statistics, and critical thinking. As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them. So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis.