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Build Your First Python Chatbot Project

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Introduction

Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells us that 71% of people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.

Today we are going to build an exciting project on Chatbot. We will implement a chatbot from scratch that will be able to understand what the user is talking about and give an appropriate response.

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Making Data Scientists Productive in Azure

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Doing data science today is far more difficult than it will be in the next 5 to 10 years. Sharing and collaborating on workflows in painful, pushing models into production is challenging. Let’s explore what Azure provides to ease data scientists’ pains.

In this post, you will learn about the Azure Machine Learning Studio, Azure Machine Learning, Azure Databricks, Data Science Virtual Machine, and Cognitive Services. What tools and services can we choose based on a problem definition, skillset, or infrastructure requirements?

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A Complete Guide To Math And Statistics For Data Science

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As Josh Wills once said,

“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.

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Announcing OmniSci.jl: A Julia Client for OmniSci

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Today, I’m pleased to announce a new way to work with the OmniSci platform: OmniSci.jl, a Julia client for OmniSci! This Apache Thrift-based client is the result of a passion project I started when I arrived at OmniSci in March 2018 to complement our other open-source libraries for accessing data: pymapd, mapd-connector, and JDBC.

Julia and OmniSci: Similar in Spirit and Outcomes

If you’re not familiar with the Julia programming language, the language is a dynamically-typed, just-in-time compiled language built on LLVM that can achieve or beat the performance of high-performance, compiled languages such as C/C++ and FORTRAN. With the performance of C++ and the convenience of writing Python, Julia quickly became my favorite programming language when I started using it around 2013.

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An Interpretation of the Diagonal for an ROC Curve

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In another post, we discussed the use of ROC curves to describe the goodness of a classifier. I did say that I would post a brief paragraph on the interpretation of the diagonal. If you look around, some say that it describes the "strategy of randomly guessing a class", that it is obtained with "a diagnostic test that is no better than chance level," even obtained by "making a prediction by tossing of an unbiased coin."

Let us get back to ROC curves to illustrate those points. Consider a very simple dataset with 10 observations (that is not linearly separable):

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Intelligently Automate Machine Learning, Artificial Intelligence, and Data Science

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It was great speaking with Michael Berthold, Founder and CEO at KNIME during their fall summit. Michael created KNIME after seeing all of the great data pharmaceutical companies were generating but also seeing the difficulty they had garnering insights due to the challenges of massaging and analyzing the data.

KNIME is an open platform that enables organizations to put their data to good use. Open data science platforms enable:


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Top 10 Machine Learning, Deep Learning, and Data Science Courses for Beginners (Python and R)

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Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are really hot at this moment and offering a lucrative career to programmers with high pay and exciting work. It’s a great opportunity for programmers who are willing to learn these new skills and upgrade themselves. It’s also important from the job perspective because Robots and Bots are getting smarter day by day, thanks to these technologies and most likely will take over some of the jobs which many programmers do today. Hence, it’s important for software engineers and developers to upgrade themselves with these skills. Programmers with these skills are also commanding significantly higher salaries as data science is revolutionizing the world around us. Machine Learning specialist is one of the top paid technical jobs in the world. However, most developers and IT professionals are yet to learn these valuable set of skills.

For those, who don’t know what is a Data Science, Machine Learning, or Deep Learning, they are very related terms with all pointing towards machine doing jobs which is only possible for humans till date and analyzing the huge set of data collected by modern day application.


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Top 5 Courses to Learn R Programming

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More and more programmers are learning R programming language to become a Data Scientist, one of the hottest and high paying technical jobs on the planet.

Even though, I am from the Python camp, when it comes on choosing between Python and R for Data Science, Machine Learning, and Artificial Intelligence, mainly because of the awesome libraries like TensorFlow Python offers, I had tried R for a short time.


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Machine Learning for Cybersecurity 101

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The considerable number of articles cover Machine Learning for cybersecurity and the ability to protect us from cyber attacks. Still, it’s important to scrutinize how actually Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can help in cybersecurity right now and what this hype is all about.

First of all, I have to disappoint you. Unfortunately, Machine Learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where Machine Learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What’s worse, now hackers are able to use Machine Learning to carry out all their nefarious endeavors.


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AdaBoost Algorithm For Machine Learning

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What Is AdaBoost?

First of all, AdaBoost is short for Adaptive Boosting. Basically, Ada Boosting was the first really successful boosting algorithm developed for binary classification. Also, it is the best starting point for understanding boosting. Moreover, modern boosting methods build on AdaBoost, most notably stochastic gradient boosting machines.

Generally, AdaBoost is used with short decision trees. Further, the first tree is created, the performance of the tree on each training instance is used. Also, we use it to weight how much attention the next tree. Thus, it is created should pay attention to each training instance. Hence, training data that is hard to predict is given more weight. Although, whereas easy to predict instances are given less weight. 


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