While architecting a data-based solution, one needs to approach the problem differently depending on the specific strategy being adopted. In traditional machine learning, the focus is mostly on feature engineering. In DL, the emphasis is shifting to tagging larger volumes of data with less focus on feature development. Similarly, synthetic data is a lot more useful in DL than ML. So, the data strategies can be significantly different. Both approaches require very similar approaches to the analysis of errors. But, in most development processes, those approaches are not followed leading to substantial delay in production times. Hyper parameter tuning for performance improvement requires different strategies between ML and DL solutions due to the longer training times of DL systems. Transfer learning is a very important aspect to evaluate in building any state of the art system whether ML or DL. The last but not the least is understanding the biases that the system is learning. Deeply non-linear models require special attention in this aspect as they can learn highly undesirable features.

In our presentation, we will focus on all the above aspects with suitable examples and provide a framework for practitioners for building ML/DL applications.

Cleaning, Preparing , Transforming and Exploring Data is the most time-consuming and least enjoyable data science task, but one of the most important ones. With Optimus we’ve solve this problem for small or huge datasets, also improving a whole workflow for data science, making it easier for everyone. You will learn how the combination of Apache Spark and Optimus with the Python ecosystem can form a whole framework for Agile Data Science allowing people and companies to go further, and beyond their common sense and intuition to solve complex business problems.

This demo would be regarding some of the work that I have already done since starting my journey in Machine Learning. So, there are a lot of MOOCs out there for ML and data science but the most important thing is to apply the concepts learned during the course to solve simple real-world use cases.

• One of the projects that I did included building state of the art Facial recognition system [VIDEO]. So for that, I referred to several research papers and the foundation was given to me in one of the courses itself, however, it took a lot of effort to connect the dots and that's the fun part.
• In another project, I made an Emoji Classifier for humans [VIDEO] based on your hand gestures. For that, I used deep learning CNN model to achieve great accuracy. I took reference from several online resources that made me realize that the data science community is very helpful and we must make efforts to contribute back.
• The other projects that I have done using machine learning:
  1. Handwritten digit recognition [VIDEO],
  2. Alphabet recognition [VIDEO],
  3. Apparel classification [VIDEO],
  4. Devnagiri recognition [VIDEO].

With each project, I have tried to apply one new feature or the other to make my model a bit more efficient. Hyperparameter tuning or just cleaning the data.

In this demonstration, I would just like to point out that knowledge never goes to waste. The small computer vision applications that I built in my college has helped me to gain deep learning computer vision task. It's always enlightening and empowering to learn new technologies.

I recently was part of a session on ‘Solving real world applications from Machine learning’ to Microsoft Advanced Analytics User Group of Belgium as well as broadcasted across the globe (Meetup Link) [Session Recording]

By now it is evident that a solid math foundation is indispensable if one has to get into Data science in an honest-to-goodness way. Unfortunately, for many of us math was just a means to get better scores at school-level and never really a means to understand the world around us.
That systemic failure (education system) causes many of us to feel a “gap” when learning data science concepts. It is high time that we acknowledge that gap and take remedial action.

The purpose of the workshop is to develop an intuitive understanding of the concepts.
We let go the fear of rigorous notation and embrace the rationale behind it.
The intended key take away for participants is confidence to deal with math.

Reinforcement Learning algorithms becoming more and more sophisticated every day which is evident from the recent win of AlphaGo and AlphaGo Zero (https://deepmind.com/blog/alphago-zero-learning-scratch/ ). OpenAI has provided toolkit openai gym for research and development of Reinforcement Learning algorithms.

In this workshop, we will focus on introduction to the basic concepts and algorithms in Reinforcement Learning and hands on coding.

Content

• Introduction to Reinforcement Learning Concepts and teminologies
• Setting up OpenAI Gym and other dependencies
• Introducing OpenAI Gym and its APIs
• Implementing simple algorithms using couple of OpenAI Gym Environments
• Demo of Deep Reinforcement Learning using one of the OpenAI Gym Atari game

Linear Algebra, Optimization & Statistics is base of all machine learning. This workshop will cover required linear algebra for machine learning in a hands on way through short code examples. We will cover basic theory, interesting applications and the big picture.

Python ecosystem for Natural language processing has evolved in last decade and rich set of open source tools and data sets are now available.

In this session, we will go over basics of Natural language processing along with sample code demonstration and hands on tutorials using following famous python libraries,

1. NLTK : One of the oldest and famous library for natural language analysis for researchers
2. Stanford CoreNLP : Production ready NLP library. (Written in java but has many open source python wrappers)
3. SpaCy: Comparatively new python NLP toolkit marketed as 'Industrial Strength' python library.

Session will introduce the various use cases and basic concepts related to the natural language processing with demo and hands on tutorials

Following NLP fundamentals will be discussed,

- Syntax Vs. Semantics

- Regular Expressions (Demo and Hands on)

- Word Embeddings (Demo and Hands on)

- Word Tokenization (Demo and Hands on)

- Part of Speech Tagging (Demo and Hands on)

- Text Similarity (Demo and Hands on)

- Text Summarization

- Named Entity Recognition ((Demo and Hands on))

- Sentiment Analysis (Demo and Hands on)

Data science refers to the science of extracting useful information from data. Knowledge discovery in data bases, data mining, Information extraction also closely match with data science. Supervised learning,Semi supervised learning,Un supervised learning methodologies are out of Academia and penetrated deep into the industry leading to actionable insights, dashboard driven development, data driven reasoning and so on. Data science has been the buzzword for last few years in industry with only a handful of data scientists around the world. The industry needs more and more data scientists in future to solve problems using statistical techniques. The exponential availability of unstructured data from the web has thrown huge challenges to data scientists to exploit them before driving conclusions.

Now that's overload of information and buzzwords. It all has to start somewhere? Where and how to start? How to get hands dirty rather than just reading books and blogs. Is it really science or just code?. Let's get into code to talk data science.

In this workshop i will show the tools required to do real data science rather than just reading by building real models using Deep neural networks and show live demo of the same. Also share some of the key data science techniques every aspiring data scientist should have to thrive in the industry.

Training a machine learning / deep learning model is one thing and deploying it to a production is completely different beast. Not only you have to deploy it to a production, but you will have to retrain the model every now and then and redeploy the updates. With many machine learning / deep learning projects / POCs running in parallel with multiple environments such as dev, test prod, managing model life cycle from training to deployment can quickly become overwhelming. In this talk, I will discuss an approach to handle this complexity using Docker and Python. Rough outline of the talk is,

• Introduction to the topic
• Problem statement
• Quick introduction to Docker
• Discussing the proposed architecture
• Alternative architecture using AWS infrastructure
• Demo