If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Analytics Vidhya was created with a mission to create the next generation data science ecosystem in India. Today, we help millions of people and stream millions of hours of videos every month. Tens of thousands of people participate in our hackathons every weekend and thousands of people are finding meaningful career opportunities through our portal today. We couldn't thank our community members enough for this. I could not have imagined this impact when we started Analytics Vidhya.
Data Retrieval is one of the most basic skills which people forgo. A lot of business analytics professional does not know how to proceed. Structured Query Language(SQL) makes the job easy. Just a couple of lines of codes will help you get 1000's of rows of data. Also, not only does this give examples for syntax in MySQL but in multiple languages such as Oracle, PostgreSQL, etc. The concepts of database and database management are explained in a beginner-friendly manner with practical examples. But the name suggests the best feature of the book- one has to spend just 10 minutes regularly to become an expert.
You've built your machine learning model – so what's next? You need to evaluate it and validate how good (or bad) it is, so you can then decide on whether to implement it. That's where the AUC-ROC curve comes in. The name might be a mouthful, but it is just saying that we are calculating the "Area Under the Curve" (AUC) of "Receiver Characteristic Operator" (ROC). I have been in your shoes.
Just when I thought TensorFlow's market share would be eaten by the emergence (and rapid adoption) of PyTorch, Google has come roaring back. TensorFlow 2.0, recently released and open-sourced to the community, is a flexible and adaptable deep learning framework that has won back a lot of detractors. I love the ease with which even beginners can pick up TensorFlow 2.0 and start executing deep learning tasks. There are a plethora of offshoots that come with TensorFlow 2.0. You can read about them in this article that summarizes all the developments at the TensorFlow Dev Summit 2020.
Google Colab is an amazing gift to the data science community from the fine folks at Google. Colab gives us the ability to build complex and heavy machine learning and deep learning models without having to expend our machine's limited resources. I can certainly appreciate this given how much I used to struggle on my machine! The'out of memory' error is now quite infamous in the data science community – Google Colab provides us with the workaround and adds several cherries on top! I love the free GPU and TPU support – it's simply unparalleled and unrivaled in any other coding IDE.
The'SHapley Additive exPlanations' Python library, better knows as the SHAP library, is one of the most popular libraries for machine learning interpretability. The SHAP library uses Shapley values at its core and is aimed at explaining individual predictions. But wait – what are Shapley values? Simply put, Shapley values are derived from Game Theory, where each feature in our data is a player, and the final reward is the prediction. Depending on the reward, Shapley values tell us how to distribute this reward among the players fairly. We won't cover this technique in detail here, but you can refer to this excellent article explaining how Shapley values work: A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values! The best part about SHAP is that it offers a special module for tree-based models. Considering how popular tree-based models are in hackathons and in the industry, this module makes fast computations, even considering dependent features.
So you've built your machine learning or deep learning model. That final stage – the crucial cog in your machine learning or deep learning project – is model deployment. You need to be able to get the model to the end user, right? And yet you'll face a ton of questions about model deployment when you sit for data scientist interviews! What are the different tools for model deployment?
Can you explain what is underfitting and overfitting in the context of machine learning? Describe it in a way even a non-technical person will grasp. Your ability to explain this in a non-technical and easy-to-understand manner might well decide your fit for the data science role! Even when we're working on a machine learning project, we often face situations where we are encountering unexpected performance or error rate differences between the training set and the test set (as shown below). How can a model perform so well over the training set and just as poorly on the test set?
The third edition of DataHack Summit organised by Analytics Vidhya is going on in Bengaluru to highlight the latest trends, state of the art developments and machine learning frameworks to democratize AI for everyone. The four-day summit, which kicked off yesterday, has been a strategic platform for industries and technologies worldwide to understand and leverage the latest innovations and the impact they have on our businesses and daily lives. The first day of the conference witnessed 20 sessions by leading AI experts like Eric Weber (ListReports), Mathangi Sri (PhonePe), Ujjyaini Mitra (Zee5), Dr Vikas Agrawal (Oracle Analytics Cloud), Abhishek Khanna, Jayatu Sen Chaudhury (American Express) et al. including 8 live hack sessions by Sudalai Rajkumar (H2O.ai), The panel had Kunal Jain (Analytics Vidhya), Dr Om Deshmukh (Yodlee), AviPatchava (Bright Money), Eric Weber (List Reports) and Tarry Singh (DeepKapha.ai, Kunal Jain, Founder and CEO, Analytics Vidhya, said, "It's exciting to see more than 1000 passionate data science professionals from diverse industries and domains (more than 500 organizations) coming together and building a vibrant Data Science ecosystem in India. Think of this group as a think tank powering hundreds of banks, every ecommerce player and travel portal in the country building algorithms to improve customer experience and deliver business value."
Bengaluru: Bengaluru Analytics Vidhya's third edition of DataHack Summit, India's Largest Conference on Applied Artificial Intelligence and Machine Learning will be held from 13 – 16 November 2019 at NIMHANS Convention Centre, Bengaluru. Global AI Leaders, Researchers, Machine Learning Experts, Data Scientists, Analysts and Engineers will be attending the summit to spark discussions on Machine Learning, Artificial Intelligence, Reinforcement Learning, Natural Language Processing, Generative Modeling, Computer Vision, Explainable AI, Cloud Computing, Deep Learning, Transfer Learning, Quantum Computing, and Speech Recognition. There will be a more than 1000 AI & ML professionals will be attending 8 workshops, 30 hack sessions and 70 talks. The conference will witness speakers including Dr. GeethaManjunath (Founder & CEO of NIRAMAI), SayanRanu (IIT Delhi), Dat Tran (Head of AI at Axel Springer Ideas Engineering), UjjyainiMitra (Head of Data, ZEE5), XanderSteenbrugge (Head of applied ML-research at ML6), Prateek Jain (Microsoft), Jayatu Sen Chaudhury (American Express), Nishant Agrawal (Intel), Dr. Vikas Agrawal (Oracle Analytics Cloud), Dr. HarshadKhadilkar (TCS) and 100 more experts sharing their views on the impact of Artificial Intelligence and Machine Learning. According to Kunal Jain, " Analytics Vidhya's mission is to build next-gen data science ecosystem and with DataHack Summit 2019 – we aim to bring together people, machines and their collaborative experience to make our world data-driven! After the immense success of the DataHack Summit 2018, the Summit has become bigger and will go deeper on the subject. With more than 70 sessions from experts across the globe spread across 4 days – there cannot be a better place to learn about Artificial Intelligence, Machine Learning and Deep Learning."