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Kartik Chaudhary offered expert AI/ML insights, detailed feedback, and practical improvement tips during an exceptional mock interview.

I have curated a comprehensive checklist of more than 300 concepts that are relevant for MLE interviews. If you prepare these topics, you will be able to crack any top company interview. In this interview guide (13 pages long PDF), you will find the checklists related to the following key concepts:<ol><li>Machine Learning Round</li><li>Deep Learning Round</li><li>ML System Design Round</li><li>Leadership Round</li></ol> Print it out and track your progress.

Crack your next MLE interview for FAANG or other top company

Machine Learning Engineer - Interview Preparation

This package contains two digital products: 1 - MLE Interview checklist of over 300 useful topics2 - A guide that will help you give your best during the interview.

ML Interview Preparation Kit

Giving a great interview is an Art! Trust me there is much more to an interview, apart from just answering questions. I have taken hundreds of Interviews for Google. I am sharing my experience with the help of this guide (11 pages PDF). This guide covers the following key topics:<ol><li>Common mistakes to avoid during an interview</li><li>Useful tips and tricks for an awesome interview</li><li>Extra tips for Coding Rounds (DSA rounds).</li></ol>

Master the Art of the Interview

Crack your next FAANG (or any other Top company) Interview!!

Following are the key topics of mock interviews:<ol><li>Coding (DSA) Round</li><li>AI/ML/DL Technical Round</li><li>ML system design Round</li></ol>

Choose one of the topics: AI/ML, ML System Design, Coding

Mock Interview - AI/ML/DSA

Ask away anything related to my expertise. I will try my best to help you out 🙂

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<h3>Key Features</h3><ul><li>Learn generative learning approach of ML and its key differences from the discriminative learning approach.</li><li>Understand why GANs are difficult to train, and key techniques to make their training stable to get impressive results.</li><li>Implement multiple variants of GANs for solving problems such as image generation, image-to-image translation, image super-resolution and so on.</li></ul> <h3>Who this book is for </h3>If you are a ML practitioner who wants to learn about generative learning approaches and get expertise in Generative Adversarial Networks for generating high-quality and realistic content, this book is for you. Starting from a gentle introduction to the generative learning approaches, this book takes you through different variants of GANs, explaining some key technical and intuitive aspects about them. This book provides hands-on examples of multiple GAN variants and also, explains different ways to evaluate them. It covers key applications of GANs and also, explains the adversarial examples. <h3>What will you learn</h3><ul><li>Learn about the generative learning approach of training ML models</li><li>Understand key differences of the generative learning approach from the discriminative learning approach</li><li>Learn about various generative learning approaches and key technical aspects behind them</li><li>Understand and implement the Generative Adversarial Networks in details</li><li>Learn about some key challenges faced during GAN training and two common training failure modes</li><li>Build expertise in the best practices and guidelines for developing and training stable GANs</li><li>Implement multiple variants of GANs and verify their results on your own datasets</li><li>Learn about the adversarial examples, some key applications of GANs and common evaluation strategies</li></ul> <h3>Book Description</h3>Generative Adversarial Networks have become quite popular due to their wide variety of applications in the fields of Computer Vision, Digital Marketing, Creative artwork and so on. One key challenge with GANs is that they are very difficult to train. This book is a comprehensive guide that highlights the common challenges of training GANs and also provides guidelines for developing GANs in such a way that they result in stable training and high-quality results. This book also explains the generative learning approach of training ML models and its key differences from the discriminative learning approach. After covering the different generative learning approaches, this book deeps dive more into the Generative Adversarial Network and their key variants. This book takes a hands-on approach and implements multiple generative models such as Pixel CNN, VAE, GAN, DCGAN, CGAN, SGAN, InfoGAN, ACGAN, WGAN, LSGAN, WGAN-GP, Pix2Pix, CycleGAN, SRGAN, DiscoGAN, CartoonGAN, Context Encoder and so on. It also provides a detailed explanation of some advanced GAN variants such as BigGAN, PGGAN, StyleGAN and so on. This book will make you a GAN champion in no time. <h3>Table of Contents</h3>1. Generative Learning2. Generative Adversarial Networks3. GAN Failure Modes4. Deep Convolutional GANs4(II). Into the Latent Space5. Towards stable GANs6. Conditional GANs7. Better Loss functions8. Image-to-Image Translation9. Other GANs and experiments9(II). Advanced Scaling of GANs10. How to evaluate GANs?11. Adversarial Examples12. Impressive Applications of GANs13. Top Research Papers

Train stable GANs using TensorFlow2, Keras and Python.

The GAN Book

In this call, I will address in detail any query you may have regarding your career, any obstacles you may be facing and help you gain clarity on further steps to take and decisions to make when it comes to your planning your career.

Career guidance

In this call, I will review your Resume through and through, help you on how to customise your resume according to the role you are applying for, suggest changes if needed, give you tips on how you can make your resume ATS friendly and solve any queries you might have related to your resume.

Resume review

Let's discuss your current project, and I can support with the technical and non-technical aspects of the project. My expertise lie in the following fields:<ul><li>Computer Vision</li><li>NLP</li><li>Deep Learning</li><li>Machine Learning</li><li>MLOps</li><li>GenAI</li></ul>

Project Advisory

Want to get on a 1-on1 call with me and have a brief discussion about what I do, how I can help you or talk about any brief query you have under the umbrella of interview prep? Book a call with me and I\'ll see you inside. The only reason to keep a nominal charge is to let me have a chance to speak with people who genuinely want to connect.

Quick coffee chat

Artificial Intelligence (AI) and Machine Learning (ML) professional with 7+ years of industry experience in developing ML & AI based solutions/products using both structured and unstructured data. 

Kartik loves sharing knowledge and runs his own blog on Machine Learning and Artificial Intelligence (Drops-of-AI). All the posts/blogs represent his own views/opinions and do not represent his employer in any way.

Kartik is currently working as a Senior AI/ML Engineer at Google to design AI solutions/products for Google's strategic enterprise customers, leveraging core Google products including TensorFlow, DataFlow, BQML, AutoML, KubeFlow, AI platform and Vertex AI. 

Prior to Google, Kartik has worked for UnitedHealthGroup (UHG) as a Data Scientist and helped in making the 'healthcare system work better for everyone' with the use of AI and ML techniques. During his time at UHG, he has filed multiple patents at the intersection of AI/ML/HealthCare and was recognised with the title of a 'Senior Inventor' in 2020.

Technical and Functional Capabilities:
• Artificial Intelligence || Machine Learning || Deep Learning || Generative Learning
• Statistics || Exploratory Data Analysis || Data Visualisation || Unsupervised Analysis
• Computer Vision || Natural Language Processing || Speech Recognition || OCR/HTR
• Stakeholder Management || AI Consulting || Architecting AI Solutions
• AI Researcher || Technical Content Writer || Open Source Contributor || Publications
• Tensorflow || Keras || PyTorch || SK-Learn || Kubernetes || KubeFlow || GCP || OpenCV || Docker
• Tensorflow-Serving || TFX || TFDV || TF-Records || Quantization || Distributed Training
• Python || C || C++ || Java || SQL || BQML || MLOps
• Google Cloud certified Professional Machine Learning Engineer