How to study Machine Learning quickly before an Interview?

If you follow me on LinkedIn, you will often see me sharing interview questions related to Machine Learning, Deep Learning, Statistics, Data Science, along with sharing other goodies. But what if you do not have the time to go through those questions and answers?

How not knowing Conda Environments makes You a Rookie

I'll be honest with you, when I started my journey as a Machine Learning Engineer I never knew how to use a Conda Environment to my advantage, or why I should use it at all! Until I learned about it at work, and now I cannot live without it. Why? For a peaceful life! Trust me when I say this, you too would be relieved to know about what the Conda Environment does, and how you can use it to solve real life problems. So, here's to global peace!

Learn DevOps for Free during January 2021!

Earlier this month, CloudxLab started a great initiative called #NoPayJan. They made some of their most popular courses completely free of cost, just for the month of January 2021!

Bring your Machine Learning ideas to life with Microsoft, with zero coding (almost)

The internet is full of advice on how you can start your Machine Learning journey, how you need to spend years mastering the mathematics behind Machine Learning before you can actually start learning it, and even then how difficult it is to learn. Do not believe those naysayers.

As each day passes by, we are taking baby steps to ensure that Machine Learning is accessible to everyone, no matter their educational background. Because more than the mathematics, Machine Learning needs ideas. And latest in this path to make Machine Learning accessible to everyone, we have an offering from Microsoft; Lobe.

Creating UI for your ML model using Gradio

More often than not, you will have a Machine Learning course showing you how to prepare the data, create the model, and evaluate it. You'll have a number in the end, it can be anything from RMSE to loss, accuracy, to tell you how your model performed, and that would be the end of it. But that is not the end of it, because you have another equally important step left, deployment. Now, you may not actually deploy your model to production, but you can definitely test it by creating an UI for others to be able to use it.

Will Programmers become obsolete in future because of this Jupyter extension?

Since the time GPT-3 arrived on the scene, there have been talk about whether programmers will becomes obsolete in future. This might seem like a distant dream, but Kartik Godawat and Deepak Rawat has already transformed this into somewhat of a reality.

Online Course on Introduction to R Programming and Data Analytics by IIT Kharagpur

The Vinod Gupta School of Management, Indian Institute of Technology (IIT), Kharagpur is organizing an online, short term, weekend course on Introduction to R Programming and Data Analytics from November 7 to 27, 2020. Anyone can enroll for this course, and since this course will be conducted during the weekends it would be easier for someone who is working during the weekdays, or have regular classes, to be able to complete this course without disturbing their regular schedule.

Will QLattice ever rule the World?

To answer that question, yes, or atleast I hope so. Because that would mean making life so much easier for Machine Learning Engineers, enthusiasts, and beginners equally. How? Well, I'll tell you that in a bit. But first, let me tell you what is QLattice.

New Language Model Outperforms GPT-3 with Lesser Parameters

GPT-3 has been turning heads with it's extraordinary 175 Billion parameters performance, so much so that Microsoft partnered with OpenAI for an exclusive license to it. But recently, in a paper published on arXiv by Timo Schick and Hinrich Schutze from the Center for Information and Language Processing (Ludwig Maximilian University, Munich), it was shown that the same performance can be achieved by a language model using only a tiny fraction of those many parameters.

Learn Image Stitching with OpenCV through this Hands-on Project

So what is Image Stitching?
According to Wikipedia, image stitching or photo stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama or high-resolution image. Here's what it looks like:

How much Math is required for Machine Learning?

This is a question that plagues thousands of learners of Machine Learning, how much math do we need to learn before we can start working on a Machine Learning project? I have seen learners practicising their mathematics skills for months before giving up, and other who quit without even trying because they didn't take up math during college. Don't let that happen to you.

A Foolproof Guide to Python Tutorials

The first thing that you will need to begine your Machine Learning journey is a solid programming background. But then, you must be wondering, "What does he mean by programming background? Do I need to learn the inside out of a programming language? Any language? Will FORTRAN do the work, cause I picked it up in high school?" Let me answer them, one at a time.

Microsoft gets Exclusive GPT-3 License from OpenAI

Sometime in mid-July last year, Microsoft announced it would invest $1 billion in the San Francisco-based artificial intelligence research company OpenAI. Now, almost a year later, Microsoft broadened the scope of this multi-year partnership by getting an exclusive license of OpenAI's groundbreaking GPT-3 model.

All You Need to Know about Data Mining Pipelines

Data mining is a process of collecting, cleaning, processing, analyzing and gaining useful insights of the data.
In present era, almost all automated processes like financial modelling, IoT, recommendation in retail marketing, use data. Data is mostly available in unstructured form that means it’s not possible to gain any insight from the data directly. That’s why data mining is needed to extract useful information from data. Through data mining, data is converted into one structured format so that it can be used more efficiently.

23 Best Machine Learning Books for 2020

Due to the Covid-19 lockdown, most of us have turned into autodidacts. Which is good since right now we have lot of time in our hands, and learning something new would be very helpful for our career. So, I thought why not curate a list of books that would help you learn Machine Learning. So, here's a list of 23 best Machine Learning books for 2020. Most of these books are available in the author's website for free, I have given the link to those too. Happy learning!

1. Deep Learning Book
- by Aaron Courville, Ian Goodfellow, and Yoshua Bengio
This book is freely available here.

2. The Elements of Statistical Learning
- by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
This book is freely available here.

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- by Aurelien Geron

4. Pattern Recognition and Machine Learning
- by Christopher Bishop
This book is freely available here.

5. An Introduction to Statistical Learning: With Applications in R
- by Gareth M. James, Trevor Hastie, Daniela Witten, Robert Tibshirani
This book is freely available here.

6. The Hundred-Page Machine Learning Book
- by Andriy Burkov

7. Understanding Machine Learning: From Theory to Algorithms
- by Shai Ben-David and Shai Shalev-Shwartz
This book is freely available here.

8. Mathematics for Machine Learning
- by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth
This book is freely available here.

9. Data Science from Scratch
- by Joel Grus

10. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
- by Cameron Davidson-Pilon
This book is freely available here.

11. Python Data Science Handbook: Essential Tools for Working with Data
- by Jake VanderPlas
This book is freely available here.

12. Think Stats
- by Allen B. Downey
This book is freely available here.

13. Grokking Deep Learning
- by Andrew W. Trask
This book is freely available here.

14. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
- by Pedro Domingos

15. Foundations of Machine Learning
- by Afshin Rostamizadeh, Ameet Talwalkar, and Mehryar Mohri
This book is freely available here.

16. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
- by Peter Flach

17. Bayesian Reasoning and Machine Learning
- by David Barber
This book is freely available here.

18. Machine Learning for Hackers
- by Drew Conway and John Myles White

19. Introduction to Machine Learning with Python: A Guide for Data Scientists
- by Andreas C. Müller and Sarah Guido

20. Introduction to Machine Learning
- by Ethem Alpaydın

21. Machine Learning: A Probabilistic Perspective
- by Kevin P. Murphy

22. Deep Learning with Python
- by François Chollet

23. Artificial Intelligence: A Modern Approach
- by Peter Norvig and Stuart J. Russell

Machine Learning Roadmap 2019

With so many resources available online, people often gets confused as to where to start their journey on Machine Learning. And then there are those who are not from a Mathematics or CS background, they love to stick to creating models in their browsers. But if you want to build a career in Machine Learning, you cannot escape the clutches of Mathematics or programming. What to do then? Simple, follow this Machine Learning roadmap, use it as a guide, or as a stepping stone to build your career in Machine Learning.

250+ Machine Learning and Deep Learning Resources (Courseware and Lecture Videos)

Whether you are starting with Machine Learning or Deep Learning, or you are a Data Scientist looking into a specific field of study, gathering resources is a pain. Wouldn't it be great if you had all the resources at your fingertips?