Sreedath Panat’s Post

"Foundations for Machine Learning: New Course Launch" In 2022, I graduated with a PhD in Mechanical Engineering from MIT. Although a big component of my research was purely hands-on experiments, my exposure to foundational graduate-level ML courses at MIT, research courses, and Scientific Machine Learning via Julia gave me the confidence of a Machine Learning researcher. I incorporated ML into my research, and it solved a problem that is otherwise difficult to solve theoretically or experimentally. When I co-founded Vizuara AI Labs with Raj and Rajat, our core principles were depth and foundational knowledge. Now I have co-authored multiple AI-ML research papers and two of them are accepted to the upcoming NeurIPS workshop. Behind all of this effort, there is the confidence that stems from knowing what happens underneath the ML algorithms. Most of the online courses have little emphasis on fundamentals. People are so used to spending time on toy Kaggle projects. Very few people I know can build a neural network from scratch or explain what happens behind them. For the last 4 months, I have been working to launch a new course on Vizuara's YouTube channel on "Foundations for Machine Learning." This will be a 45-hour course with ~65 lectures. I will be hosting all lectures on this playlist: https://lnkd.in/gazvPG_b My singular goal with this course is to teach you the entire foundations required to learn ML from scratch. There are no prerequisites. If you have basic logical thinking capability and a willingness to dedicate time, consistently, you can follow this course. I have split the course into 5 modules. 1) First I will cover the 4 mathematical pillars of ML: Linear Algebra, Probability, Statistics, and Calculus. 2) In the second module I cover the basic programming fundamentals for a complete beginner. I will teach you Python from scratch and it's some of the most important packages for ML including NumPy and PyTorch. 3) In the 3rd module we will learn about optimization and gradient descent. I wanted to dedicate an entire module to optimization because when you actually build ML models, you will be spending a lot of time on optimization. 4) In the 4th module, I will give you an overview of the AI landscape. What happened from 2010-2020 and what does it look like from 2020-2030? This overview will help you understand overall where ML, DL, NLP, CV, and GenAI are heading. 5) In the final module, I will cover the most important 2 steps you will have to master as a Data Scientist or ML engineer: processing data and communication via storytelling. I will teach you some of the most powerful preprocessing and visualization techniques. I have already published the first lecture. Check out here. I am sure you will enjoy and learn a lot: https://lnkd.in/gj3CX2Eh -Dr. Sreedath Panat Team Vizuara

useful synopsis

2 days ago, there was a very popular Reddit post written by an engineer who transitioned to top ML jobs in at companies like Amazon, Apple, Meta and Netflix. The post explained the resources you need for transitioning to serious ML roles. I found that post very informative. Sharing it here with my own learnings: All these resources are free. Part 1: Fundamentals 1️⃣ Linear Algebra – Blue1Brown's playlist on YouTube is the most engaging Linear Algebra playlist. Link: https://lnkd.in/gJMe89Hz 2️⃣ Multivariable Calculus – Khan Academy's Multivariable Calculus lessons are an amazing refresher. Link: https://lnkd.in/gdkzPvpj 3️⃣ Statistics and Probability – Please watch the Youtube playlist by the channel StatsQuest. Unique style and awesome content. Link: https://lnkd.in/gxRgRw_A Part 2: Machine Learning 1️⃣ Stanford Intro to Machine Learning by Andrew Ng. The course also offers a free set of course notes, which are very well written. Link: https://lnkd.in/gtPxYRjC 2️⃣ Caltech Machine Learning – These are less mathematical and more intuition based. Link: https://lnkd.in/gz8A_Qkh 3️⃣ Vizuara AI's Machine Learning Teach by Doing: 35 lectures. Whiteboard + coding approach. I have spent 2 months of hard work and am very proud of these lectures. Link: https://lnkd.in/gbUDy46Y Part 3: Deep Learning 1️⃣ Andrej Karpathy's Zero to Hero Series – Andrej Karpathy, an AI researcher who graduated with a Stanford PhD and led Tesla AI for several years, released an amazing series of hands on lectures on YouTube. Link: https://lnkd.in/gqdfEnfK 2️⃣ Vizuara AI Neural networks from scratch series: 35 lectures. Whiteboard + coding approach. I have spent 2 months of hard work and am very proud of these lectures. Link: https://lnkd.in/gRMnQt64 Part 4: Transformers and LLMs 1️⃣ University of Waterloo lectures: This will give you lots of insights into how Transformers work. Link: https://lnkd.in/gNuVUe7j 2️⃣ Vizuara AI Building Transformers series: One of the best resources to understand transformers from scratch. Link: https://lnkd.in/gpcZAwgd 3️⃣ ChatGPT Explainer: Wolfram's YouTube explainer video on ChatGPT is one of the best videos to watch. Link: https://lnkd.in/gwzh3NZd 4️⃣ Interactive LLM Visualization – This LLM visualization that you can play with in your browser is hands down the best interactive experience with an LLM. Link: https://bbycroft.net/llm Hope this is useful!

Very comprehensive source for learning AI ... Kudos to Raj Abhijit Dandekar. Those who are logically minded - one of the best ways to understand AI deeply is from its root - mathematics - Linear Algebra and Calculus. Like many of you - I too learned these math concepts in a very dry way. For example - never thought matrix multiplication would be useful in this way, the rules to multiply two rectangle of numbers - appeared very arbitrary. Never thought - Taylor's series can be used to approximate a function which will be used ML, partial derivatives will be used to minimising the error between actual and expected. AI offered the opportunity to study Maths in practical sense.

Jumpstart Your AI Engineering Journey in 2024 In the fast-paced world of tech, AI engineering stands out as a field brimming with potential. Here's your quick guide to getting started: The AI Landscape - AI isn't just about robots. It's a diverse field encompassing machine learning, natural language processing, and computer vision. From healthcare to finance, AI is reshaping industries and our daily lives. First Steps - Sharpen Your Tools Brush up on math: linear algebra, calculus, and statistics Get cozy with Python – it's the go-to language for AI - Choose Your Path University programs in AI or data science Online courses: Coursera, edX, Udacity Bootcamps for intensive learning - Essential Skills Languages: Python, R, Java Frameworks: TensorFlow, PyTorch, Keras Tools: Jupyter Notebook, Google Colab - Learning Resources Books: "Artificial Intelligence: A Modern Approach" by Russell and Norvig Online: Andrew Ng's courses on Coursera Practice: Kaggle competitions, personal projects - Network and Grow Join communities: Reddit's r/MachineLearning, Stack Overflow Attend conferences: NeurIPS, ICML Follow research: arXiv, AI research labs - Stay Ahead Keep up with ethical AI developments Continuous learning is key in this rapidly evolving field Remember, AI engineering is more than just coding – it's about solving real-world problems. Stay curious, keep practicing, and you'll be well on your way to shaping the future of technology. https://lnkd.in/ggy6uBeF

These are excellent learning resources.

Prepare yourself for live-long learning as a Data Scientist.

In the past few years I have completed a number of hands on courses on applied mathematics (linear algebra, statistics, calculus) and computer coding (python) that form the modern foundation of machine learning (ML), deep learning(DL) and artificial intelligence(AI) techniques that we all hear so often about these days. I am a neuroscientist so I learn these tools to answer questions about the brain. As with all learning, I always have something in mind, a problem that I need to address, a problem that is too dauntingly difficult to solve with current tools available. This repost contains links to some amazing courses online that tackle from scratch the basis of mathematical and coding blocks that built the intuition into designing, deploying and applying ML-DL-AI models to answer questions about the structure and function of the brain. Be brave, dive deeply, have patience, have fun, there is a long trip ahead for those who want to engage... knowledge does come for free...you will however pay the price with your time

A few weeks ago, there was a very popular Reddit post written by an engineer who transitioned to top ML jobs in at companies like Amazon, Apple, Meta and Netflix. The post explained the resources you need for transitioning to serious ML roles. Raj made a post about it which got my attention, so I’m resharing it here. Thanks for the gem! All these resources are free. Part 1: Fundamentals 1️⃣ Linear Algebra – Blue1Brown's playlist on YouTube is the most engaging Linear Algebra playlist. Link: https://lnkd.in/gJMe89Hz 2️⃣ Multivariable Calculus – Khan Academy's Multivariable Calculus lessons are an amazing refresher. Link: https://lnkd.in/gdkzPvpj 3️⃣ Statistics and Probability – Please watch the Youtube playlist by the channel StatsQuest. Unique style and awesome content. Link: https://lnkd.in/gxRgRw_A Part 2: Machine Learning 1️⃣ Stanford Intro to Machine Learning by Andrew Ng. The course also offers a free set of course notes, which are very well written. Link: https://lnkd.in/gtPxYRjC 2️⃣ Caltech Machine Learning – These are less mathematical and more intuition based. Link: https://lnkd.in/gz8A_Qkh 3️⃣ Vizuara AI's Machine Learning Teach by Doing: 35 lectures. Whiteboard + coding approach. I have spent 2 months of hard work and am very proud of these lectures. Link: https://lnkd.in/gbUDy46Y Part 3: Deep Learning 1️⃣ Andrej Karpathy's Zero to Hero Series – Andrej Karpathy, an AI researcher who graduated with a Stanford PhD and led Tesla AI for several years, released an amazing series of hands on lectures on YouTube. Link: https://lnkd.in/gqdfEnfK 2️⃣ Vizuara AI Neural networks from scratch series: 35 lectures. Whiteboard + coding approach. I have spent 2 months of hard work and am very proud of these lectures. Link: https://lnkd.in/gRMnQt64 Part 4: Transformers and LLMs 1️⃣ University of Waterloo lectures: This will give you lots of insights into how Transformers work. Link: https://lnkd.in/gNuVUe7j 2️⃣ Vizuara AI Building Transformers series: One of the best resources to understand transformers from scratch. Link: https://lnkd.in/gpcZAwgd 3️⃣ ChatGPT Explainer: Wolfram's YouTube explainer video on ChatGPT is one of the best videos to watch. Link: https://lnkd.in/gwzh3NZd 4️⃣ Interactive LLM Visualization – This LLM visualization that you can play with in your browser is hands down the best interactive experience with an LLM. Link: https://bbycroft.net/llm Hope this is useful! Credits to: Justin Hodges, PhD #ai #machinelearning #datascience #engineering #llm

Learning AI technology with no prior experience can be challenging but is definitely achievable with the right resources and approach. Here's a step-by-step guide on how you can get started: Build a strong foundation in mathematics and programming: Mathematics: Familiarize yourself with linear algebra, calculus, probability, and statistics, as these are fundamental concepts in AI. Programming: Learn a programming language like Python, which is widely used in AI and machine learning. Resources like Python for Everybody and Automate the Boring Stuff with Python are great for beginners. Learn about machine learning and AI concepts: Take an introductory course on machine learning, such as Andrew Ng's Machine Learning course on Coursera or the Machine Learning course on edX. Read books like "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron or "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig. Practice with hands-on projects: Explore machine learning libraries like scikit-learn, TensorFlow, and Keras. Work on projects from platforms like Kaggle or Analytics Vidhya, which offer datasets and competitions to practice on. Build your own projects and experiment with different techniques and datasets. Specialize in specific domains: Once you have a solid foundation, you can specialize in areas like computer vision, natural language processing, reinforcement learning, or deep learning. Take advanced courses or read specialized books and research papers in your chosen domain. Stay updated and participate in the community: Follow AI and machine learning blogs, podcasts, and social media channels to stay updated with the latest developments. Participate in online forums like Stack Overflow, Reddit's Machine Learning community, and Kaggle Discussions. Attend local meetups, conferences, or online events to connect with other enthusiasts and professionals. Consider online resources and certifications: Explore online courses and certifications from platforms like Coursera, edX, Udacity, and Deeplearning.ai to demonstrate your skills and knowledge. Remember, learning AI is a continuous process, and it's essential to keep practicing and staying up-to-date with the latest developments in the field. Don't hesitate to reach out to the AI community for guidance and support throughout your learning journey.