Some programming experience Be comfortable with coding in Python Windows/Linux/MAC machine Desire to learn data science Nothing else! It's just you, your computer and your ambition to get started today Nothing else! It's just you, your computer and your ambition to get started today Are you ready to start your path to becoming a Data Scientist or ML Engineer? This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This is a massive 4-in-1 course covering: 1) Vector models and text preprocessing methods 2) Probability models and Markov models 3) Machine learning methods 4) Deep learning and neural network methods In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Document retrieval / search engine Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.
ML from scratch is a student-led tutorial / seminar series initiated by Johannes Bill and others from Jan Drugowitsch Lab at Harvard Medical School. The objective is to teach neuroscience students to learn cutting edge machine learning models by implementing them. I started participating from 2022, and I prepared the tutorial and led a few seminars in it!
Open AI has trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using just a small amount of labeled contractor data. With a bit of fine-tuning, the AI research and deployment company is confident that its model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Its model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents. A spokesperson for the Microsoft-backed firm said: "The internet contains an enormous amount of publicly available videos that we can learn from. You can watch a person make a gorgeous presentation, a digital artist draw a beautiful sunset, and a Minecraft player build an intricate house. However, these videos only provide a record of what happened but not precisely how it was achieved, i.e. you will not know the exact sequence of mouse movements and keys pressed. "If we would like to build large-scale foundation models in these domains as we've done in language with GPT, this lack of action labels poses a new challenge not present in the language domain, where "action labels" are simply the next words in a sentence." In order to utilise the wealth of unlabeled video data available on the internet, Open AI introduces a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT). The team begin by gathering a small dataset from contractors where it records not only their video, but also the actions they took, which in its case are keypresses and mouse movements. With this data the company can train an inverse dynamics model (IDM), which predicts the action being taken at each step in the video. Importantly, the IDM can use past and future information to guess the action at each step. The spokesperson added: "This task is much easier and thus requires far less data than the behavioral cloning task of predicting actions given past video frames only, which requires inferring what the person wants to do and how to accomplish it.
This course offers a deep and wide range of skills set from Programming to statistics and machine learning algorithms. The skills you will attain from this course could make you an expert Data Analyst, Quality Analyst and Business Analyst and Statistical Analyst roles. Machine learning algorithms such as Regression, Clustering, Classification and prominent libraries such as Pandas, Matplotlib, SciKit -learn is covered from this course. The main goal of the course is to provide a deeper understanding and hands-on learning experience on the Data Science domain with the help of Python programming language along with real-time Data Science projects to provide an overall knowledge on Data Science domain. This course covers all the topics from Mathematics to Programming to Visualization techniques that are needed for a Data Scientist role.
Having the skills and the knowledge to attack every aspect of point cloud processing opens up many ideas and development doors. It is like a toolbox for 3D research creativity and development agility. And at the core, there is this incredible Artificial Intelligence space that targets 3D scene understanding. It is particularly relevant due to its importance for many applications, such as self-driving cars, autonomous robots, 3D mapping, virtual reality, and the Metaverse. And if you are an automation geek like me, it is hard to resist the temptation to have new paths to answer these challenges! This tutorial aims to give you what I consider the essential footing to do just that: the knowledge and code skills for developing 3D Point Cloud Semantic Segmentation systems. But actually, how can we apply semantic segmentation? And how challenging is 3D Machine Learning? Let me present a clear, in-depth 201 hands-on course focused on 3D Machine Learning.
British-born Andrew Ng has had a rich career in the technology industry as Co-Founder and Head of Google Brain, former Chief Scientist at Baidu and Co-Founder of Coursera. At Baidu, Ng built the company's artificial intelligence (AI) sector into a team of several people. In an interview with Lex Fridman, Ng shared where his passion for the industry started: " Growing up in Hong Kong and Singapore, I started learning to code when I was five or six years old. At that time I was learning the BASIC programming language and they would take these folks and they'll tell you type this program into your computer." "So I typed out programs on my computer and as the result of all the typing, I would get to play these very simple, shoot them up games that I had implemented on my little computer. So I thought it was fascinating as a young kid that I could write this code. I was really just copying code from a book into my computer to then play these cool little video games. Another moment for me was when I was a teenager and my father was a doctor was reading about expert systems and about neural networks. So he got me to read some of these books and I thought it was really cool that you could write a computer that started to exhibit intelligence." he continued.
There has been a large number of courses that teach the fundamentals of programming and data science. They do a good job in reinforcing various concepts in machine learning and show various steps that are usually followed when building a project with ML capabilities. While these courses mostly focus on the theoretical aspects of machine learning, it can be handy if one learns to put more emphasis on the good practices when building applications related to data science and machine learning. With the rise in data and an exponential increase in the compute power, there has been a rapid increase in the demand for people who would make use of the data and generate predictions along with useful insights depending on the use case of the project. Furthermore, there are numerous data related positions such as data engineer, data architect, data scientists, deep learning engineer and machine learning engineer.