E-learning and ed-tech startups are becoming a rage as more and more people are developing interest in new forms of education involving innovation. This change is productive, crucial and able to break the geological boundaries for gaining quality education. The rising number of COVID-positive cases has made it very clear that without social distancing, it will be very difficult to control the spread of this deadly virus. So, in order to overcome the challenges faced by educational institutions, online teaching tools are now being implemented across different levels of education among many schools and universities in the world. Digitization of education is viewed as a need of the hour in India, where not many institutions are equipped with the right tools nor do they have the specialized aptitudes to make the learning environment technologically advanced.

Recently, I actively started looking for a job change to Data science, and I don't have any formal education like a Master's or Ph.D. background in AI/Machine Learning. I started learning it completely out of my own interest (not just because of the hype). It was one of the challenging tracks to opt-in, especially if you are working simultaneously on some other technology. I started my journey by enrolling myself in many MOOCs(Massive Open Online Courses) and started reading multiple blogs. It slowly started making sense.

Artificial Intelligence (AI) is evolving at an exponential rate. Today, it has expanded beyond tech and geographical constraints and is slowly bringing massive changes worldwide. In recent times, AI influencers are driving conversations about AI news and trends across social media and beyond while also offering advice to numerous enterprises. Plus, they also help us keep updated with the recent innovations and information about AI. Analytics Insight brings 10 LinkedIn influencers who share the latest trends in the AI domain through insightful articles on their LinkedIn blogs.

This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.

Online Courses Udemy - Learn Incredible Website Design, Development Tools and Platforms using Artificial Intelligence (AI) Technology Created by Srinidhi Ranganathan English [Auto-generated] Students also bought XML and XSD: a complete W3C-content based course ( 10 hours) The Complete Web Developer Masterclass: Beginner To Advanced HTML & CSS - Certification Course for Beginners Start to finish - Creating a complete game using Unity3D ML for Business Managers: Build Regression model in R Studio Preview this course GET COUPON CODE Description What is this Website Design course all about? Website design is the art and science of building the look, feel, and how a website functions in a nutshell. This course is having a clear, concise, and easy to use website design technologies and will ultimately lead to better user experience for your custom audience or clients. There are many aspects of successful website design like HTML, colors, layouts, text size, graphics, and so much more. But, this course is a huge differentiator in the design field as it uses artificial intelligence-based website design state-of-the-art technologies which is covered nowhere in the world.

Data Science is a big deal these days! So it stands to reason that you might want to learn it because of its amazing potential and popularity in the technical market. But you don't need to spend thousands of dollars on getting a university degree to learn Data Science. It's even predicted that "armchair data scientists" who don't have any formal qualifications in Data Science but the skills to analyze data will become even more popular than "traditional data scientists". So you can easily learn the basics of Data Science from online courses and then build upon those basics by practice.

Offered by DeepLearning.AI. About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.

We study the problem of online learning with dynamics, where a learner interacts with a stateful environment over multiple rounds. In each round of the interaction, the learner selects a policy to deploy and incurs a cost that depends on both the chosen policy and current state of the world. The state-evolution dynamics and the costs are allowed to be time-varying, in a possibly adversarial way. In this setting, we study the problem of minimizing policy regret and provide non-constructive upper bounds on the minimax rate for the problem. Our main results provide sufficient conditions for online learnability for this setup with corresponding rates. The rates are characterized by 1) a complexity term capturing the expressiveness of the underlying policy class under the dynamics of state change, and 2) a dynamics stability term measuring the deviation of the instantaneous loss from a certain counterfactual loss. Further, we provide matching lower bounds which show that both the complexity terms are indeed necessary. Our approach provides a unifying analysis that recovers regret bounds for several well studied problems including online learning with memory, online control of linear quadratic regulators, online Markov decision processes, and tracking adversarial targets. In addition, we show how our tools help obtain tight regret bounds for a new problems (with non-linear dynamics and non-convex losses) for which such bounds were not known prior to our work.

Online Courses Udemy - Data Analytics: SQL for newbs, beginners and marketers, Dominate data analytics, data science, and big data Created by Lazy Programmer Inc English [Auto-generated] Students also bought Data analyzing and machine learning Hands-on with KNIME Machine Learning Practical: 6 Real-World Applications Careers in Data Science A-Z Statistics Masterclass for Data Science and Data Analytics Text Mining and Natural Language Processing in R Preview this course GET COUPON CODE Description It is becoming ever more important that companies make data-driven decisions. With big data and data science on the rise, we have more data than we know what to do with. One of the basic languages of data analytics is SQL, which is used for many popular databases including MySQL, Postgres, SQLite, Microsoft SQL Server, Oracle, and even big data solutions like Hive and Cassandra. I'm going to let you in on a little secret. Most high-level marketers and product managers at big tech companies know how to manipulate data to gain important insights.

Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.