There's so much opportunity for students to learn AI -- and it doesn't have to be hard. Learning AI sounds daunting and until recently, it's been challenging for students to find enough guidance and support to build AI projects. There are great resources to help students learn coding languages, but how do students go from learning the basics of Python and Linux to building an autonomous robot (especially on a budget)? Guidelines to build with AI should be accessible to all. After all -- creativity and innovation happen when barriers are taken down, not added.

I read Modern Romance by Aziz Ansari in 2016 and beyond a shadow of a doubt, it is one of the most influential books I've ever read. At the time, I was a snot-nosed college student who was still dating someone from high school. The numbers and figures given by the book about online dating success struck me as being callous. Millennials and their predecessors were blessed and cursed with the advent of the internet. The proliferation of partner-choice desensitizes us and gives us unrealistic expectations when it came to searching for our "soulmate." Instead of feeling dissuaded, I felt inspired.

This blog was written by an independent guest blogger. Historically, the idea of artificial intelligence (AI) saturating our world has been met with suspicion. Indeed, it's one of the more popular tropes of science fiction -- learning machines gain sentience that helps them take over the planet. While we're not even slightly close to that dystopian reality, we have reached a point at which AI has been significantly integrated into various aspects of our society. While this isn't without its risks, largely from a security standpoint, there are huge benefits.

TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models. TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models. I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities. My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with the solid understanding of TensorFlow.

With advancing technology, there has been a drastic growth of technologies such as artificial intelligence and machine learning. More increasingly AI has become a driving force that is transforming the virtual world day by day. This has become thousands of start-ups coming up every single day based on AI or its AI tools ranging from Siri to auto-journalism. Everything is being operated with the help of AI and ML. And with AI entering all sectors it has also started to transform the educational sector which is traditional in nature. AI tools for education that are intelligent, adaptive, encouraging personalized learning systems are being deployed in all the educational institutions such as schools, colleges, and universities across the globe for analyzing huge amounts of data collected from the students that can significantly impact the lives of students and educators.

Learn how to solve real life problem using the Machine learning techniques Machine Learning models such as Linear Regression, Logistic Regression, KNN etc. Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc. Understanding of basics of statistics and concepts of Machine Learning How to do basic statistical operations and run ML models in Python Indepth knowledge of data collection and data preprocessing for Machine Learning problem How to convert business problem into a Machine learning problem

Handwritten character recognition (HCR) is a challenging learning problem in pattern recognition, mainly due to similarity in structure of characters, different handwriting styles, noisy datasets and a large variety of languages and scripts. HCR problem is studied extensively for a few decades but there is very limited research on script independent models. This is because of factors, like, diversity of scripts, focus of the most of conventional research efforts on handcrafted feature extraction techniques which are language/script specific and are not always available, and unavailability of public datasets and codes to reproduce the results. On the other hand, deep learning has witnessed huge success in different areas of pattern recognition, including HCR, and provides end-to-end learning, i.e., automated feature extraction and recognition. In this paper, we have proposed a novel deep learning architecture which exploits transfer learning and image-augmentation for end-to-end learning for script independent handwritten character recognition, called HCR-Net. The network is based on a novel transfer learning approach for HCR, where some of lower layers of a pre-trained VGG16 network are utilised. Due to transfer learning and image-augmentation, HCR-Net provides faster training, better performance and better generalisations. The experimental results on publicly available datasets of Bangla, Punjabi, Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu, Marathi, Nepali and Arabic languages prove the efficacy of HCR-Net and establishes several new benchmarks. For reproducibility of the results and for the advancements of the HCR research, complete code is publicly released at \href{https://github.com/jmdvinodjmd/HCR-Net}{GitHub}.

We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional probability of the input given the label, and are thereby required to explain every word in the input. We use channel models for recently proposed few-shot learning methods with no or very limited updates to the language model parameters, via either in-context demonstration or prompt tuning. Our experiments show that, for both methods, channel models significantly outperform their direct counterparts, which we attribute to their stability, i.e., lower variance and higher worst-case accuracy. We also present extensive ablations that provide recommendations for when to use channel prompt tuning instead of other competitive models (e.g., direct head tuning): channel prompt tuning is preferred when the number of training examples is small, labels in the training data are imbalanced, or generalization to unseen labels is required.

This past year has seen almost all students turn to technology more than ever before as part of their education. STEM (Science, Technology, Engineering, and Mathematics) is becoming more engrained in education for today's students. As a result, understanding coding or programming is more important than ever. While many first learn to code with block coding where different'blocks' of code are stacked together like building blocks, text coding is the next step. According to many in Computer Science education as well as people in the industry, Python is one of the most important languages for beginners to learn since it is applicable to so many different areas.

SAP India and Microsoft on Thursday announced the launch of a joint skilling programme'TechSaksham' for empowering young women students from underserved communities to build careers in technology. Through the joint initiative, SAP India and Microsoft aims to skill 62,000 women students in areas like artificial intelligence, cloud computing, web design and digital marketing. The programme will work in collaboration with the AICTE Training and Learning Academy-ATAL and state collegiate education departments to support the professional development of faculty at participating institutes, a statement said. In the first year of implementation, the initiative will train 1,500 teachers and each faculty trained will be equipped to support over 50 students in one year, impacting 60,000-75,000 students, it added. The pan-India initiative will be implemented by Edunet Foundation that will develop future-ready skills in young women graduating in sciences, engineering, computer applications, and vocational studies.