Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You'll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.

Google opened the doors to its Machine Learning Crash Course, which had already been taken by more than 18, 000 Googlers, in March 2018.

Efficient continual learning in humans is enabled by a rich set of neurophysiological mechanisms and interactions between multiple memory systems. The brain efficiently encodes information in non-overlapping sparse codes, which facilitates the learning of new associations faster with controlled interference with previous associations. To mimic sparse coding in DNNs, we enforce activation sparsity along with a dropout mechanism which encourages the model to activate similar units for semantically similar inputs and have less overlap with activation patterns of semantically dissimilar inputs. This provides us with an efficient mechanism for balancing the reusability and interference of features, depending on the similarity of classes across tasks. Furthermore, we employ sparse coding in a multiple-memory replay mechanism. Our method maintains an additional long-term semantic memory that aggregates and consolidates information encoded in the synaptic weights of the working model. Our extensive evaluation and characteristics analysis show that equipped with these biologically inspired mechanisms, the model can further mitigate forgetting.

Artificial intelligence is a technology that is altering all facets of life despite a widespread lack of familiarity. It is a versatile tool that helps individuals to reconsider how we combine information, evaluate data, and use the insights obtained to enhance decision-making. It makes sense that one of the most in-demand skills in the technology industry, which has impacted practically every industry from cybersecurity to software to IoT, has to do with artificial intelligence (AI). Business processes may be made more effective, complications can be reduced, and the quality of life can be improved thanks to the innovative applications and services that are being quickly developed using AI.

Today, it is a rapidly growing field that has the potential to revolutionize many aspects of our lives. In this course I am sharing an approach on how you can benefit from AI tools that you can have access to and the way you can use them to help you increase your productivity. Whether you are a Content Writer, a Teacher, a Graphical Designer, a Developer or in any field, there something AI can do for you and this is what this course is about! Artifical Intelligence can be applied today to a wide range of fields, including robotics, natural language processing, computer vision, and machine learning. The development of AI has the potential to revolutionize many industries and has sparked a great deal of debate about its potential impact on society.

Bengaluru, December 27, 2022: MIT xPRO has announced two new upskilling programs in Executive Leadership Principles and Machine Learning for Business, Engineering, and Science. Delivered through digital skills training platform Simplilearn, these programs leverage MIT xPRO's thought leadership in engineering and management developed over years of research, teaching, and practice as well as Simplilearn's dynamic, interactive, digital learning platform. The Executive Leadership Principles program is designed to enable learners to understand an array of organizational and leadership aspects. Some of the focus areas include organizational strategies and capabilities, applying influence, negotiation, conflict resolution, change management, problem solving, navigating culture and networks, as well as discovering and implementing leadership strengths. This Executive Program offers masterclasses taught by MIT faculty and instructors, assessments, case studies, and tools.

In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand. Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content can easily confuse one with where to begin or how to start their data science career. To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not.

The late Gabriel Opara was an African poet who wrote a lot of wonderful poems, including the popular "Piano and Drums," which is my favorite. I fell in love with the poem in my high school days when I did literature in English. Having completed the Deep Learning specialization and Tensorflow developer certification courses on Coursera, I decided to put my knowledge of Natural Language Processing into practice by applying AI to the things that interest me, of which African poems are one. Data collection is usually among the early stages of any machine learning project. I got all the data needed for this project from the public web. The data set consists of 14 poems by Gabriel in about 408 lines; the first 10 lines are the titles of the poems.

Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep sequence models have costs that scale with the sequence length rather than the sequence complexity. In this work, we instead treat data sequences as observations from an underlying continuous-time process and learn how to efficiently discretize while retaining information about the full sequence. As a consequence of decoupling sequential information from its temporal discretization, our approach allows for greater compression rates and smaller computational complexity. Moreover, the continuous-time approach naturally allows us to decode at different time intervals. We empirically verify our approach on multiple domains involving compression of video and motion capture sequences, showing that our approaches can automatically achieve reductions in bit rates by learning how to discretize.

Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that the Grey Wolf Optimizer is one of the most recent, well-regarded and widely-used AI search techniques. A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.