The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career 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.

Do you feel overwhelmed going through all the AI and Machine learning study materials? These Machine learning and AI projects will get you started with the implementation of a few very interesting projects from scratch. The first one, a Web application for Object Identification will teach you to deploy a simple machine learning application. The second one, Dog Breed Prediction will help you building & optimizing a model for dog breed prediction among 120 breeds of dogs. This is built using Deep Learning libraries.

The analytics leader of a US-based Fortune 200 company was under severe pressure. Her team supported 45,000 employees of the global energy company, and the business users weren't happy. The analytics deliverables were often late and suffered from poor quality. The analytics team was a part of the IT organization and was struggling to fill their open positions. The skills needed couldn't be found within the IT team.

IIT Madras has invited applications for a free online course called Introduction to Machine Learning on the NPTEL platform. The course, which is AICTE FDP approved, can be taken by senior undergraduate or postgraduate students pursuing their BE, MS, ME or even PhD. The course is 12 weeks long and will be conducted from 26 July to 15 October 2021. It would be most beneficial for students pursuing education in the domains of computer science and engineering, artificial intelligence, data science, programming and robotics. The course will be conducted by professor Balaraman Ravindran who is associated with the department of computer science at the Indian Institute of Technology Madras and is also a Mindtree Faculty Fellow.

Machine learning (ML), is the study of computer algorithms, that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms, build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. In simple words, machine learning is a subset under the broad umbrella of artificial intelligence.

Taught by Professor Mausam of the Indian Institute of Technology, Delhi, it discusses the philosophy of AI and how to model a new problem as an AI problem. It describes a variety of basic components of AI, such as search and logic, which can be used to model and solve a new problem. It also teaches many primary algorithms (a process or set of rules) to solve each formulation. The course prepares a student to take a variety of advanced courses in various subfields of AI. This teaches you the meaning of common AI terminology.

Artificial intelligence in education comprises a combination of education tools and information technology. In addition, rising awareness regarding the advantages of AI-based education system coupled with the demand for enhancing equity and quality of education system is expected to give an edge to the players in the education system, providing several lucrative opportunities in the coming years. The adoption of AI tools like natural language processing, deep learning, etc. is also foreseen to provide the scope of growth for students and teachers, particularly in K12 education. With the COVID-19 pandemic across the globe, AI is expected to bring a change in perspective in the methods of imparting quality education around the world and will guarantee an upgraded network among the students and teachers in the world. Furthermore, AI devices will help the students take online education from new colleges. Every one of these aspects will adorn the development of global Artificial Intelligence in education market in the coming decade.

We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision-maker has information available from past periods and needs to make one decision, while in the online setting, the decision-maker optimizes decisions dynamically over time based a new set of feasible actions and contextual functions in each period. For the offline setting, we characterize the optimal minimax policy, establishing the performance that can be achieved as a function of the underlying geometry of the information induced by the data. In the online setting, we leverage this geometric characterization to optimize the cumulative regret. We develop an algorithm that yields the first regret bound for this problem that is logarithmic in the time horizon.

'Machine Learning is all about how a machine with an artificial intelligence learns like a human being' Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python.

Uplatz provides this frequently asked list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This comprehensive list of important data science interview questions and answers might play a significant role in shaping your career and helping you get your next dream job. You can get into the mainstream of the Data Science world learning from this powerful set of Data Science interview questions. Data Science can be defined as multidisciplinary blend of trends prediction, data inference, algorithm development, and technology to solve analytically complex problems. At the core of data science is nothing but data.