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.

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.

Most tutorials/articles are usually focused on paper reviews and the performance of machine learning models in a lab. However, a significantly overlooked area is putting models into production and monitoring their performance, called online machine learning or online learning, where the model constantly learns from new data. The main advantage of online learning is that it prevents data from going "stale". Sometimes, the nature and distribution of the data are likely to change over time. If your model doesn't keep on improving, its performance will keep on decreasing.

If you have seen the Terminator movies, you have probably wondered whether your smart refrigerator will one day become self-aware and plot a world takeover with other artificial intelligence-powered tech. But Ayesha Khanna, co-founder and CEO of artificial intelligence solutions firm ADDO AI, does not see AI that way. Instead, she sees the possibilities in applying AI to make people's lives better, particularly in building smart cities that are people-focused in terms of giving everyone access to basic services and goods. "More than anything else, AI has the potential to democratize access and make growth inclusive," she said in her keynote during the Southeast Asia Development Symposium 2021 organized by the Asian Development Bank in March. Countries need to make sure their cities are livable since each second, five people join the ranks of the middle class, most of them through migration to cities, said Khanna.

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.