In this hands-on project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people's sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities. Note: This course works best for learners who are based in the North America region.

How to become a pro in Linear Programming for Data Science? In this course, you will learn all about the mathematical optimization of linear programming in data science. This course is very unique and has its own importance in its respective disciplines. Data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under special rules and formulas.

In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you'd find in Excel or Google Sheets.

Description Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This course is made to give you all the required knowledge at the beginning of your journey, so that you don't have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real life questions, to choose and apply appropriate methods to answer those questions, and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, prediction with machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by over 360 practice questions and 120 data exercises.

What can be (machine) learned about the complexity of Buchberger's algorithm? Given a system of polynomials, Buchberger's algorithm computes a Gr\"obner basis of the ideal these polynomials generate using an iterative procedure based on multivariate long division. The runtime of each step of the algorithm is typically dominated by a series of polynomial additions, and the total number of these additions is a hardware independent performance metric that is often used to evaluate and optimize various implementation choices. In this work we attempt to predict, using just the starting input, the number of polynomial additions that take place during one run of Buchberger's algorithm. Good predictions are useful for quickly estimating difficulty and understanding what features make Gr\"obner basis computation hard. Our features and methods could also be used for value models in the reinforcement learning approach to optimize Buchberger's algorithm introduced in [Peifer, Stillman, and Halpern-Leistner, 2020]. We show that a multiple linear regression model built from a set of easy-to-compute ideal generator statistics can predict the number of polynomial additions somewhat well, better than an uninformed model, and better than regression models built on some intuitive commutative algebra invariants that are more difficult to compute. We also train a simple recursive neural network that outperforms these linear models. Our work serves as a proof of concept, demonstrating that predicting the number of polynomial additions in Buchberger's algorithm is a feasible problem from the point of view of machine learning.

IIT Hyderabad is providing an M.Tech course of 2 years in Artificial Intelligence. The admission for this course is conducted in two modes: Mode R1 and Mode R2. For Mode R1, candidates must have a valid GATE score from subjects like CS/ST/MA/EE/EC. For Mode R2, candidates must have passed or in the final year of M.Ac/ B.Tech/ B.E with a minimum of 8.0 CGPA. The shortlisted candidates are declared based on the valid information in the application form.

If you've ever applied for a credit card or loan, you know that financial firms process your information before making a decision. This is because giving you a loan can have a serious financial impact on their business. But how do they make a decision? In this course, you will learn how to prepare credit application data. After that, you will apply machine learning and business rules to reduce risk and ensure profitability. You will use two data sets that emulate real credit applications while focusing on business value.

Even though multi-class classification is not as common, it certainly poses a much bigger challenge than binary classification problems. You can literally take my word for it because this article has been the most challenging post I have ever written (have written close to 70). I found that the topic of multiclass classification is deep and full of nuances. I have read so many articles, read multiple StackOverflow threads, created a few of my own, and spent several hours exploring the Sklearn user guide and doing experiments. This was enough to conclude that no single resource shows an end-to-end workflow of dealing with multiclass classification problems on the Internet (maybe, I missed it).

How to become pro in Linear Programming for Data Science? In this course you will learn all about the mathematical optimization of linear programming in data science. This course is very unique and have its own importance in their respective disciplines. The data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under the special rules and formula.