Sales Forecasting determines how the company invests and grows to create a massive impact on company valuation. This is part 2, and you will learn how to do sales prediction using Time Series. I'm working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model. Now let's look at the moving average, as it gives you an overall idea of the trends in the dataset, it's useful in long-term forecasting. Rolling mean/ Standard Deviation-- helps in understanding short-term trends in data and outliers.

I currently work at Rebaie Analytics Group to develop algorithms in computer vision, natural language processing, and other deep learning fields. In college, I started reading about the impact of data science in transforming business and even in the way humans interact with machines in our daily lives. Further inspired by the AI influencer and keynote speaker Ali Rebaie, I wanted to apply an anthropological perspective to solve current AI challenges. Like I do with any subject I'm interested in, I jumped right into learning everything I could, starting with taking machine learning courses online. I was glad to find Coursera -- it's really the most effective and interactive e-learning platform out there.

ChatGPT is a powerful natural language processing tool developed by OpenAI that can be used to generate human-like responses to user input. One of the exciting applications of ChatGPT is to build chatbots that can interact with users and provide helpful responses. In this tutorial, we will guide you through the process of creating a simple chatbot using ChatGPT. To get started, you will need to set up your development environment. You can use any text editor or integrated development environment (IDE) to write your Python code.

The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.

Reinforcement learning is an effective way to solve the decision-making problems. It is a meaningful and valuable direction to investigate autonomous air combat maneuver decision-making method based on reinforcement learning. However, when using reinforcement learning to solve the decision-making problems with sparse rewards, such as air combat maneuver decision-making, it costs too much time for training and the performance of the trained agent may not be satisfactory. In order to solve these problems, the method based on curriculum learning is proposed. First, three curricula of air combat maneuver decision-making are designed: angle curriculum, distance curriculum and hybrid curriculum. These courses are used to train air combat agents respectively, and compared with the original method without any curriculum. The training results show that angle curriculum can increase the speed and stability of training, and improve the performance of the agent; distance curriculum can increase the speed and stability of agent training; hybrid curriculum has a negative impact on training, because it makes the agent get stuck at local optimum. The simulation results show that after training, the agent can handle the situations where targets come from different directions, and the maneuver decision results are consistent with the characteristics of missile.

If so, then this course is for you! With DALL•E, you'll learn how to create impressive AI art in minutes. You'll learn how to use DALL•E to create art; we'll go through all of the tools available so you can master this revolutionary new medium. Whether you're a seasoned artist or a complete beginner, this course will give you the skills you need to get started in the exciting world of AI art. This course takes you step-by-step through the process of becoming a professional AI artist.

Created by Raymond Davey 4.5 hours on-demand video course Ever since OpenAI arrived on the scene, access to a trained AI has become accessible to everyone. GPT allows you to ask a chatbot to complete tasks, and to answer questions. Fine-tuning allows you to change the way the AI responds. Embedding allows you to use your own knowledge base. Dall-E allows you to generate images from text.

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy.

We are introduced to new discoveries and technologies every day, and one of the best and most popular inventions today is artificial intelligence (AI) and its tools. One of them is Chat GPT, a conversational model of AI that is a powerful chatbot that answers follow-up questions and writes code for the users. The day it was launched, everybody was going gaga over the new technology and the remarkable uses of this AI-powered chatbot. In this blog we will get to know about the perks of ChatGPT for coding. A conversational AI-powered chatbot created by OpenAI is popular right now due to its many applications, including assisting students with their homework projects, offering suggestions for creating websites, and even writing code.

We are witnessing a rapid increase in real-world autonomous robotic deployments in environments ranging from indoor homes and commercial establishments to large-scale urban areas, with applications ranging from domestic assistance to urban last-mile delivery. The developers of these robots inevitably have to make impactful design decisions to ensure commercial viability, but such decisions have serious real-world consequences. Unfortunately, it is not uncommon for such projects to face intense bouts of social backlash, which can be attributed to a wide variety of causes, ranging from inappropriate technical design choices to transgressions of social norms and lack of community engagement. To better prepare students for the rigors of developing and deploying real-world robotics systems, we developed a Responsible Robotics teaching module, intended to be included in upper-division and graduate-level robotics courses. Our module is structured as a role-playing exercise that aims to equip students with a framework for navigating the conflicting goals of human actors which govern robots in the field. We report on instructor reflections and anonymous survey responses from offering our responsible robotics module in graduate-level and upper-division undergraduate robotics courses at UT Austin. The responses indicate that students gained a deeper understanding of the socio-technical factors of real-world robotics deployments than they might have using self-study methods, and the students proactively suggested that such modules should be more broadly included in CS courses.