Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI This course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This course discusses tools and techniques using Python to visualize, explain, and build trustworthy AI systems. This course covers the working principle and mathematical modeling of LIME (Local Interpretable Model Agnostic Explanations), SHAP (SHapley Additive exPlanations) for generating local and global explanations.

Fabio Mardero is a data scientist from Italy. He graduated in physics and statistical and actuarial sciences. He is currently working at a well-known Italian insurance company as a data scientist and Non-Life technical provisions evaluator. Arrays and Matrices, reading files, DataFrame, Series, pivot tables, group by, pipelines, datetime objects.

Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor also has $n$ number of rows and columns. Let's take a gray-scale image as an example, which is a two-dimensional matrix of numeric values, commonly known as pixels. Ranging from '0' to '255', each number represents a pixel intensity value. Here, the lowest intensity number (which is '0') represents black regions in the image while the highest intensity number (which is '255') represents white regions in the image.

Python is an ideal first programming language for anyone interested in coding. Here are the top 10 Free Online Courses for Python from Udemy we've curated to help you learn Python. In this post you'll find 10 good beginners Python courses you can learn from and start your career as a software developer or web developer. All courses are free and you'll have lifetime access to the material! What better way to learn a new programming language than to dive right in? Python may be a general-purpose programming language, but it has specialized libraries that lend themselves to machine learning, artificial intelligence (AI), and scientific computing.

This is the first course that gives hands-on Data Science, Analytics & AI Real world Projects using Python.. This is a practical course, the course I wish I had when I first started learning Data Science. It focuses on understanding all the basic theory and programming skills required as a Data Scientist, but the best part is that it has Practical Case Studies covering so many common business problems faced by Data Scientists in the real world. "Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review & Glassdoor. This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of Data Science, Machine Learning, Data analysis,, Natural Language Processing to real-world business problems.

This course is designed to teach you the ins and outs of easy film and video making by showing you the art and craft of making films and videos by showing you ... Want to make films and videos that really catch attention and praise? This online Easy Filmmaking Course will teach you how to make great films and videos using proven techniques and approaches. This course is designed to teach you the ins and outs of easy film and video making by showing you the art and craft of making films and videos by showing you how to plan, design and put them together. While there are plenty of courses about making films and videos, it's hard to find a course that gives you a step-by-step insight into making films easily that really punch through the noise. This is the course for you, taught by a professional filmmaker who has personally make more than 40 productions.

Description Linear Algebra for Machine Learning is a training course on the application of linear algebra in data science and machine learning, published by the Informit Academy. In this training course, you will get acquainted with the theoretical and practical issues of linear algebra and you will implement it in a completely practical way in projects related to machine learning. Machine learning and data science are two of the most widely used disciplines in today's digital world, and learning them can bring you many career opportunities. What you will learn in Linear Algebra for Machine Learning: Familiarity with the application of algebra and the principles of mathematics in the field of machine learning Familiarity with the basics of linear algebra Familiarity with different approaches to developing machine learning based solutions In-depth understanding of the working process of machine learning-based algorithms Improve the skills of mathematical intuition In-depth understanding of other topics related to machine learning such as calculus, statistics, optimization algorithms and… Course specifications Publisher: InformIT Instructor: Jon Krohn Language: English Level: Medium Courses: 58 Duration: 6 hours and 32 minutes Course topics Lesson 1: Orientation to Linear Algebra Lesson 2: Data Structures for Algebra Lesson 3: Common Tensor Operations Lesson 4: Solving Linear Systems Lesson 5: Matrix Multiplication Lesson 6: Special Matrices and Matrix Operations Lesson 7: Eigenvectors and Eigenvalues Lesson 8: Matrix Determinants and Decomposition Lesson 9: Machine Learning with Linear Algebra Prerequisites for Linear Algebra for Machine LearningMathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with quantitative information - such as understanding charts and rearranging simple equations - then you should be well-prepared to follow along with all of the mathematics.

Regression analysis is a useful mechanism for estimating the relationship between a dependent variable and one or more independent variables. It is widely used in forecasting and has become an important machine learning tool. It becomes crucial for someone starting in machine learning to understand how regression analysis works. Let us look at a few resources available online to get started with regression analysis. MachineHack, a popular platform for data scientists and AI practitioners provides courses on regression in the form of bootcamps. Bootcamps are pocket courses for all who aspire to become data scientists, data engineers and machine learning developers.

Hands on Real-World Projects on Various Domains of Data Science in Machine Learning, Natural Language Processing, Time Series Analysis Develop Natural Language Processing Models for Customer Sentiments Develop time series forecasting models to predict Prices of stocks Learn how to map your Problem into Data Science problem Learn best practices for real-world data sets. Basic knowledge of programming is recommended. However, You can follow my Basics of Python Course which is free of cost therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science and directly apply these skills to solve real world challenging business problems. Basic knowledge of programming is recommended. However, You can follow my Basics of Python Course which is free of cost therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge.

Predicting contextualised engagement in videos is a long-standing problem that has been popularly attempted by exploiting the number of views or the associated likes using different computational methods. The recent decade has seen a boom in online learning resources, and during the pandemic, there has been an exponential rise of online teaching videos without much quality control. The quality of the content could be improved if the creators could get constructive feedback on their content. Employing an army of domain expert volunteers to provide feedback on the videos might not scale. As a result, there has been a steep rise in developing computational methods to predict a user engagement score that is indicative of some form of possible user engagement, i.e., to what level a user would tend to engage with the content. A drawback in current methods is that they model various features separately, in a cascaded approach, that is prone to error propagation. Besides, most of them do not provide crucial explanations on how the creator could improve their content. In this paper, we have proposed a new unified model, CLUE for the educational domain, which learns from the features extracted from freely available public online teaching videos and provides explainable feedback on the video along with a user engagement score. Given the complexity of the task, our unified framework employs different pre-trained models working together as an ensemble of classifiers. Our model exploits various multi-modal features to model the complexity of language, context agnostic information, textual emotion of the delivered content, animation, speaker's pitch and speech emotions. Under a transfer learning setup, the overall model, in the unified space, is fine-tuned for downstream applications.