In this course we aim AI enthuasist from any work dicipline. You can be a engineering studeny in Cımputer, Machine, Indsurty, .. Engineering Departments or you can be a biologist trying to find a new methodology for disease detection, or a finance expert who want to cluster his/her customers into segments.. You can use Machine Learning in any field of your real life! By studying the projects in this course you will have a general understanding about machine learning and its aim to use in real life. After this course you will have a clear concept of AI and machine learning in your head and it's upto you to deep dive into more detailed subjects of machine learning, deep learning or artificial intelligence.

Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses. These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you'll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram. The toughest obstacle to overcome when you're learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability.

Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems. How to formulate an MO model. How to build an MO model using the Gurobi Python API.

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied area

Deep Learning Prerequisites: Linear Regression in Python, Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. Created by Lazy Programmer Inc. Preview this Course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Importance of XAI in modern world Differentiation of glass box, white box and black box ML models Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques Trade-off between accuracy and interpretability Application of InterpretML package from Microsoft to generate explanations of ML models Need of counterfactual and contrastive explanations Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets. Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets. 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.

Since businesses often don't have the time or resources to support the long and tedious work required to complete data science projects, most of them never come to fruition. The fairly recent development of automated machine learning, or AutoML, rectifies this by speeding up the work data scientists perform through automation. Dennis Michael Sawyers, data scientist and author of Automated Machine Learning with Microsoft Azure, uses Azure's AutoML product as the foremost example of how automated ML software expedites and simplifies this otherwise arduous work. In this Q&A, Sawyers discusses the evolution of automated machine learning platforms and how they are used to develop ML models. Editor's note: The following interview was edited for length and clarity.

Developing & deploying AI & Machine Learning applications using python AI & ML frameworks · how to use most popular AI & ML frameworks: NumPy,SciPy, Scikit- . When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training. Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment, troubleshooting issues and may make you give up in the middle. Instructor based training can be expensive at times and need your time commitment. This course combines the best of both these options.

This course, which has been developed by Coursera in collaboration with Harvard University, will allow you to take the first step in resolving major real-world challenges. It describes and explains the theories that are behind new-age technologies like game-playing engines, handwriting recognition and machine translation. It explores in detail the concepts and algorithms that underpin modern AI.

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!