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!

You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.

Learn how to use Spark, Pyspark AWS, Spark applications, Spark EcoSystem, Hadoop and Mastering PySpark.

Decision trees are a supervised learning method used to build a model that predicts the value of a target variable by learning simple decision rules from the data features. DTs are used for both classification and regression and are simple to understand and interpret. Below, we have listed down the top online courses, YouTube videos and guides for enthusiasts to master decision trees. The course by CodeAcademy focuses on teaching developers how to build and use decision trees and random forests. The course looks at two methods in detail: Gini impurity and Information Gain.