Goto

Collaborating Authors

Information Technology: Instructional Materials


Natural Language Processing (NLP) with Python: 2020

#artificialintelligence

BESTSELLER Created by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy English English [Auto] PREVIEW THIS COURSE - GET COUPON CODE Description Recent reviews: "Very practical and interesting, Loved the course material, organization and presentation. Thank you so much" "This is the best course to learn NLP from the basic. According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is'Natural Language Processing', You are at right place. How Android speech recognition recognize your voice with such high accuracy.


Introduction to AI, Machine Learning and Python basics

#artificialintelligence

Free Coupon Discount Preview this course Introduction to AI, Machine Learning and Python basics, Learn to understand Artificial Intelligence and Machine Learning algorithms, and learn the basics of Python Programming


Top 10 Courses to Learn AI, Machine Learning and Deep Learning

#artificialintelligence

Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Over 20 videos spread across the entire module will explain you error analysis and different kind of the learning techniques.


Deploy Machine Learning Pipeline on AWS Fargate - KDnuggets

#artificialintelligence

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve it as a web application using Google Kubernetes Engine. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline serverless using AWS Fargate. This tutorial will cover the entire workflow starting from building a docker image locally, uploading it onto Amazon Elastic Container Registry, creating a cluster and then defining and executing task using AWS-managed infrastructure i.e.


The Data Science Course 2020 Q2 Updated: Part 4 > Python & R

#artificialintelligence

You will learn both Python and R Programming with Data Science in this course. Python: You will first learn how to Install Anaconda and Jupyter on your desktop/laptop Python: You will understand and learn the basics of For Loops and Advanced For Loops. You will have clarity on Python generators and will master the flow of your code using "If Else" Python: You will understand Why foundations Modify Lists and Dictionaries and Functions. Learn how to analyze, retrieve and clean data with Python Python: Learn Concatenation (Combining Tables) with Python and Pandas and Manipulating Time and Date Data with Python Datetime Python: You will learn to Use Pandas with Large Data Sets, Time Series Analysis and Effective Data Visualization in Python R: You will learn the most important tools in R that will allow you to do data science R: You will have the tools to tackle a wide variety of data science challenges, using the best parts of R. R: You will learn how to Tidy the data. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored.


Introduction to Machine Learning For Beginners [A to Z] 2020

#artificialintelligence

To provide awareness of the two most integral branches (i.e. To build appropriate neural models from using state-of-the-art python framework. To build neural models from scratch, following step-by-step instructions. To build end - to - end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available. To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.



UiPath Advanced REFramework - Everything Explained

#artificialintelligence

Note: This course is for candidates having 6 months of working experience in developing RPA solutions. Robotics Process Automation (RPA) is the talk of the town in the business world these days – and with a good reason. RPA is revolutionizing the way we work by removing the boring and repetitive work. This gives the employees time to be creative and do more interesting work. At the front of this revolution is UiPath, which is widely acknowledged as the leading RPA software vendor.


An honest reaction to Andrew Ng's AI for medicine specialization

#artificialintelligence

Sometime ago, the world's most affable and recognizable AI leader, Andrew Ng launched a specialization called AI for medicine through his MOOC institution, deeplearning.ai. I have always been a big fan of Andrew Ng, and it was he who had introduced me to the world of machine learning through his grainy Youtube videos of Stanford lectures back in 2012. I was very excited that finally, Andrew Ng has finally turned his attention to the critical shortage of AI experts in the medical field . Truth be told, AI in the medical world has not seen as much progress as other domains like personalized advertisements, recommendations, autonomous driving etc. There are lot of complex issues like data privacy, small sample sizes etc. which I would prefer to discuss in depth in another post.


Linear Programming for Data Science and Business Analysis

#artificialintelligence

In this course you will learn all about the mathematical optimization of linear programming for data science and business analytics. 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. The length of the course is more than 6 hours and there are total more than 4 sections in this course.