AutoML tools are the need of the hour for data scientists to reduce their workloads in the world where the generation of data is only increasing exponentially. Readily available AutoML tools make the data science practitioner's work more comfortable and covers necessary foundations needed to create automated machine learning modules. And with the spur in data and the potential that this data holds, data scientists will benefit more by using AutoML capabilities. As we approach the midpoint of 2020, it is slowly being recognised that this year will see an increase in adaptation of AutoML. With the massive potential of AutoML about to burst, non-data science professionals and data science practitioners will look to get a more comprehensive view on the technology.

This review has been written with the intention of not only providing you with my opinion of the course but also to provide an insight into the topics covered and teach some of the key concepts. The Advanced Machine Learning and Signal Processing course was developed by IBM and available on Coursera. It is available as an individual course or as one-part of a four-part massive open online course (MOOC), the Advanced Data Science Specialization. The Fundamentals of Scalable Data Science focuses on the basics of Apache Spark in the cloud and acted as an introduction to IBM Watson Studio (IBM's cloud service). In contrast, this course is significantly more in-depth, focusing on more advanced machine learning concepts and signal processing. The course is delivered by two IBM data scientists, Romeo Kienzler and Nickolay Manchev.

It is sad that we are affected by Coronavirus outbreak which has led to schools/colleges being closed, engineering/mba internships being cancelled and job joining being postponed. But, on a positive side we can use this time of crisis to invest in ourselves to hone our skills and prepare for a better tomorrow. Having spent 6 years in the industry as a software developer/product manager and with a decade of experience in Python programming, I want to give back to the community in this time of crisis. I will share with you lectures, notebooks, have youtube/hangout live training workshops to teach you skills which can help you in the industry.

Link: best udemy course Beginning with Machine Learning & Data Science in Python Fundamentals of Data Science: Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML by UNP United Network of Professionals What you'll learn You will be able to apply data science algorithms for solving industry problems You will have a clear understanding of industry standards and best practices for predictive model building You will be able to derive key insights from data using exploratory data analysis techniques You will be able to efficiently handle data in a structured way using Pandas You will have a strong foundation of linear regression, multiple regression and logistic regression You will be able to use python scikit-learn for building different types of regression models You will be able to use cross validation techniques for comparing models, select parameters You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc.. You will be able to regularize models for reliable predictions Description 85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). Naturally, 85% of the interview questions comes from these topics as well. This is a concise course created by UNP to focus on what matter most. This course will help you create a solid foundation of the essential topics of data science.

With an increasing number of digital text documents shared across the world for both business and personal reasons, the need for translation capabilities becomes even more critical. There are multiple tools available online that enable people to copy/paste text and get the translated equivalent in the language of their choice. While this is a great way to perform ad hoc translation of a (limited) amount of text, it can be tedious and time-consuming if performed frequently. Your organization may largely depend on content to document your products and services, teach your customers how to interact with you, or just share the cool things you are doing. This content is often text-heavy and mostly written in English.

The Women in AI Podcast - Women at the forefront of AI discuss their work and diversity issues faced in STEM - Listen here. The DeepMind Podcast - A new series that we hope will answer the difficult questions in AI - Listen here. Lex Fridman's AI Podcast - a series of conversations about technology, science, and the human condition hosted by MIT's Lex Fridman - Listen here. The Eye on AI - Justin Gottschlich explains his group's efforts to automate software development - Listen along here. The NVIDIA AI Podcast - NVIDIA release new episodes every other week with guest speakers at the forefront of AI - Listen along here. Artificially Intelligent - Weekly discussions on the impacts of AI - Listen here. Underrated ML - Regular RE•WORK speaker, Sara Hooker & her brother, Sean Hooker have started their new podcast based on underrated ML papers - Listen here. Concerning AI - A series on AI hosted by Ted Sarvata & Brandon Sanders - Listen here.

Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features. This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set.

The book AI Crash Course by Hadelin de Ponteves contains a toolkit of four different AI models: Thompson Sampling, Q-Learning, Deep Q-Learning and Deep Convolutional Q-learning. It teaches the theory of these AI models and provides coding examples for solving industry cases based on these models. InfoQ readers can find an excerpt of AI Crash Course on the publisher's website. InfoQ interviewed Hadelin de Ponteves about using different AI models and how to develop AI skills. InfoQ: Why did you write this book?

Cognilytica is running our Artificial Intelligence (AI) and Machine Learning (ML) training and certification based on the best practices CPMAI methodology, with a live virtual training starting the week of March 25, 2020 that will teach you how to apply the CPMAI Methodology for your projects. This live, instructor-led training is conducted completely online at designated times with live trainers. The course is run as a series of eight (8) sessions conducted over four (4) weeks. This vendor neutral course is an intensive, interactive, real-world based "fire hose" that prepares you to succeed with your AI & ML efforts, whether you're just beginning them or are well down the road with implementation, reflecting the best thinking and research that Cognilytica produces. Cognilytica's CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise.

Data cleaning is a critically important step in any machine learning project. In tabular data, there are many different statistical analysis and data visualization techniques you can use to explore your data in order to identify data cleaning operations you may want to perform. Before jumping to the sophisticated methods, there are some very basic data cleaning operations that you probably should perform on every single machine learning project. These are so basic that they are often overlooked by seasoned machine learning practitioners, yet are so critical that if skipped, models may break or report overly optimistic performance results. In this tutorial, you will discover basic data cleaning you should always perform on your dataset.