Suchitra is a professor by profession and learner by passion. She hold a PhD degree in Electronics and Communication Engineering with core competency in computer vision, pattern recognition, Artificial Intelligence,machine learning and deep learning. She is passionate about data science, Artificial Intelligence, natural language processing and firmly believes that future is Artificial Intelligence.

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data.

Be able to run deep learning models with Keras on Tensorflow 2 backend Run Deep Neural Networks on a real-world scientific protein dataset Understand how to feed own data to deep learning models (i.e. I answer questions on the same day. Understand how to feed own data to deep learning models (i.e. Understand and use Keras' functional API to create models with multiple inputs and outputs I answer questions on the same day. You should be able to use Python (if, while, lists.

AI is an enabler in transforming diverse realms by exploiting deep learning architectures. The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the Generative Adversarial Networks used for data creation in deep learning routines.

Attribution for the core content is given to the textbook "Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" which I would urge you to buy on Amazon

Anyone who has programming experience and wants to learn machine learning and deep learning. Statisticians and mathematicians who want to learn machine learning and deep learning.

This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer. After completing this course, you will be able to: - gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data - understand how parallel code is written, capable of running on thousands of CPUs.

The machine learning industry is forecasted to increase at a CAGR of 44.1 percent over the forecast period, from USD 1.03 billion in 2016 to USD 8.81 billion in 2022, according to predictions. Machine learning is an artificial intelligence (AI) technology that allows computers to learn and evolve without having to be explicitly programmed. The process of developing computer programs that can retrieve data and learn on their own is known as machine learning. Before getting into topic read "Deep Learning Vs Machine Learning Vs Data Mining Vs Artificial Intelligence" to understand the difference. One of the most intriguing machine learning systems I've ever come across.

Feature engineering is a very important aspect of machine learning. This article covers the step by step process of feature engineering. Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn about variable imputation, variable encoding, feature transformation, discretization, and how to create new features from your data. In this course, you will learn multiple feature engineering methods that will allow you to transform your data and leave it ready to train machine learning models.

Welcome to Machine Learning with Imbalanced Datasets. Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models. If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets.