This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!

The aim of this course is to provide a practical understanding of advanced Machine Learning algorithms in Apache Spark to make predictions and recommendation and derive insights from large distributed datasets. This course starts with an introduction to the key concepts and data types that are fundamental to understanding distributed data processing and Machine Learning with Spark. Further to this, we provide practical recipes that demonstrate some of the most popular algorithms in Spark, leading to the creation of sophisticated Machine Learning pipelines and applications. The final sections are dedicated to more advanced use cases for Machine Learning: streaming, Natural Language Processing, and Deep Learning. In each section, we briefly establish the theoretical basis of the topic under discussion and then cement our understanding with practical use cases.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on.We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming.It focuses on the various tree-based machine learning models used by industry practitioners.We will also discuss k-nearest neighbors, Naive Bayes, Support Vector Machine and recommendation engine.By the end of the course, you will have mastered the required statistics for Machine Learning Algorithm and will be able to apply your new skills to any sort of industry problem. Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore.

Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms. This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models. Finally, you'll explore dimensionality reduction with various parameters.

If you're serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you. These data science skills are in great demand, but there's no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects. Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers - and in turn, you can demand top dollar for your abilities. You may be a data science veteran or an enthusiast - if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant.

This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment.

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. You will build systems that classify documents, recognize images, detect ads, and more. You'll learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model's performance.

TensorFlow is one of the most commonly used frameworks for Deep Learning and AI. This course will be your guide to understand and learn the concepts of Artificial intelligence by applying them in a real-world project with TensorFlow. This course will show you how to combine the power of Artificial Intelligence and TensorFlow to develop some exciting applications for the real world. This course will take you through all the relevant AI domains, tools, and algorithms required to build optimal solutions and will show you how to implement them hands-on. You will then be taken through techniques such as reinforcement learning, heuristic searches, neural networks, Computer Vision, OpenAI Gym, and more in different stages of your application.

Learn to work in an exciting area of computer science and artificial intelligence. In this course we will train an artificial brain to make the game work. No matter where the present falls, the computer will know exactly how get it. Make an AI Christmas game! Our Unity game will have a holiday setting featuring a sled.

Enter and explore the fascinating world of intelligent apps with Artificial Intelligence with Python. Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps that intelligently interact with the world around you, automatic speech recognition systems, and more. Prateek Joshi is an artificial intelligence researcher, an author of eight published books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications.