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.

Welcome to Data Visualization in Python for Machine learning engineers. This is the third course in a series designed to prepare you for becoming a machine learning engineer. I'll keep this updated and list only the courses that are live. Here is a list of the courses that can be taken right now. Please take them in order.

In coming tutorials on this blog I will be dealing with how to create deep learning models that predict text sequences. However, before we get to that point we have to understand some key Natural Language Processing (NLP) ideas. One of the key ideas in NLP is how we can efficiently convert words into numeric vectors which can then be "fed into" various machine learning models to perform predictions. The current key technique to do this is called "Word2Vec" and this is what will be covered in this tutorial. After discussing the relevant background material, we will be implementing Word2Vec embedding using TensorFlow (which makes our lives a lot easier). To get up to speed in TensorFlow, check out my TensorFlow tutorial. Also, if you prefer Keras – check out my Word2Vec Keras tutorial.

Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the tree boosting method of choice. But a starting from 2015, a first to try, always winning algorithm surged to the surface: XGBoost. This algorithm re-implements the tree boosting and gained popularity by winning Kaggle and other data science competition. In the thesis Tree Boosting With XGBoost – Why Does XGBoost Win "Every" Machine Learning Competition, the author Didrik Nielsen from Norwegian University of Science and Technology is trying to: The paper introduce in first place the supervised learning task and discuss the model selection techniques.