About this course: In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.

The importance of training has long been recognized, but the link between training and performance is often hard to quantify. Typically, training and learning at work have taken place away from the workplace. Training has been the preserve of the classroom. Recently, classroom training may have been replaced or augmented by the digital content in a Learning Management System (LMS). In theory, the LMS makes learning more accessible and available.

Welcome to this course: Learn TensorFlow Slim(TF-Slim) From Scratch. TensorFlow-Slim is a light-weight library for defining, training, and evaluating complex models in TensorFlow. With the TensorFlow-Slim library, we can build, train, and evaluate the model easier by providing lots of high-level layers, variables, and regularizers. At the end of this course, you will be geared up to take on any challenges of implementing TensorFlow-Slim in your machine learning environment.

When faced with the choice of saving the life of the passenger or the pedestrian passing by, how should an autonomous car be programed to act? In many scenarios, the most logical decision isn't always the most ethical one, which is why objective decision-making alone in algorithms is not enough. As machine learning and advanced artificial intelligence algorithms become more prevalent in our daily lives, it becomes increasingly important to address the inherent biases and ethical blind spots built into these systems. In fact, when we don't, we risk unleashing systems that may have far-reaching and disastrous consequences across many areas of society, from medical diagnoses to judicial decisions. Join Nathana Sharma, as she talks about the importance of designing AI algorithms that are capable of making decisions that are not just rationally correct, but also ethically right.

Welcome to this e-learning course developed and produced by Dr Neil Thompson and hosted by Simpliv. Neil is a well-published author in the people professions field, an international conference speaker and sought-after consultant.The overall aim of this course is to help you broaden and deepen your understanding of what is involved in learning, what can prevent it from happening and what you can do to maximize your learning. Learning is part of everyday life and something we are very familiar with. But, that does not mean that we are making the most of the learning opportunities we encounter. Indeed, it is fair to say that, despite the emphasis on the importance of learning, relatively few people achieve optimal learning.

Being able to perform machine learning in C will make you a very desirable hiring target. Not that you wouldn't be if you used any other language but, the truth is that machine learning in C is a great combination that is likely to give you access to very interesting positions! In this course, we focus on the practical part of machine learning--employing different C libraries. Several popular machine learning libraries currently exist--we'll review them and you'll become familiar with four of them. We use examples of standard machine learning algorithms implemented through the libraries.

Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation.

Learn to use Python Artificial Intelligence for data science. Do you want to learn how to use Artificial Intelligence (AI) for automation? You will learn how to code in Python, calculate linear regression with TensorFlow, analyze credit card fraud and make a stock market prediction app. AI is code that mimics certain tasks. You can use AI to predict trends like the stock market.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

By the end of this course you will have 3 complete mobile machine learning models and apps. We will build a simple weather prediction project, stock market prediction project, and text-response project. For each we will build a basic version in PyCharm, save the trained model, export the trained model to Android Studio, and build an app around model. We'll give you all necessary information to succeed from newbie to pro. We will install PyCharm 2017.2.3 and explore the interface.