Data Mining and Machine Learching are a hot topics on business intelligence strategy on many companies in the world. These fields give to data scientists the opportunity to explore on a deep way the data, finding new valuable information and constructing intelligence algorithms who can "learn" since the data and make optimal decisions for classification or forecasting tasks. This course is focused on practical approach, so i'll supply you useful snippet codes and i'll teach you how to build professional desktop applications for machine learning and datamining with python language. We'll also manage real data from an example of a real trading company and presenting our results in a professional view with very illustrated graphical charts. We'll initiate at the basic level covering the main topics of Python Language and also the needing programs to develop our applications.

This hands-on workshop demystifies machine learning and explores how machine learning tools have been and can be used for artistic expression. Participants will learn about the concept of machine learning, its history and critical issues, examples of art projects that use ML, and explore resources that allow them to continue experimenting with ML on their own. This is open to artists by any definition. No pre-requisites or prior knowledge is required. If possible, participants should bring a computer (we will be interacting with and creating some online examples), but are still welcome to attend/observe without one.

Today, AI has turned into reality what used to be the stuff of sci-fi novels. For decades, scholars from diverse disciplines have been predicting how AI and robotics are about to change the way we think, work and live. Although, not everyone is on the same page when it comes to AI, there is no denying that it is already demonstrating its positive potential in many industries. One area where AI is expected to play a huge role is education. However, in India, the education sector is still seeking ways to respond to the advent of this technology.

Want to build skills in artificial intelligence (A.I.) and deep learning? Udacity and Google are launching a free introductory course on the subject, which naturally leans into TensorFlow, the open-source library for deep learning software developed by Google. "Intro to TensorFlow for Deep Learning" is a two-month course, and now open to enrollment. Its goal is to help developers build A.I. applications that can scale (using TensorFlow, of course). It's the second TensorFlow-based collaboration between the two firms; in 2016, Udacity and Google launched a TesnorFlow course that taught students the basics of the platform.

We are excited to announce the launch of our free ebook Machine Learning for Human Beings, authored by researcher in the field of computer vision and machine learning Mohit Deshpande, in collaboration with Pablo Farias Navarro, founder of Zenva. In over 100 pages you will learn the basics of Machine Learning – text classification, clustering and even face recognition and learn to implement these algorithms using Python! This ebook covers both theoretical and practical aspects of Machine Learning, so that you have a strong foundation and understand what happens under the hood. This book is provided at no cost in PDF format.

AI is no longer the future… it is now the present. AI has quickly gone from the innovation labs to being fully implemented from one department to the next. The AI revolution needs people that understand the technology and can take it to the next level. In this workshop you will learn from industry experts on AI/ML/NLP and will be working on building a Customer Service or Marketing Chatbot which you can launch on Messenger, your website or on Google Voice or Alexa. This is a full day workshop, that starts at 9am and ends at 5pm.

In this Swift for TensorFlow session, you will learn about language-integrated automatic differentiation, and tooling optimized for your productivity. See the revamped dev site https://www.tensorflow.org/

Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases. We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer. We justify this approximation by showing the exact best-response for a shallow linear network with L2-regularized Jacobian can be represented by a similar gating mechanism. We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function. Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities. Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values. Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems. We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs).

Natural Language Processing (NLP) is a hot topic into Machine Learning field. This course is an advanced course of NLP using Deep Learning approach. Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course. This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate so fast the processes.

Scikit-learn is a free machine learning library for Python. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy. In this tutorial we will learn how to easily apply Machine Learning with the help of the scikit-learn library, which was created to make doing machine learning in Python easier and more robust. To do this, we'll be using the Sales_Win_Loss data set from IBM's Watson repository. We will import the data set using pandas, explore the data using pandas methods like head(), tail(), dtypes(), and then try our hand at using plotting techniques from Seaborn to visualize our data. Then we'll dive into scikit-learn and use preprocessing.LabelEncoder() in scikit-learn to process the data, and train_test_split() to split the data set into test and train samples. We will also use a cheat sheet to help us decide which algorithms to use for the data set. Finally we will use three different algorithms (Naive-Bayes, LinearSVC, K-Neighbors Classifier) to make predictions and compare their performance using methods like accuracy_score() provided by the scikit-learn library. We will also visualize the performance score of different models using scikit-learn and Yellowbrick visualization. If you need to brush up on these topics, check out these pandas and data visualization blog posts. For this tutorial, we will use the Sales-Win-Loss data set available on the IBM Watson website.