Tasting Machine Learning With Minitab Predictive Analytics

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.

National Instruments (NI), the provider of solutions that enable engineers and scientists to solve the world’s greatest engineering challenges, announced LabVIEW 2017 Analytics and Machine Learning Toolkit. This solution includes VIs for training machine learning models that discover patterns in large amounts of data. You can deploy trained machine learning models to recognize patterns in new data.

Explore the advanced topics in Machine Learning with R in a step by step manner to build powerful predictive models in R