Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
This video gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science courseis invaluable. It coversa wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuresguidance and tips on everything from sentiment analysis to neural networks. With this video,you’ll soon be able to answer some of the most important questions facing you and your organization.
About the Author
Jason is an avid Python machine learning practitioner, obsessed college football fan, and German Shepherd lover. Jason completed his graduate and undergraduate degrees at Arizona State University. During that time, Jason conducted statistical analysis and visual communication analysis for the Arizona State Football program and was part of a 4-person team that placed 3rd nationally in The Great Minds Challenge: IBM Watson Edition, a collegiate machine learning competition. Jason currently works for TransDev and zTrip where he combines data from multiple enterprise sources to gain actionable insights about customers. Jason also recently taught a Machine Learning workshop for a Fortune 500 company and is currently learning to leverage the Apache Spark ecosystem using both Scala and Python.
This video covers the implementation of a perceptron algorithm in Python.
This video shows how to work around the iris dataset.
This video shows how to train our perceptron on iris dataset.
This video shows how to visualize the performance of our classifier.
This video shows how to implement the adaptive linear neuron algorithm (adaline).
This video shows how to improve the performance of machine learning classifiers.
This video covers the implementation of Adaline
This video delves into the logistic regression in scikit-learn.
This video delves into predicting class probabilities.
This video covers how to train an SVM in Scikit-Learn.
This video shows the effect of gamma parameter.
This video shows how to train with decision trees with Scikit-Learn.
This video shows the mapping of ordinal features.
This video shows feature scaling.
This video covers feature importance with random forests
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