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Building High Accuracy Model With Core Machine Learning

This free online course examines how to build high accuracy models with Core ML, as well as how accuracy is calculated.

Publisher: YouAccel Training
Building High Accuracy Model With Core Machine Learning is a free online course that explains how to create a dataset & ML model. This course teaches you how to install Python environments and how to declare python variables. Discover the core concepts of machine learning and how to start building apps that can think. Learn how to build the app interface using the interface builder and how to build high accuracy models with Core ML. Register Now!
Building High Accuracy Model With Core Machine Learning
  • Duration

    10-15 Hours
  • Students

    27
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

Trying to set up your own Python environment for Machine Learning can be a tricky task. If you’ve never set up something like that before, you might spend hours fiddling with different commands trying to get it to work. Building High Accuracy Model With Core Machine Learning is a free online course that introduces you to the fundamentals of modern machine learning. This course explains machine learning and outlines some examples as well as the uses of machine learning. Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. In this course, you will learn about the machine learning process. Jupyter is a free, open-source, interactive web tool. It is known as a computational notebook, which researchers can use to combine software code, computational output, and multimedia resources in a single document. This course gives you a step-by-step guide on how to install a Jupyter notebook.

In this course, you will learn how to run values through convolutional layers, and all the cool processing you need to do. It describes how to build your Convolutional Neural Network using Keras. The goal of Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech. This course teaches you how to build an app that can recognize handwriting using the core ML model. It uncovers the process of importing the core ML model to a project in order to use it. This course teaches you how to perform OCR handwriting recognition using OpenCV, Keras, and TensorFlow. You will understand the process involved in making a prediction using a Core ML module to create a request handler. This course teaches you how to handle the result given from Core ML and use the result to cycle through. You will discover what is required in order to determine what number you are actually predicting.

Finally, this course teaches you how to import the Iris Dataset into a Python file using Scikit-learn. Learn how to prepare and organize the data, as well as to properly load it into a model. You are going to learn everything you need to know to start building more intelligent apps and your own ML models. Core ML is the foundation for domain-specific frameworks and functionality. Core ML supports Vision for analyzing images, Natural Language for processing text. Core ML also supports Speech for converting audio to text, and Sound Analysis for identifying sounds in audio. This course will introduce you to Core Machine Learning, how it works, and how to properly use Core Machine Learning. This course gives you hands-on knowledge on how to correctly integrate Machine Learning into iOS Apps. This course will be of immense benefit to website and application developers. Data analysts and anyone seeking basic knowledge using the Python environment. Register and Get Started Now!

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