|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machinelearning-dataanalysis-mammothinteractive/
课程评论：没有评论
课程名称：高级机器学习与数据分析项目训练营 课程概述： 本课程将带您深入数据科学与分析的世界，涵盖广泛的示例，包括CIFAR 100图像数据集、Apple的Xcode开发、Swift编程、CoreML、图像识别以及使用pandas整理数据等。该课程由Mammoth Interactive创建，获得了Kickstarter的第一号项目资助。您将学习Android Studio、Java、应用开发、Pycharm、Python编程、TensorFlow等技能，构建基于机器学习的高级项目，包括使用神经元功能的高级MNIST数据库、文本摘要生成器以及物体定位和识别。 机器学习是一种机器根据以前的数据暴露和大量训练进行决策或预测的能力。换句话说，如果一台机器（程序、应用等）通过训练提高了预测准确性，那么它就“学习”了。 在课程中，您将学习以下内容： 1. 模型运作原理：了解计算图由连接的节点构成，节点通常有权重和偏差，以帮助确定最可能的路径。 2. 数据收集与格式化：学习如何收集大量数据，并确保数据格式一致（图像大小、色彩方案等），同时标注数据，并将数据划分为互斥的训练集和测试集。 3. 模型构建：学习如何确定最适合的模型架构以及针对所解决问题的最佳算法。 4. 训练、测试与评估：了解模型如何根据输入、权重和偏差选择神经网络的路径。在监督学习中，您将向模型展示给定输入的正确输出，模型会调整神经元的权重和偏差，以最小化其输出与正确答案之间的差异。 快来注册，和Mammoth Interactive一起学习吧！
"Excellent! Thank you for all your hard work." - Mammoth Interactive student Inderpal"Great! Well explained and the instructor provides clear examples" - Mark T.Dive into a world of data science and analysis with a wide range of examples including the CIFAR 100 image dataset, Xcode development for Apple, Swift coding, CoreML, image recognition, and structuring data with pandas.This Mammoth Interactive course was funded by a #1 project on KickstarterLearn Android Studio, Java, app development, Pycharm, Python coding, Tensforflow and more with Mammoth Interactive.Build advanced projects using machine learning including advanced the MNIST database with neuron functions. Build a text summarizer and learn object localization, object recognition and Tensorboard.Machine learning is a machine's ability to make decisions or predictions based on previous exposure to data and extensive training. In other words, if a machine (program, app, etc.) improves its prediction accuracy through training then it has "learned".Learn How Models WorkComputational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely. There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluatingData Gathering and FormattingYou will learn to gather plenty of data for the model to learn from.All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. Also divide data into mutually exclusive training and testing sets.Model BuildingYou will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you're trying to solve.Training, Testing and EvaluatingThe model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network. In supervised learning, we show the model what the correct outputs are for a given set of inputs and the model alters the weights and biases of neurons to minimize the difference between its output and the correct answer.Enroll Now to Learn with Mammoth Interactive
