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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-absolute-beginners/
课程评论：没有评论
课程名称：机器学习项目：从初学者到专业人士 课程概述：本课程最近更新，现包含8个项目，让你获得与机器学习不同概念的真实世界体验。未来还会有更多项目添加到课程中。如果你想要参与技术发展的未来，机器学习是一个理想的起点。我们精心设计了这个课程，将复杂的机器学习概念简化，使其更容易理解。 课程内容覆盖了机器学习的基础概念，包括必要的算法，使计算机能够思考、响应，并根据不同情境处理数据。课程从头开始，深入机器学习，逐步讲解最重要的原则。学习者需具备一定的数学基础以及Python编程经验，以便在实际项目中测试算法。 此课程包括多种机器学习算法，内容涵盖监督学习、无监督学习、强化学习以及神经网络等。学习者将在多个实际项目中应用这些算法，获得实战经验。课程还设置了每个章节末尾的测验，以及6个全新的项目，帮助你通过真实示例感受机器学习的强大。 课程项目包括： 1. **棋盘游戏评价预测** - 进行线性回归分析，预测棋盘游戏的平均评价。 2. **信用卡欺诈检测** - 使用概率密度进行异常检测，识别信用卡欺诈行为。 3. **Python自然语言处理入门** - 学习NLP方法，包括分词、词性识别和短语分块。 4. **深度学习中的物体识别高性能实现** - 基于CIFAR-10数据集实现深度神经网络。 5. **图像超分辨率与SRCNN** - 使用Tensorflow实现超分辨率卷积神经网络，改善图像质量。 6. **自然语言处理：文本分类** - 通过多分类算法解决文本分类任务。 7. **K均值聚类图像分析** - 利用K均值聚类分析和分类MNIST数据集的图像。 8. **基于主成分分析的数据压缩与可视化** - 将Iris数据集压缩为2D特征集并可视化。 这个课程使机器学习变得简单易懂，欢迎立即注册，踏入编程的未来！
Update: This course has been updated to include 8 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future!If you've ever wanted Jetsons to be real, well we aren't that far off from a future like that. If you've ever chatted with automated robots, then you've definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it's reach and making our devices smarter.Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that's placed before them.So, if you've ever wanted to play a role in the future of technology development, then here's your chance to get started with Machine Learning. Because machine learning is complex and tough, we've designed a course to help break it down into more simple concepts that are easier to understand.This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer.The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects.The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that's not all. In addition to quizzes that you'll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples!9 Projects That Are Included in This Course:Project 1 -Board Game Review Prediction - In this project, you'll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project.Project 2 - Credit Card Fraud Detection - In this project, you'll learn to focus on anomaly detection by using probability densities to detect credit card fraud.Project 3 - Getting Started with Natural Language Processing In Python - This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.Project 4- Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning - In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network.Project 5 - Image Super Resolution with the SRCNN - Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality.Project 6 - Natural Language Processing: Text Classification - In this project, you'll learn an advanced approach to Natural Language Processing by solving a text classification task using multiple classification algorithms. Project 7 - K-Means Clustering For Image Analysis - In this project, you'll learn how to use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset. Project 8 - Data Compression & Visualization Using Principle Component Analysis - This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering. All of this and so much more is included in this course. So, what are you waiting for?Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming.
