使用 Tensorflow 進行機器學習 | Python 5 級
本機器學習課程透過實作專案教授 TensorFlow、線性迴歸、神經網路和模型調整等基本概念。學生使用資料集應用技能並創建自己的最終機器學習項目
AI Code Academy
平均評分：
4.7
評論數量：(1,743)
熱門課程
包含什麼
10 現場會議
10 上課時間項目
每週 2-4 小時. Projects are not mandatory but we strongly encourage students to complete them.評估
包括我們無法翻譯此文，請刷新頁面並再試一次。
課堂經歷
This comprehensive Machine Learning Fundamentals course covers essential concepts and practical applications across ten structured lessons. Beginning with an introduction to TensorFlow and basic machine learning principles, students progress through topics such as linear regression, model tuning strategies, K-NN clustering, decision forests, and neural networks, including feed-forward networks and convolutional neural networks (CNNs). Each lesson incorporates hands-on projects using datasets like MNIST and CIFAR10, where students implement algorithms, tune models for optimal performance, and visualize results. The course culminates in a final project phase where participants develop and present their own machine learning applications, exploring diverse topics from image classification to model ensembling and hyperparameter optimization. For a week to week program, check out the syllabus.
學習目標
This Machine Learning Fundamentals course covers TensorFlow introduction, basic principles, linear regression, model tuning, K-NN clustering, decision forests, and neural networks like CNNs.Through hands-on projects with MNIST and CIFAR10 datasets, students implement algorithms, optimize models, and present final projects on topics including image classification and hyperparameter optimization.
教學大綱
10 課程
超過 10 週課 1:
Intro To TensorFlow
Objectives:
Learn the basics of machine learning
What is learning?
History of machine learning
Regression vs classification
Introduce TensorFlow
Installing TensorFlow
What is a Tensor?
Low-level API and high-level API
How do Tensors perform computations?
Tensorflow variables
Project 1: Perform basic computations with TensorFlow
Matrix addition, matrix multiplication using tensors in TensorFlow 60 分鐘線上直播課
課 2:
Linear Regression
This project aims to teach linear regression fundamentals, including hypothesis setup with the equation 𝑦 = 𝑚 𝑥 + 𝑏 y=mx+b, where 𝑚 m represents weights and 𝑏 b denotes biases. Participants will learn to compute these parameters using gradient descent and understand the mean squared error cost function. Using TensorFlow and NumPy, they will implement a linear regression model, iteratively refining weights and biases over multiple epochs and visualizing model performance. 60 分鐘線上直播課
課 3:
Model Tuning
Objectives
Why do we need to tune models?
Convergence problems
Preparation
Strategies for tuning models
Grid Search
Random Search
Data transformation
Project 3: Tune the linear regression model to produce better results
Implement Grid Search
Implement Random Search
Implement a data transformation 60 分鐘線上直播課
課 4:
K-NN Clustering
Objectives:
How do we solve classification problems?
Problems with classification
Strategies and models to use
K-NN Algorithms
How do clustering algorithms work?
How does the K-NN algorithm perform classification
How do we measure distance?
What is the effect of the K hyperparameter?
Project 4: MNIST Classification with K-NN
Import the dataset and process it
Implement the K-NN algorithm
Visualize the results
Tune the model for better results 60 分鐘線上直播課
其他詳情
外部資源
學習者無需使用標準 Outschool 工具以外的任何應用程式或網站。
已加入 April, 2020
4.7
1743評論
熱門課程
教師專業知識和證書
**USE PROMO CODE: CODEAIPROMO10 FOR $10 OFF ANY COURSE - Valid until Nov, 25 **
~We offer early registration, sibling discounts, and multi-course bundles. ~
~Check out our complete Outschool offering here: https://shorturl.at/bcBGP ~
At AI Code Academy, we specialize in project-based STEM coding, AI, and mathematics programs for young learners. We are one of the few organizations that offer AI and machine learning courses tailored for kids. Our comprehensive curriculum spans from basic computer skills and Scratch coding to more advanced Python, Java, web design, game development, and AI machine learning projects.
Our unique focus is on introducing students to AI early, helping them grasp complex concepts like machine learning, data analysis, and smart devices, while also reinforcing mathematics skills, essential for their success in STEM fields.
With a team of passionate instructors—college students and recent graduates with degrees in Engineering and Computer Science—we provide hands-on, real-world projects that prepare students for future careers in AI, coding, robotics, and mathematics.評論
現場團體課程
US$27
每週或US$269 用於 10 課程每週1次，共 10 週
60 分鐘
有8 位學習者完成此課程
即時視訊會議
年齡: 13-18
4-8 每班學員人數
