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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tensorflow-advanced-lasso-linear-regression-with-python/
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课程名称：TensorFlow 2.x与Python的深度学习入门项目 课程概述： 欢迎参加《TensorFlow 2.0的深度学习入门课程》。在本课程中，您将学习高级线性回归技术，并能够构建各种回归问题。通过这些知识，您将能够解决现实世界中的问题，如客户生命周期价值预测、预测分析等。 您将学习： - TensorFlow 2.x - Google Colab - 线性回归 - 梯度下降算法 - 数据分析 - 回归分析 - 特征工程与选择，包括Lasso回归 - 模型评估 课程项目将围绕“客户收入（生命周期价值）预测”进行，使用梯度下降算法进行分析。问题背景为一家大型儿童教育玩具公司希望分析客户数据，以便更好地理解收入情况。 课程内容包括： 1. 数据分析与预处理：分析客户数据，并据此进行数据预处理，学习必要的数据分析、多重共线性和因子分析等。 2. 特征工程： - Lasso回归 - 确定最佳惩罚因子 - 特征选择 3. 管道模型 4. 评估 课程将从TensorFlow 2.x的基础知识开始，逐步深入线性回归及优化函数（如梯度下降）背后的直觉。
Welcome to the Course Introduction to Deep Learning with TensorFlow 2.0:In this course, you will learn advanced linear regression technique process and with this, you can be able to build any regression problem. Using this you can solve real-world problems like customer lifetime value, predictive analytics, etc.What you will Learn· TensorFlow 2.x· Google Colab· Linear Regression· Gradient Descent Algorithm· Data Analysis· Regression· Feature Engineering and Selection with Lasso Regression.· Model EvaluationAll the above-mentioned techniques are explained in TensorFlow. In this course, you will work on the Project Customer Revenue (Lifetime value) Prediction using Gradient Descent AlgorithmProblem Statement: A large child education toy company that sells educational tablets and gaming systems both online and in retail stores wanted to analyze the customer data. The goal of the problem is to determine the following objective as shown below.1. Data Analysis & Pre-processing: Analyse customer data and draw the insights w.r.t revenue and based on the insights we will do data pre-processing. In this module, you will learn the following.1. Necessary Data Analysis2. Multi-collinearity3. Factor Analysis2. Feature Engineering:1. Lasso Regression2. Identify the optimal penalty factor.3. Feature Selection3. Pipeline Model4. EvaluationWe will start with the basics of TensorFlow 2.x to advanced techniques in it. Then we drive into intuition behind linear regression and optimization function like gradient descent.
