Suitable for machine learning, data analysis, data mining students and Python users

This course introduces how to use Python and its common libraries for data analysis and modeling, starting with data preprocessing, building machine learning models, and evaluating the effects. For each challenge, analyze the problem solving ideas and how to construct the appropriate model and give the appropriate evaluation method. In each case, students can quickly learn how to use pandas for data processing and analysis, use Matplotlib for visualization, and build machine learning models based on the SciKit-Learn library.

Catalog Siege lion; www.54gcshi.com/forum.php?m… Chapter 1: Using Python library to analyze and process Kobe Bryan’s career data How to use Anaconda to build a Python environment? How to use Anaconda to build a Python environment? How to use Anaconda to build python environment 分 钟 2: Credit Card fraud Detection 分 钟 8 Video Data Introduction and Challenges 课时12 课时12 video usage data generation strategy 11:00 chapter 3: analysis of iris data set 课时13 introduction and feature of video data classroom-based display 11:10 课时14 distribution rules of different features of video 06:32 课时15 video decision tree model parameter explanation 11:07 课时16 video decision tree parameter selection 09:28 课时17 video will establish a good decision tree visualization display 08:47 chapter 4: Titanic rescue prediction 课时18 video crew data analysis 06:10 课时19 video data pretreatment 13:36 课时20 video using regression algorithm for prediction 14:30 课时21 video using random forest improved model 12:56 课时22 video random forest feature importance analysis 10:40 课时 5: machine learning model of cascade structure 课时23 video cascade model principle 05:06 Class 24 Video data preprocessing and Heat map 10:25 Class 25 Video two-stage input feature production 06:35 Class 26 Video Prediction using cascade model The influence of different attribute indexes of video staff on the results 15:42 课 程 29 Video data preprocessing 12:03 课 程 30 Video Construction prediction Model 10:28 课 程 31 Video Analysis based on Clustering Model 05:42 Chapter 7: Handwriting Recognition using Neural Network (MNIST) 课时32 video tensorflow framework installation 07:09 课时33 video neural network model overview 12:52 课时34 video using tensorflow to set basic parameters 09:51 课时35 video convolutional neural network model 10:49 课时36 video building a complete neural network model 14:32 课时37 video training neural network model 12:34 chapter 8: principal component analysis (PCA) 课时38 video PCA principle introduction 05:34 课时39 video data preprocessing 08:42 课时40 video covariance analysis 10:27 课时41 video dimensionality reduction using PCA 07:46 Chapter 9: Stock price Prediction Based on NLP 课 hour 42 Video Data Introduction and Story background 04:11 课 hour 43 Video Feature extraction based on word frequency 10:25 课 Hour 44 Video improvement feature selection method 12:25 课 Hour 10: Data analysis of lending Company 12:08 Video data cleaning 12:08 Periods 46 Video data preprocessing 10:12 periods 47 Video profit methods and model evaluation 13:26 periods 48 Video Prediction results 12:47 periods 49 Video Python exercises 13:38