[FreeCourseLab.com] Udemy - 2021 Python for Machine Learning & Data Science Masterclass

磁力链接/BT种子名称

[FreeCourseLab.com] Udemy - 2021 Python for Machine Learning & Data Science Masterclass

磁力链接/BT种子简介

种子哈希：31d804aa5a716fd0a94a200cdad38a3b1836d8a1
文件大小： 10.48G
已经下载：3640次
下载速度：极快
收录时间：2021-07-09
最近下载：2025-10-10

地址随时变，回家记住路

小野猫.com黑猫警长.com哆啦a猫.com御猫.com科目三.com猫哭老鼠.com女猫.com☜☜☜找最新地址请保存左面网址

磁力链接

magnet:?xt=urn:btih:31D804AA5A716FD0A94A200CDAD38A3B1836D8A1
推荐使用PIKPAK网盘下载资源，PIKPAK是目前最好用网盘，10T超大空间，不和谐任何资源，支持无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 在线观看 世界之窗 含羞草 极乐禁地 91视频 51品茶 逼哩逼哩 萝莉岛 欲漫涩 91短视频 成人快手 抖阴破解版 ai色色 pilipili 草榴社区 哆哔涩漫 好色先生 疯马秀 TikTok成人版 悠悠禁区 波多 听泉鉴鲍 xvideo 外网天堂 PornHub 抖音Max 呦乐园 拔萝卜 糖心视频 麻豆Vlog 少女日记

资源截图

API Integration

最近搜索

文件列表

  • 23 Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4 218.8 MB
  • 05 Pandas/028 Pandas Project Exercise Solutions.mp4 181.0 MB
  • 13 Logistic Regression/016 Logistic Regression Project Exercise - Solutions.mp4 152.6 MB
  • 08 Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three.mp4 143.9 MB
  • 17 Random Forests/007 Coding Classification with Random Forest Classifier - Part Two.mp4 136.7 MB
  • 05 Pandas/026 Pandas Pivot Tables.mp4 135.0 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4 134.2 MB
  • 11 Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4 126.5 MB
  • 11 Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4 123.3 MB
  • 16 Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4 121.5 MB
  • 23 Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4 120.4 MB
  • 07 Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4 116.5 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4 114.4 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions.mp4 113.3 MB
  • 22 K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4 113.1 MB
  • 08 Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4 111.4 MB
  • 06 Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4 111.0 MB
  • 07 Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4 110.8 MB
  • 11 Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4 110.4 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4 110.2 MB
  • 13 Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 110.2 MB
  • 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4 110.1 MB
  • 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4 107.9 MB
  • 05 Pandas/023 Pandas Input and Output - HTML Tables.mp4 107.4 MB
  • 08 Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4 106.9 MB
  • 04 NumPy/002 NumPy Arrays.mp4 104.3 MB
  • 16 Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp4 103.5 MB
  • 22 K-Means Clustering/004 K-Means Clustering - Coding Part One.mp4 102.2 MB
  • 05 Pandas/004 DataFrames - Part One - Creating a DataFrame.mp4 102.1 MB
  • 06 Matplotlib/006 Matplotlib - Subplots Functionality.mp4 100.9 MB
  • 05 Pandas/025 Pandas Input and Output - SQL Databases.mp4 100.8 MB
  • 25 PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp4 99.8 MB
  • 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp4 99.1 MB
  • 15 Support Vector Machines/010 Support Vector Machine Project Solutions.mp4 98.0 MB
  • 08 Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp4 97.7 MB
  • 05 Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp4 97.6 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp4 95.6 MB
  • 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp4 93.7 MB
  • 07 Seaborn Data Visualizations/011 Seaborn Grid Plots.mp4 91.2 MB
  • 05 Pandas/014 GroupBy Operations - Part One.mp4 91.1 MB
  • 05 Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp4 89.5 MB
  • 17 Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 89.0 MB
  • 07 Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 88.7 MB
  • 01 Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp4 88.6 MB
  • 05 Pandas/006 DataFrames - Part Three - Working with Columns.mp4 88.2 MB
  • 10 Linear Regression/006 Python coding Simple Linear Regression.mp4 88.0 MB
  • 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp4 87.2 MB
  • 10 Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp4 85.2 MB
  • 22 K-Means Clustering/005 K-Means Clustering Coding Part Two.mp4 84.5 MB
  • 22 K-Means Clustering/007 K-Means Color Quantization - Part One.mp4 84.3 MB
  • 05 Pandas/021 Pandas - Time Methods for Date and Time Data.mp4 84.1 MB
  • 22 K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp4 83.6 MB
  • 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp4 80.0 MB
  • 05 Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp4 78.0 MB
  • 25 PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp4 77.7 MB
  • 05 Pandas/013 Missing Data - Pandas Operations.mp4 77.2 MB
  • 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/001 Introduction to Supervised Learning Capstone Project.mp4 76.9 MB
  • 10 Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp4 76.8 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp4 76.8 MB
  • 05 Pandas/007 DataFrames - Part Four - Working with Rows.mp4 76.1 MB
  • 05 Pandas/008 Pandas - Conditional Filtering.mp4 72.6 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp4 70.0 MB
  • 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp4 69.6 MB
  • 22 K-Means Clustering/008 K-Means Color Quantization - Part Two.mp4 67.9 MB
  • 13 Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 66.7 MB
  • 18 Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp4 66.2 MB
  • 13 Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp4 65.6 MB
  • 22 K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp4 65.5 MB
  • 10 Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 64.8 MB
  • 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp4 64.6 MB
  • 10 Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp4 64.4 MB
  • 10 Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp4 64.0 MB
  • 22 K-Means Clustering/006 K-Means Clustering Coding Part Three.mp4 62.4 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp4 62.3 MB
  • 22 K-Means Clustering/009 K-Means Clustering Exercise Overview.mp4 62.2 MB
  • 11 Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp4 61.8 MB
  • 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp4 60.8 MB
  • 01 Introduction to Course/UNZIP-FOR-NOTEBOOKS-Ver7.zip 59.6 MB
  • 10 Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp4 58.4 MB
  • 13 Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 57.6 MB
  • 10 Linear Regression/002 Linear Regression - Algorithm History.mp4 57.4 MB
  • 05 Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp4 56.3 MB
  • 15 Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 55.3 MB
  • 22 K-Means Clustering/003 K-Means Clustering Theory.mp4 54.9 MB
  • 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp4 54.6 MB
  • 23 Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp4 54.5 MB
  • 07 Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 54.1 MB
  • 07 Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp4 53.6 MB
  • 17 Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 53.1 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp4 52.6 MB
  • 06 Matplotlib/010 Matplotlib Exercise Questions Overview.mp4 51.3 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp4 51.0 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp4 49.2 MB
  • 15 Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp4 48.5 MB
  • 17 Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 47.8 MB
  • 05 Pandas/020 Pandas - Text Methods for String Data.mp4 47.3 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp4 47.3 MB
  • 07 Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 47.2 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp4 46.7 MB
  • 07 Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp4 46.6 MB
  • 06 Matplotlib/008 Matplotlib Styling - Colors and Styles.mp4 46.4 MB
  • 17 Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp4 45.5 MB
  • 18 Boosting Methods/003 AdaBoost Theory and Intuition.mp4 43.6 MB
  • 11 Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp4 42.7 MB
  • 03 Machine Learning Pathway Overview/001 Machine Learning Pathway.mp4 42.5 MB
  • 05 Pandas/017 Combining DataFrames - Inner Merge.mp4 42.2 MB
  • 05 Pandas/005 DataFrames - Part Two - Basic Properties.mp4 42.2 MB
  • 10 Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp4 42.0 MB
  • 04 NumPy/003 NumPy Indexing and Selection.mp4 41.6 MB
  • 05 Pandas/027 Pandas Project Exercise Overview.mp4 41.3 MB
  • 13 Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 39.2 MB
  • 05 Pandas/022 Pandas Input and Output - CSV Files.mp4 38.9 MB
  • 05 Pandas/016 Combining DataFrames - Concatenation.mp4 38.6 MB
  • 10 Linear Regression/014 Polynomial Regression - Training and Evaluation.mp4 38.1 MB
  • 10 Linear Regression/015 Bias Variance Trade-Off.mp4 38.0 MB
  • 13 Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 37.8 MB
  • 04 NumPy/004 NumPy Operations.mp4 37.8 MB
  • 16 Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp4 37.3 MB
  • 15 Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp4 37.0 MB
  • 04 NumPy/006 Numpy Exercises - Solutions.mp4 36.6 MB
  • 06 Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp4 36.5 MB
  • 15 Support Vector Machines/009 Support Vector Machine Project Overview.mp4 36.5 MB
  • 07 Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp4 36.1 MB
  • 09 Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp4 35.2 MB
  • 02 OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp4 35.1 MB
  • 13 Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 34.2 MB
  • 02 OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp4 33.6 MB
  • 21 Unsupervised Learning/001 Unsupervised Learning Overview.mp4 33.3 MB
  • 11 Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp4 33.0 MB
  • 06 Matplotlib/002 Matplotlib Basics.mp4 32.6 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/009 Text Classification Project Exercise Overview.mp4 32.0 MB
  • 02 OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp4 31.2 MB
  • 25 PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp4 31.2 MB
  • 10 Linear Regression/010 Linear Regression - Residual Plots.mp4 31.1 MB
  • 10 Linear Regression/020 Introduction to Cross Validation.mp4 30.7 MB
  • 10 Linear Regression/005 Linear Regression - Gradient Descent.mp4 30.6 MB
  • 05 Pandas/002 Series - Part One.mp4 30.0 MB
  • 16 Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 29.6 MB
  • 17 Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp4 28.7 MB
  • 05 Pandas/012 Missing Data - Overview.mp4 28.6 MB
  • 05 Pandas/003 Series - Part Two.mp4 27.4 MB
  • 05 Pandas/024 Pandas Input and Output - Excel Files.mp4 27.2 MB
  • 02 OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp4 27.1 MB
  • 06 Matplotlib/009 Advanced Matplotlib Commands (Optional).mp4 26.5 MB
  • 22 K-Means Clustering/002 Clustering General Overview.mp4 26.1 MB
  • 01 Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp4 25.7 MB
  • 10 Linear Regression/019 Feature Scaling.mp4 25.5 MB
  • 13 Logistic Regression/015 Logistic Regression Exercise Project Overview.mp4 25.5 MB
  • 17 Random Forests/002 Random Forests - History and Motivation.mp4 25.2 MB
  • 14 KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp4 24.7 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp4 24.7 MB
  • 13 Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp4 24.6 MB
  • 10 Linear Regression/017 Polynomial Regression - Model Deployment.mp4 24.4 MB
  • 01 Introduction to Course/005 Environment Setup.mp4 24.4 MB
  • 10 Linear Regression/007 Overview of Scikit-Learn and Python.mp4 24.3 MB
  • 18 Boosting Methods/006 Gradient Boosting Theory.mp4 24.1 MB
  • 18 Boosting Methods/004 AdaBoost Coding Part One - The Data.mp4 23.9 MB
  • 10 Linear Regression/012 Polynomial Regression - Theory and Motivation.mp4 23.3 MB
  • 05 Pandas/019 Combining DataFrames - Outer Merge.mp4 23.3 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 23.1 MB
  • 18 Boosting Methods/002 Boosting Methods - Motivation and History.mp4 23.0 MB
  • 13 Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp4 22.8 MB
  • 14 KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp4 22.1 MB
  • 09 Machine Learning Concepts Overview/002 Why Machine Learning_.mp4 22.0 MB
  • 16 Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp4 20.4 MB
  • 25 PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp4 20.0 MB
  • 09 Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp4 19.0 MB
  • 16 Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp4 18.6 MB
  • 13 Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp4 18.2 MB
  • 10 Linear Regression/026 Linear Regression Project - Data Overview.mp4 17.8 MB
  • 10 Linear Regression/004 Linear Regression - Cost Functions.mp4 17.4 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp4 17.3 MB
  • 05 Pandas/018 Combining DataFrames - Left and Right Merge.mp4 17.2 MB
  • 06 Matplotlib/007 Matplotlib Styling - Legends.mp4 17.0 MB
  • 13 Logistic Regression/011 Classification Metrics - ROC Curves.mp4 16.9 MB
  • 07 Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 16.8 MB
  • 07 Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp4 16.6 MB
  • 15 Support Vector Machines/002 History of Support Vector Machines.mp4 16.3 MB
  • 10 Linear Regression/021 Regularization Data Setup.mp4 16.2 MB
  • 07 Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp4 15.8 MB
  • 13 Logistic Regression/002 Introduction to Logistic Regression Section.mp4 14.6 MB
  • 17 Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp4 14.4 MB
  • 15 Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp4 14.0 MB
  • 09 Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp4 13.8 MB
  • 10 Linear Regression/018 Regularization Overview.mp4 13.7 MB
  • 06 Matplotlib/003 Matplotlib - Understanding the Figure Object.mp4 12.3 MB
  • 06 Matplotlib/005 Matplotlib - Figure Parameters.mp4 12.0 MB
  • 06 Matplotlib/001 Introduction to Matplotlib.mp4 11.9 MB
  • 13 Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp4 11.6 MB
  • 07 Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp4 11.1 MB
  • 07 Seaborn Data Visualizations/001 Introduction to Seaborn.mp4 11.0 MB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp4 10.4 MB
  • 09 Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp4 10.1 MB
  • 04 NumPy/005 NumPy Exercises.mp4 10.1 MB
  • 17 Random Forests/003 Random Forests - Key Hyperparameters.mp4 10.1 MB
  • 05 Pandas/001 Introduction to Pandas.mp4 9.1 MB
  • 04 NumPy/001 Introduction to NumPy.mp4 8.3 MB
  • 02 OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp4 8.2 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/moviereviews.csv 7.6 MB
  • 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/17-Supervised-Learning-Capstone-Project.zip 7.4 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/001 Introduction to NLP and Naive Bayes Section.mp4 7.1 MB
  • 16 Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp4 6.6 MB
  • 25 PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp4 6.4 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp4 6.2 MB
  • 22 K-Means Clustering/20-Kmeans-Clustering.zip 6.1 MB
  • 23 Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp4 6.1 MB
  • 14 KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp4 5.2 MB
  • 22 K-Means Clustering/bank-full.csv 5.2 MB
  • 22 K-Means Clustering/001 Introduction to K-Means Clustering Section.mp4 4.8 MB
  • 15 Support Vector Machines/001 Introduction to Support Vector Machines.mp4 4.5 MB
  • 18 Boosting Methods/001 Introduction to Boosting Section.mp4 4.3 MB
  • 17 Random Forests/001 Introduction to Random Forests Section.mp4 4.3 MB
  • 17 Random Forests/15-Random-Forests.zip 4.1 MB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/22-DBSCAN.zip 3.7 MB
  • 10 Linear Regression/001 Introduction to Linear Regression Section.mp4 3.5 MB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/airline-tweets.csv 3.4 MB
  • 16 Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp4 2.7 MB
  • 13 Logistic Regression/11-Logistic-Regression-Models.zip 2.1 MB
  • 16 Tree Based Methods_ Decision Tree Learning/14-Decision-Trees.zip 1.9 MB
  • 15 Support Vector Machines/13-Support-Vector-Machines.zip 1.6 MB
  • 14 KNN - K Nearest Neighbors/12-K-Nearest-Neighbors.zip 1.4 MB
  • 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/Telco-Customer-Churn.csv 976.5 kB
  • 18 Boosting Methods/16-Boosted-Trees.zip 940.0 kB
  • 23 Hierarchical Clustering/21-Hierarchical-Clustering.zip 636.5 kB
  • 18 Boosting Methods/mushrooms.csv 374.0 kB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/18-Naive-Bayes-and-NLP.zip 197.1 kB
  • 22 K-Means Clustering/palm-trees.jpg 176.9 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-circles.csv 61.3 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-moons.csv 60.1 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-blobs.csv 57.2 kB
  • 17 Random Forests/data-banknote-authentication.csv 46.5 kB
  • 23 Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn_en.srt 43.3 kB
  • 11 Feature Engineering and Data Preparation/003 Dealing with Outliers_en.srt 42.2 kB
  • 05 Pandas/028 Pandas Project Exercise Solutions_en.srt 39.7 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-two-blobs-outliers.csv 39.2 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/cluster-two-blobs.csv 39.2 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions_en.srt 39.0 kB
  • 11 Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns_en.srt 37.6 kB
  • 22 K-Means Clustering/CIA-Country-Facts.csv 33.5 kB
  • 16 Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model_en.srt 33.5 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods_en.srt 33.4 kB
  • 05 Pandas/026 Pandas Pivot Tables_en.srt 33.0 kB
  • 04 NumPy/002 NumPy Arrays_en.srt 32.7 kB
  • 05 Pandas/021 Pandas - Time Methods for Date and Time Data_en.srt 32.5 kB
  • 11 Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows_en.srt 32.2 kB
  • 13 Logistic Regression/016 Logistic Regression Project Exercise - Solutions_en.vtt 31.6 kB
  • 08 Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three_en.srt 31.6 kB
  • 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K_en.vtt 31.4 kB
  • 22 K-Means Clustering/004 K-Means Clustering - Coding Part One_en.srt 31.1 kB
  • 07 Seaborn Data Visualizations/002 Scatterplots with Seaborn_en.srt 30.4 kB
  • 05 Pandas/025 Pandas Input and Output - SQL Databases_en.srt 30.1 kB
  • 15 Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics_en.srt 30.0 kB
  • 16 Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data_en.srt 30.0 kB
  • 05 Pandas/004 DataFrames - Part One - Creating a DataFrame_en.srt 29.7 kB
  • 18 Boosting Methods/003 AdaBoost Theory and Intuition_en.srt 29.6 kB
  • 06 Matplotlib/006 Matplotlib - Subplots Functionality_en.srt 29.3 kB
  • 07 Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn_en.srt 28.9 kB
  • 10 Linear Regression/006 Python coding Simple Linear Regression_en.srt 28.8 kB
  • 17 Random Forests/007 Coding Classification with Random Forest Classifier - Part Two_en.vtt 28.6 kB
  • 05 Pandas/013 Missing Data - Pandas Operations_en.srt 28.1 kB
  • 05 Pandas/008 Pandas - Conditional Filtering_en.srt 27.8 kB
  • 08 Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One_en.srt 27.5 kB
  • 18 Boosting Methods/005 AdaBoost Coding Part Two - The Model_en.srt 27.2 kB
  • 22 K-Means Clustering/005 K-Means Clustering Coding Part Two_en.srt 27.2 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition_en.srt 27.1 kB
  • 25 PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python_en.srt 26.9 kB
  • 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks_en.vtt 26.8 kB
  • 05 Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns_en.srt 26.6 kB
  • 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/001 Introduction to Supervised Learning Capstone Project_en.srt 26.3 kB
  • 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks_en.srt 26.3 kB
  • 10 Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation_en.srt 26.2 kB
  • 23 Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization_en.srt 26.0 kB
  • 13 Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math_en.srt 25.4 kB
  • 07 Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn_en.srt 25.4 kB
  • 02 OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One_en.srt 25.2 kB
  • 06 Matplotlib/011 Matplotlib Exercise Questions - Solutions_en.srt 25.1 kB
  • 11 Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation_en.srt 24.7 kB
  • 05 Pandas/020 Pandas - Text Methods for String Data_en.srt 24.5 kB
  • 13 Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model_en.srt 24.4 kB
  • 10 Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split_en.srt 24.3 kB
  • 22 K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two_en.srt 24.1 kB
  • 08 Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two_en.srt 24.0 kB
  • 13 Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation_en.srt 24.0 kB
  • 05 Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting_en.srt 24.0 kB
  • 10 Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression_en.srt 23.6 kB
  • 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation_en.vtt 23.5 kB
  • 13 Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood_en.srt 23.5 kB
  • 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net_en.vtt 23.2 kB
  • 10 Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares_en.srt 23.1 kB
  • 15 Support Vector Machines/010 Support Vector Machine Project Solutions_en.vtt 23.0 kB
  • 07 Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions_en.srt 22.9 kB
  • 05 Pandas/023 Pandas Input and Output - HTML Tables_en.srt 22.9 kB
  • 13 Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA_en.srt 22.4 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split_en.srt 22.2 kB
  • 01 Introduction to Course/003 Anaconda Python and Jupyter Install and Setup_en.srt 22.1 kB
  • 05 Pandas/014 GroupBy Operations - Part One_en.srt 21.9 kB
  • 22 K-Means Clustering/006 K-Means Clustering Coding Part Three_en.srt 21.9 kB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions_en.vtt 21.8 kB
  • 22 K-Means Clustering/008 K-Means Color Quantization - Part Two_en.srt 21.8 kB
  • 22 K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One_en.srt 21.6 kB
  • 07 Seaborn Data Visualizations/012 Seaborn - Matrix Plots_en.srt 21.6 kB
  • 05 Pandas/007 DataFrames - Part Four - Working with Rows_en.srt 21.6 kB
  • 06 Matplotlib/008 Matplotlib Styling - Colors and Styles_en.srt 21.6 kB
  • 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two_en.vtt 21.5 kB
  • 06 Matplotlib/004 Matplotlib - Implementing Figures and Axes_en.srt 21.5 kB
  • 05 Pandas/015 GroupBy Operations - Part Two - MultiIndex_en.srt 21.4 kB
  • 23 Hierarchical Clustering/cluster-mpg.csv 21.3 kB
  • 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two_en.srt 21.2 kB
  • 10 Linear Regression/022 L2 Regularization - Ridge Regression Theory_en.srt 21.2 kB
  • 05 Pandas/006 DataFrames - Part Three - Working with Columns_en.srt 21.1 kB
  • 08 Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview_en.srt 21.1 kB
  • 07 Seaborn Data Visualizations/011 Seaborn Grid Plots_en.srt 21.0 kB
  • 17 Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models_en.srt 20.9 kB
  • 22 K-Means Clustering/007 K-Means Color Quantization - Part One_en.srt 20.9 kB
  • 05 Pandas/009 Pandas - Useful Methods - Apply on Single Column_en.srt 20.7 kB
  • 10 Linear Regression/010 Linear Regression - Residual Plots_en.srt 20.7 kB
  • 07 Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types_en.srt 20.6 kB
  • 11 Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options_en.srt 20.6 kB
  • 17 Random Forests/007 Coding Classification with Random Forest Classifier - Part Two_en.srt 20.5 kB
  • 10 Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial_en.srt 20.4 kB
  • 10 Linear Regression/020 Introduction to Cross Validation_en.srt 20.3 kB
  • 09 Machine Learning Concepts Overview/004 Supervised Machine Learning Process_en.srt 20.2 kB
  • 06 Matplotlib/002 Matplotlib Basics_en.srt 20.1 kB
  • 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation_en.vtt 20.1 kB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions_en.srt 19.9 kB
  • 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One_en.vtt 19.8 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search_en.srt 19.7 kB
  • 15 Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins_en.srt 19.0 kB
  • 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions_en.vtt 19.0 kB
  • 05 Pandas/017 Combining DataFrames - Inner Merge_en.srt 19.0 kB
  • 05 Pandas/012 Missing Data - Overview_en.srt 18.8 kB
  • 02 OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two_en.srt 18.5 kB
  • 17 Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error_en.srt 18.4 kB
  • 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough_en.vtt 17.9 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split_en.srt 17.9 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering_en.srt 17.8 kB
  • 25 PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn_en.srt 17.7 kB
  • 23 Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition_en.srt 17.7 kB
  • 22 K-Means Clustering/003 K-Means Clustering Theory_en.srt 17.7 kB
  • 17 Random Forests/002 Random Forests - History and Motivation_en.srt 17.6 kB
  • 11 Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data_en.srt 17.4 kB
  • 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net_en.srt 17.4 kB
  • 14 KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition_en.srt 17.3 kB
  • 10 Linear Regression/005 Linear Regression - Gradient Descent_en.srt 17.1 kB
  • 18 Boosting Methods/004 AdaBoost Coding Part One - The Data_en.srt 17.1 kB
  • 05 Pandas/022 Pandas Input and Output - CSV Files_en.srt 17.0 kB
  • 02 OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three_en.srt 17.0 kB
  • 22 K-Means Clustering/002 Clustering General Overview_en.srt 16.9 kB
  • 16 Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two_en.srt 16.8 kB
  • 10 Linear Regression/013 Polynomial Regression - Creating Polynomial Features_en.srt 16.8 kB
  • 15 Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One_en.srt 16.8 kB
  • 25 PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two_en.srt 16.8 kB
  • 04 NumPy/003 NumPy Indexing and Selection_en.srt 16.6 kB
  • 17 Random Forests/004 Random Forests - Number of Estimators and Features in Subsets_en.srt 16.6 kB
  • 18 Boosting Methods/006 Gradient Boosting Theory_en.srt 16.5 kB
  • 10 Linear Regression/015 Bias Variance Trade-Off_en.srt 16.3 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions_en.vtt 16.3 kB
  • 03 Machine Learning Pathway Overview/001 Machine Learning Pathway_en.srt 16.2 kB
  • 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One_en.vtt 16.2 kB
  • 07 Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn_en.srt 16.1 kB
  • 25 PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One_en.srt 16.0 kB
  • 17 Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models_en.srt 15.8 kB
  • 05 Pandas/003 Series - Part Two_en.srt 15.7 kB
  • 17 Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials_en.srt 15.7 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score_en.vtt 15.6 kB
  • 05 Pandas/016 Combining DataFrames - Concatenation_en.srt 15.4 kB
  • 07 Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types_en.srt 15.4 kB
  • 10 Linear Regression/019 Feature Scaling_en.srt 15.2 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/wholesome-customers-data.csv 15.0 kB
  • 09 Machine Learning Concepts Overview/002 Why Machine Learning__en.srt 15.0 kB
  • 07 Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn_en.srt 15.0 kB
  • 05 Pandas/019 Combining DataFrames - Outer Merge_en.srt 14.9 kB
  • 01 Introduction to Course/005 Environment Setup_en.srt 14.8 kB
  • 13 Logistic Regression/016 Logistic Regression Project Exercise - Solutions_en.srt 14.7 kB
  • 10 Linear Regression/014 Polynomial Regression - Training and Evaluation_en.srt 14.5 kB
  • 13 Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy_en.srt 14.3 kB
  • 02 OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions_en.srt 13.8 kB
  • 22 K-Means Clustering/009 K-Means Clustering Exercise Overview_en.srt 13.8 kB
  • 05 Pandas/002 Series - Part One_en.srt 13.7 kB
  • 05 Pandas/005 DataFrames - Part Two - Basic Properties_en.srt 13.6 kB
  • 16 Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History_en.srt 13.5 kB
  • 10 Linear Regression/002 Linear Regression - Algorithm History_en.srt 13.4 kB
  • 21 Unsupervised Learning/001 Unsupervised Learning Overview_en.srt 13.2 kB
  • 15 Support Vector Machines/010 Support Vector Machine Project Solutions_en.srt 13.1 kB
  • 10 Linear Regression/021 Regularization Data Setup_en.srt 12.7 kB
  • 22 K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three_en.srt 12.4 kB
  • 04 NumPy/004 NumPy Operations_en.srt 12.3 kB
  • 13 Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA_en.srt 12.3 kB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/002 Naive Bayes Algorithm - Part One - Bayes Theorem_en.srt 12.1 kB
  • 09 Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms_en.srt 11.9 kB
  • 06 Matplotlib/003 Matplotlib - Understanding the Figure Object_en.srt 11.8 kB
  • 16 Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One_en.srt 11.8 kB
  • 10 Linear Regression/004 Linear Regression - Cost Functions_en.srt 11.7 kB
  • 07 Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview_en.srt 11.5 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate_en.srt 11.5 kB
  • 10 Linear Regression/012 Polynomial Regression - Theory and Motivation_en.srt 11.5 kB
  • 16 Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity_en.srt 11.4 kB
  • 13 Logistic Regression/011 Classification Metrics - ROC Curves_en.srt 11.3 kB
  • 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One_en.srt 11.3 kB
  • 10 Linear Regression/007 Overview of Scikit-Learn and Python_en.vtt 11.2 kB
  • 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation_en.srt 11.2 kB
  • 05 Pandas/024 Pandas Input and Output - Excel Files_en.srt 11.1 kB
  • 04 NumPy/006 Numpy Exercises - Solutions_en.srt 11.1 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory_en.srt 11.0 kB
  • 06 Matplotlib/007 Matplotlib Styling - Legends_en.srt 10.6 kB
  • 10 Linear Regression/018 Regularization Overview_en.srt 10.6 kB
  • 10 Linear Regression/007 Overview of Scikit-Learn and Python_en.srt 10.4 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview_en.srt 10.2 kB
  • 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One_en.srt 10.2 kB
  • 05 Pandas/027 Pandas Project Exercise Overview_en.srt 9.8 kB
  • 13 Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training_en.srt 9.8 kB
  • 06 Matplotlib/010 Matplotlib Exercise Questions Overview_en.srt 9.6 kB
  • 05 Pandas/018 Combining DataFrames - Left and Right Merge_en.srt 9.3 kB
  • 18 Boosting Methods/002 Boosting Methods - Motivation and History_en.srt 9.2 kB
  • 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough_en.srt 9.1 kB
  • 07 Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types_en.srt 9.0 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions_en.srt 9.0 kB
  • 07 Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types_en.srt 8.9 kB
  • 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions_en.srt 8.8 kB
  • 09 Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section_en.srt 8.8 kB
  • 13 Logistic Regression/002 Introduction to Logistic Regression Section_en.srt 8.6 kB
  • 10 Linear Regression/017 Polynomial Regression - Model Deployment_en.srt 8.6 kB
  • 13 Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score_en.srt 8.5 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score_en.srt 8.3 kB
  • 13 Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function_en.srt 8.3 kB
  • 22 K-Means Clustering/country-iso-codes.csv 8.1 kB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/009 Text Classification Project Exercise Overview_en.srt 8.0 kB
  • 10 Linear Regression/026 Linear Regression Project - Data Overview_en.srt 7.9 kB
  • 06 Matplotlib/005 Matplotlib - Figure Parameters_en.srt 7.8 kB
  • 13 Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic_en.srt 7.4 kB
  • 05 Pandas/001 Introduction to Pandas_en.srt 7.4 kB
  • 01 Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!_en.srt 7.3 kB
  • 15 Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition_en.srt 7.3 kB
  • 15 Support Vector Machines/009 Support Vector Machine Project Overview_en.srt 7.0 kB
  • 17 Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data_en.srt 7.0 kB
  • 06 Matplotlib/001 Introduction to Matplotlib_en.srt 6.9 kB
  • 15 Support Vector Machines/002 History of Support Vector Machines_en.srt 6.7 kB
  • 07 Seaborn Data Visualizations/001 Introduction to Seaborn_en.srt 6.7 kB
  • 06 Matplotlib/009 Advanced Matplotlib Commands (Optional)_en.srt 6.7 kB
  • 13 Logistic Regression/015 Logistic Regression Exercise Project Overview_en.srt 6.6 kB
  • 16 Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology_en.srt 6.6 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview_en.srt 6.0 kB
  • 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation_en.srt 5.5 kB
  • 14 KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview_en.srt 5.4 kB
  • 12 Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction_en.srt 5.2 kB
  • 09 Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning_en.srt 4.8 kB
  • 17 Random Forests/003 Random Forests - Key Hyperparameters_en.srt 4.6 kB
  • 25 PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis_en.srt 4.1 kB
  • 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K_en.srt 4.0 kB
  • 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/001 Introduction to NLP and Naive Bayes Section_en.srt 3.8 kB
  • 14 KNN - K Nearest Neighbors/001 Introduction to KNN Section_en.srt 3.7 kB
  • 22 K-Means Clustering/001 Introduction to K-Means Clustering Section_en.srt 3.6 kB
  • 04 NumPy/001 Introduction to NumPy_en.srt 3.1 kB
  • 17 Random Forests/001 Introduction to Random Forests Section_en.srt 2.9 kB
  • 10 Linear Regression/001 Introduction to Linear Regression Section_en.srt 2.7 kB
  • 18 Boosting Methods/001 Introduction to Boosting Section_en.srt 2.7 kB
  • 02 OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions_en.srt 2.6 kB
  • 15 Support Vector Machines/001 Introduction to Support Vector Machines_en.srt 2.4 kB
  • 16 Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods_en.srt 2.3 kB
  • 04 NumPy/005 NumPy Exercises_en.srt 2.1 kB
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section_en.srt 1.4 kB
  • 23 Hierarchical Clustering/001 Introduction to Hierarchical Clustering_en.srt 1.2 kB
  • 11 Feature Engineering and Data Preparation/001 A note from Jose on Feature Engineering and Data Preparation.html 990 Bytes
  • 01 Introduction to Course/004 Note on Environment Setup - Please read me!.html 857 Bytes
  • 01 Introduction to Course/001 Welcome to the Course!.html 598 Bytes
  • 13 Logistic Regression/001 Early Bird Note on Downloading .zip for Logistic Regression Notes.html 523 Bytes
  • 02 OPTIONAL_ Python Crash Course/001 OPTIONAL_ Python Crash Course.html 472 Bytes
  • 01 Introduction to Course/requirements.txt 221 Bytes
  • 01 Introduction to Course/external-assets-links.txt 132 Bytes
  • [FreeCourseLab.com].url 126 Bytes
  • 24 DBSCAN - Density-based spatial clustering of applications with noise/external-assets-links.txt 103 Bytes

温馨提示

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！