[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：978次
下载速度：极快
收录时间：2022-02-07
最近下载：2024-12-03

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:31D804AA5A716FD0A94A200CDAD38A3B1836D8A1
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，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 PIKPAK在线播放 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 暗网禁地 91短视频 TikTok成人版 PornHub 草榴社区 乱伦社区 少女初夜 萝莉岛

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

随机展示

相关说明

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！
>