2022 Python for Machine Learning & Data Science Masterclass

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2022 Python for Machine Learning & Data Science Masterclass

磁力链接/BT种子简介

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收录时间：2024-01-12
最近下载：2024-12-01

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文件列表

  • 23 - Hierarchical Clustering/210 - Hierarchical Clustering Coding Part Two ScikitLearn.mp4 331.0 MB
  • 5 - Pandas/46 - Pandas Project Exercise Solutions.mp4 300.7 MB
  • 13 - Logistic Regression/137 - Logistic Regression Project Exercise Solutions.mp4 276.4 MB
  • 8 - Data Analysis and Visualization Capstone Project Exercise/75 - Capstone Project Solutions Part Three.mp4 229.4 MB
  • 17 - Random Forests/168 - Coding Classification with Random Forest Classifier Part Two.mp4 216.2 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/217 - DBSCAN Outlier Project Exercise Solutions.mp4 206.8 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/224 - PCA Project Exercise Solution.mp4 202.1 MB
  • 11 - Feature Engineering and Data Preparation/111 - Dealing with Missing Data Part Two Filling or Dropping data based on Rows.mp4 195.0 MB
  • 16 - Tree Based Methods Decision Tree Learning/161 - Coding Decision Trees Part Two Creating the Model.mp4 191.2 MB
  • 8 - Data Analysis and Visualization Capstone Project Exercise/73 - Capstone Project Solutions Part One.mp4 187.8 MB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/183 - Solution Walkthrough Supervised Learning Project Tree Models.mp4 183.2 MB
  • 7 - Seaborn Data Visualizations/59 - Scatterplots with Seaborn.mp4 182.8 MB
  • 7 - Seaborn Data Visualizations/71 - Seaborn Plot Exercises Solutions.mp4 180.3 MB
  • 14 - KNN K Nearest Neighbors/143 - KNN Classification Project Exercise Solutions.mp4 178.6 MB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/181 - Solution Walkthrough Supervised Learning Project Data and EDA.mp4 177.2 MB
  • 26 - Model Deployment/227 - Model Persistence.mp4 175.7 MB
  • 5 - Pandas/41 - Pandas Input and Output HTML Tables.mp4 175.6 MB
  • 13 - Logistic Regression/135 - MultiClass Classification with Logistic Regression Part Two Model.mp4 175.4 MB
  • 11 - Feature Engineering and Data Preparation/112 - Dealing with Missing Data Part 3 Fixing data based on Columns.mp4 163.1 MB
  • 8 - Data Analysis and Visualization Capstone Project Exercise/74 - Capstone Project Solutions Part Two.mp4 161.9 MB
  • 14 - KNN K Nearest Neighbors/141 - KNN Coding with Python Part Two Choosing K.mp4 161.6 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/193 - Text Classification Project Exercise Solutions.mp4 160.8 MB
  • 16 - Tree Based Methods Decision Tree Learning/160 - Coding Decision Trees Part One The Data.mp4 160.6 MB
  • 5 - Pandas/43 - Pandas Input and Output SQL Databases.mp4 159.0 MB
  • 4 - NumPy/14 - NumPy Arrays.mp4 156.9 MB
  • 10 - Linear Regression/104 - L1 Regularization Lasso Regression Background and Implementation.mp4 155.9 MB
  • 11 - Feature Engineering and Data Preparation/109 - Dealing with Outliers.mp4 155.4 MB
  • 5 - Pandas/33 - GroupBy Operations Part Two MultiIndex.mp4 155.4 MB
  • 5 - Pandas/22 - DataFrames Part One Creating a DataFrame.mp4 154.3 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/221 - PCA Manual Implementation in Python.mp4 153.6 MB
  • 22 - KMeans Clustering/205 - KMeans Clustering Exercise Solution Part Two.mp4 153.1 MB
  • 5 - Pandas/32 - GroupBy Operations Part One.mp4 147.1 MB
  • 15 - Support Vector Machines/153 - Support Vector Machine Project Solutions.mp4 146.7 MB
  • 7 - Seaborn Data Visualizations/68 - Seaborn Grid Plots.mp4 144.0 MB
  • 17 - Random Forests/170 - Coding Regression with Random Forest Regressor Part Two Basic Models.mp4 142.5 MB
  • 15 - Support Vector Machines/150 - SVM with ScikitLearn and Python Classification Part Two.mp4 140.9 MB
  • 10 - Linear Regression/103 - L2 Regularization Ridge Regression Python Implementation.mp4 140.5 MB
  • 5 - Pandas/28 - Pandas Useful Methods Apply on Multiple Columns.mp4 139.9 MB
  • 1 - Introduction to Course/3 - Anaconda Python and Jupyter Install and Setup.mp4 139.2 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/121 - Linear Regression Project Solutions.mp4 136.9 MB
  • 5 - Pandas/44 - Pandas Pivot Tables.mp4 135.4 MB
  • 7 - Seaborn Data Visualizations/65 - Categorical Plots Distributions within Categories Coding with Seaborn.mp4 132.0 MB
  • 23 - Hierarchical Clustering/209 - Hierarchical Clustering Coding Part One Data and Visualization.mp4 131.3 MB
  • 10 - Linear Regression/86 - Python coding Simple Linear Regression.mp4 131.0 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/222 - PCA SciKitLearn.mp4 123.3 MB
  • 5 - Pandas/39 - Pandas Time Methods for Date and Time Data.mp4 121.0 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/119 - Grid Search.mp4 119.9 MB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/180 - Introduction to Supervised Learning Capstone Project.mp4 119.4 MB
  • 5 - Pandas/29 - Pandas Useful Methods Statistical Information and Sorting.mp4 118.9 MB
  • 5 - Pandas/31 - Missing Data Pandas Operations.mp4 115.8 MB
  • 5 - Pandas/25 - DataFrames Part Four Working with Rows.mp4 115.6 MB
  • 15 - Support Vector Machines/151 - SVM with ScikitLearn and Python Regression Tasks.mp4 114.0 MB
  • 22 - KMeans Clustering/204 - KMeans Clustering Exercise Solution Part One.mp4 111.8 MB
  • 22 - KMeans Clustering/198 - KMeans Clustering Coding Part One.mp4 109.9 MB
  • 10 - Linear Regression/83 - Linear Regression Understanding Ordinary Least Squares.mp4 106.8 MB
  • 26 - Model Deployment/230 - Model API Creating the Script.mp4 105.7 MB
  • 5 - Pandas/26 - Pandas Conditional Filtering.mp4 103.1 MB
  • 6 - Matplotlib/52 - Matplotlib Subplots Functionality.mp4 101.3 MB
  • 7 - Seaborn Data Visualizations/69 - Seaborn Matrix Plots.mp4 100.8 MB
  • 13 - Logistic Regression/133 - Logistic Regression with ScikitLearn Part Three Performance Evaluation.mp4 100.2 MB
  • 10 - Linear Regression/105 - L1 and L2 Regularization Elastic Net.mp4 99.4 MB
  • 13 - Logistic Regression/128 - Logistic Regression with ScikitLearn Part One EDA.mp4 99.0 MB
  • 8 - Data Analysis and Visualization Capstone Project Exercise/72 - Capstone Project Overview.mp4 97.9 MB
  • 6 - Matplotlib/57 - Matplotlib Exercise Questions Solutions.mp4 97.3 MB
  • 14 - KNN K Nearest Neighbors/140 - KNN Coding with Python Part One.mp4 96.1 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/188 - Feature Extraction from Text Coding Count Vectorization Manually.mp4 94.8 MB
  • 18 - Boosting Methods/177 - AdaBoost Coding Part Two The Model.mp4 93.9 MB
  • 11 - Feature Engineering and Data Preparation/113 - Dealing with Categorical Data Encoding Options.mp4 93.6 MB
  • 10 - Linear Regression/88 - Linear Regression ScikitLearn Train Test Split.mp4 92.6 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/216 - DBSCAN Outlier Project Exercise Overview.mp4 91.3 MB
  • 7 - Seaborn Data Visualizations/61 - Distribution Plots Part Two Coding with Seaborn.mp4 90.7 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/223 - PCA Project Exercise Overview.mp4 90.7 MB
  • 22 - KMeans Clustering/199 - KMeans Clustering Coding Part Two.mp4 90.3 MB
  • 22 - KMeans Clustering/206 - KMeans Clustering Exercise Solution Part Three.mp4 90.1 MB
  • 18 - Boosting Methods/179 - Gradient Boosting Coding Walkthrough.mp4 90.0 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/116 - Cross Validation Test Validation Train Split.mp4 88.2 MB
  • 2 - OPTIONAL Python Crash Course/11 - Python Crash Course Exercise Solutions.mp4 87.9 MB
  • 6 - Matplotlib/56 - Matplotlib Exercise Questions Overview.mp4 87.9 MB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/182 - Solution Walkthrough Supervised Learning Project Cohort Analysis.mp4 87.3 MB
  • 10 - Linear Regression/96 - Polynomial Regression Choosing Degree of Polynomial.mp4 85.5 MB
  • 7 - Seaborn Data Visualizations/63 - Categorical Plots Statistics within Categories Coding with Seaborn.mp4 84.9 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/215 - DBSCAN Hyperparameter Tuning Methods.mp4 84.9 MB
  • 22 - KMeans Clustering/202 - KMeans Color Quantization Part Two.mp4 84.1 MB
  • 5 - Pandas/27 - Pandas Useful Methods Apply on Single Column.mp4 83.0 MB
  • 17 - Random Forests/172 - Coding Regression with Random Forest Regressor Part Four Advanced Models.mp4 82.1 MB
  • 22 - KMeans Clustering/203 - KMeans Clustering Exercise Overview.mp4 81.9 MB
  • 7 - Seaborn Data Visualizations/70 - Seaborn Plot Exercises Overview.mp4 81.8 MB
  • 7 - Seaborn Data Visualizations/67 - Seaborn Comparison Plots Coding with Seaborn.mp4 81.8 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/212 - DBSCAN Theory and Intuition.mp4 81.1 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/213 - DBSCAN versus KMeans Clustering.mp4 80.7 MB
  • 17 - Random Forests/167 - Coding Classification with Random Forest Classifier Part One.mp4 80.2 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/189 - Feature Extraction from Text Coding with ScikitLearn.mp4 79.2 MB
  • 10 - Linear Regression/89 - Linear Regression ScikitLearn Performance Evaluation Regression.mp4 77.1 MB
  • 13 - Logistic Regression/127 - Logistic Regression Theory and Intuition Best fit with Maximum Likelihood.mp4 76.4 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/118 - Cross Validation crossvalidate.mp4 75.7 MB
  • 22 - KMeans Clustering/200 - KMeans Clustering Coding Part Three.mp4 73.5 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/115 - Cross Validation Test Train Split.mp4 72.5 MB
  • 15 - Support Vector Machines/149 - SVM with ScikitLearn and Python Classification Part One.mp4 71.9 MB
  • 15 - Support Vector Machines/148 - SVM Theory and Intuition Kernel Trick and Mathematics.mp4 71.6 MB
  • 17 - Random Forests/171 - Coding Regression with Random Forest Regressor Part Three Polynomials.mp4 70.7 MB
  • 1 - Introduction to Course/2 - UNZIP-FOR-NOTEBOOKS-FINAL.zip 70.4 MB
  • 1 - Introduction to Course/3 - UNZIP-FOR-NOTEBOOKS-FINAL.zip 70.4 MB
  • 10 - Linear Regression/91 - Linear Regression Model Deployment and Coefficient Interpretation.mp4 70.4 MB
  • 5 - Pandas/45 - Pandas Project Exercise Overview.mp4 70.2 MB
  • 6 - Matplotlib/54 - Matplotlib Styling Colors and Styles.mp4 67.8 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/117 - Cross Validation crossvalscore.mp4 66.3 MB
  • 5 - Pandas/38 - Pandas Text Methods for String Data.mp4 66.1 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/186 - Naive Bayes Algorithm Part Two Model Algorithm.mp4 65.7 MB
  • 10 - Linear Regression/102 - L2 Regularization Ridge Regression Theory.mp4 64.3 MB
  • 18 - Boosting Methods/176 - AdaBoost Coding Part One The Data.mp4 64.0 MB
  • 10 - Linear Regression/90 - Linear Regression Residual Plots.mp4 63.2 MB
  • 7 - Seaborn Data Visualizations/64 - Categorical Plots Distributions within Categories Understanding Plot Types.mp4 61.9 MB
  • 17 - Random Forests/166 - Random Forests Bootstrapping and OutofBag Error.mp4 61.9 MB
  • 4 - NumPy/15 - NumPy Indexing and Selection.mp4 61.0 MB
  • 10 - Linear Regression/93 - Polynomial Regression Creating Polynomial Features.mp4 59.9 MB
  • 4 - NumPy/16 - NumPy Operations.mp4 57.8 MB
  • 4 - NumPy/18 - Numpy Exercises Solutions.mp4 57.6 MB
  • 10 - Linear Regression/82 - Linear Regression Algorithm History.mp4 57.5 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/190 - Natural Language Processing Classification of Text Part One.mp4 57.3 MB
  • 18 - Boosting Methods/175 - AdaBoost Theory and Intuition.mp4 57.1 MB
  • 5 - Pandas/35 - Combining DataFrames Inner Merge.mp4 56.4 MB
  • 5 - Pandas/40 - Pandas Input and Output CSV Files.mp4 56.3 MB
  • 5 - Pandas/34 - Combining DataFrames Concatenation.mp4 56.1 MB
  • 1 - Introduction to Course/5 - Environment Setup.mp4 56.0 MB
  • 11 - Feature Engineering and Data Preparation/108 - Introduction to Feature Engineering and Data Preparation.mp4 55.8 MB
  • 10 - Linear Regression/94 - Polynomial Regression Training and Evaluation.mp4 55.5 MB
  • 22 - KMeans Clustering/201 - KMeans Color Quantization Part One.mp4 53.9 MB
  • 16 - Tree Based Methods Decision Tree Learning/155 - Decision Tree History.mp4 53.0 MB
  • 2 - OPTIONAL Python Crash Course/9 - Python Crash Course Part Three.mp4 52.6 MB
  • 5 - Pandas/24 - DataFrames Part Three Working with Columns.mp4 52.5 MB
  • 5 - Pandas/21 - Series Part Two.mp4 51.5 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/191 - Natural Language Processing Classification of Text Part Two.mp4 51.2 MB
  • 6 - Matplotlib/50 - Matplotlib Implementing Figures and Axes.mp4 50.7 MB
  • 13 - Logistic Regression/126 - Logistic Regression Theory and Intuition Linear to Logistic Math.mp4 49.9 MB
  • 10 - Linear Regression/95 - Bias Variance TradeOff.mp4 49.9 MB
  • 15 - Support Vector Machines/146 - SVM Theory and Intuition Hyperplanes and Margins.mp4 48.1 MB
  • 11 - Feature Engineering and Data Preparation/110 - Dealing with Missing Data Part One Evaluation of Missing Data.mp4 47.3 MB
  • 21 - Unsupervised Learning/194 - Unsupervised Learning Overview.mp4 46.8 MB
  • 26 - Model Deployment/231 - Testing the API.mp4 45.8 MB
  • 2 - OPTIONAL Python Crash Course/7 - Python Crash Course Part One.mp4 45.7 MB
  • 10 - Linear Regression/87 - Overview of ScikitLearn and Python.mp4 45.6 MB
  • 6 - Matplotlib/55 - Advanced Matplotlib Commands Optional.mp4 45.0 MB
  • 5 - Pandas/23 - DataFrames Part Two Basic Properties.mp4 44.9 MB
  • 6 - Matplotlib/48 - Matplotlib Basics.mp4 44.4 MB
  • 13 - Logistic Regression/136 - Logistic Regression Exercise Project Overview.mp4 43.6 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/219 - PCA Theory and Intuition Part One.mp4 42.9 MB
  • 3 - Machine Learning Pathway Overview/12 - Machine Learning Pathway.mp4 42.5 MB
  • 10 - Linear Regression/85 - Linear Regression Gradient Descent.mp4 42.2 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/120 - Linear Regression Project Overview.mp4 42.0 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/192 - Text Classification Project Exercise Overview.mp4 41.8 MB
  • 5 - Pandas/20 - Series Part One.mp4 41.7 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/187 - Feature Extraction from Text Part One Theory and Intuition.mp4 41.3 MB
  • 2 - OPTIONAL Python Crash Course/8 - Python Crash Course Part Two.mp4 41.1 MB
  • 10 - Linear Regression/100 - Introduction to Cross Validation.mp4 40.4 MB
  • 9 - Machine Learning Concepts Overview/79 - Supervised Machine Learning Process.mp4 39.7 MB
  • 17 - Random Forests/165 - Random Forests Number of Estimators and Features in Subsets.mp4 39.7 MB
  • 5 - Pandas/42 - Pandas Input and Output Excel Files.mp4 39.0 MB
  • 16 - Tree Based Methods Decision Tree Learning/159 - Constructing Decision Trees with Gini Impurity Part Two.mp4 39.0 MB
  • 10 - Linear Regression/99 - Feature Scaling.mp4 38.8 MB
  • 13 - Logistic Regression/124 - Logistic Regression Theory and Intuition Part One The Logistic Function.mp4 37.4 MB
  • 14 - KNN K Nearest Neighbors/142 - KNN Classification Project Exercise Overview.mp4 37.3 MB
  • 5 - Pandas/30 - Missing Data Overview.mp4 36.0 MB
  • 22 - KMeans Clustering/196 - Clustering General Overview.mp4 35.6 MB
  • 22 - KMeans Clustering/197 - KMeans Clustering Theory.mp4 34.3 MB
  • 23 - Hierarchical Clustering/208 - Hierarchical Clustering Theory and Intuition.mp4 33.8 MB
  • 13 - Logistic Regression/131 - Classification Metrics Precison Recall F1Score.mp4 33.5 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/185 - Naive Bayes Algorithm Part One Bayes Theorem.mp4 33.1 MB
  • 14 - KNN K Nearest Neighbors/139 - KNN Classification Theory and Intuition.mp4 32.8 MB
  • 17 - Random Forests/163 - Random Forests History and Motivation.mp4 32.6 MB
  • 18 - Boosting Methods/178 - Gradient Boosting Theory.mp4 32.2 MB
  • 10 - Linear Regression/92 - Polynomial Regression Theory and Motivation.mp4 31.9 MB
  • 5 - Pandas/37 - Combining DataFrames Outer Merge.mp4 31.5 MB
  • 15 - Support Vector Machines/152 - Support Vector Machine Project Overview.mp4 31.2 MB
  • 18 - Boosting Methods/174 - Boosting Methods Motivation and History.mp4 31.1 MB
  • 13 - Logistic Regression/130 - Classification Metrics Confusion Matrix and Accuracy.mp4 30.7 MB
  • 13 - Logistic Regression/134 - MultiClass Classification with Logistic Regression Part One Data and EDA.mp4 30.4 MB
  • 5 - Pandas/36 - Combining DataFrames Left and Right Merge.mp4 30.4 MB
  • 13 - Logistic Regression/129 - Logistic Regression with ScikitLearn Part Two Model Training.mp4 28.8 MB
  • 9 - Machine Learning Concepts Overview/77 - Why Machine Learning.mp4 28.7 MB
  • 10 - Linear Regression/101 - Regularization Data Setup.mp4 28.6 MB
  • 10 - Linear Regression/106 - Linear Regression Project Data Overview.mp4 28.1 MB
  • 26 - Model Deployment/226 - Model Deployment Considerations.mp4 26.9 MB
  • 16 - Tree Based Methods Decision Tree Learning/157 - Decision Tree Understanding Gini Impurity.mp4 26.2 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/220 - PCA Theory and Intuition Part Two.mp4 26.1 MB
  • 1 - Introduction to Course/2 - COURSE OVERVIEW LECTURE PLEASE DO NOT SKIP.mp4 25.7 MB
  • 10 - Linear Regression/97 - Polynomial Regression Model Deployment.mp4 25.4 MB
  • 16 - Tree Based Methods Decision Tree Learning/158 - Constructing Decision Trees with Gini Impurity Part One.mp4 24.9 MB
  • 26 - Model Deployment/228 - Model Deployment as an API General Overview.mp4 24.5 MB
  • 6 - Matplotlib/53 - Matplotlib Styling Legends.mp4 24.0 MB
  • 15 - Support Vector Machines/145 - History of Support Vector Machines.mp4 23.8 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/214 - DBSCAN Hyperparameter Theory.mp4 23.6 MB
  • 10 - Linear Regression/84 - Linear Regression Cost Functions.mp4 23.1 MB
  • 13 - Logistic Regression/132 - Classification Metrics ROC Curves.mp4 22.6 MB
  • 9 - Machine Learning Concepts Overview/78 - Types of Machine Learning Algorithms.mp4 21.7 MB
  • 7 - Seaborn Data Visualizations/62 - Categorical Plots Statistics within Categories Understanding Plot Types.mp4 21.5 MB
  • 10 - Linear Regression/98 - Regularization Overview.mp4 21.5 MB
  • 17 - Random Forests/169 - Coding Regression with Random Forest Regressor Part One Data.mp4 20.5 MB
  • 13 - Logistic Regression/123 - Introduction to Logistic Regression Section.mp4 20.1 MB
  • 7 - Seaborn Data Visualizations/60 - Distribution Plots Part One Understanding Plot Types.mp4 20.0 MB
  • 9 - Machine Learning Concepts Overview/76 - Introduction to Machine Learning Overview Section.mp4 19.1 MB
  • 4 - NumPy/17 - NumPy Exercises.mp4 17.1 MB
  • 6 - Matplotlib/51 - Matplotlib Figure Parameters.mp4 17.1 MB
  • 15 - Support Vector Machines/147 - SVM Theory and Intuition Kernel Intuition.mp4 16.9 MB
  • 6 - Matplotlib/49 - Matplotlib Understanding the Figure Object.mp4 15.9 MB
  • 16 - Tree Based Methods Decision Tree Learning/156 - Decision Tree Terminology.mp4 15.8 MB
  • 6 - Matplotlib/47 - Introduction to Matplotlib.mp4 15.8 MB
  • 13 - Logistic Regression/125 - Logistic Regression Theory and Intuition Part Two Linear to Logistic.mp4 15.5 MB
  • 9 - Machine Learning Concepts Overview/80 - Companion Book Introduction to Statistical Learning.mp4 15.5 MB
  • 2 - OPTIONAL Python Crash Course/10 - Python Crash Course Exercise Questions.mp4 15.2 MB
  • 7 - Seaborn Data Visualizations/66 - Seaborn Comparison Plots Understanding the Plot Types.mp4 15.0 MB
  • 17 - Random Forests/164 - Random Forests Key Hyperparameters.mp4 14.7 MB
  • 12 - Cross Validation Grid Search and the Linear Regression Project/114 - Section Overview and Introduction.mp4 14.6 MB
  • 7 - Seaborn Data Visualizations/58 - Introduction to Seaborn.mp4 14.6 MB
  • 5 - Pandas/19 - Introduction to Pandas.mp4 12.4 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/218 - Introduction to Principal Component Analysis.mp4 11.1 MB
  • 4 - NumPy/13 - Introduction to NumPy.mp4 10.9 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/184 - Introduction to NLP and Naive Bayes Section.mp4 10.1 MB
  • 26 - Model Deployment/225 - Model Deployment Section Overview.mp4 9.0 MB
  • 22 - KMeans Clustering/195 - Introduction to KMeans Clustering Section.mp4 7.9 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/184 - moviereviews.csv 7.6 MB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/180 - 17-Supervised-Learning-Capstone-Project.zip 7.4 MB
  • 14 - KNN K Nearest Neighbors/138 - Introduction to KNN Section.mp4 7.2 MB
  • 15 - Support Vector Machines/144 - Introduction to Support Vector Machines.mp4 6.5 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/211 - Introduction to DBSCAN Section.mp4 6.2 MB
  • 22 - KMeans Clustering/195 - 20-Kmeans-Clustering.zip 6.1 MB
  • 17 - Random Forests/162 - Introduction to Random Forests Section.mp4 6.0 MB
  • 18 - Boosting Methods/173 - Introduction to Boosting Section.mp4 5.9 MB
  • 22 - KMeans Clustering/195 - bank-full.csv 5.2 MB
  • 10 - Linear Regression/81 - Introduction to Linear Regression Section.mp4 5.0 MB
  • 25 - PCA Principal Component Analysis and Manifold Learning/218 - 23-PCA-Principal-Component-Analysis.zip 4.1 MB
  • 17 - Random Forests/162 - 15-Random-Forests.zip 4.1 MB
  • 16 - Tree Based Methods Decision Tree Learning/154 - Introduction to Tree Based Methods.mp4 3.9 MB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/211 - 22-DBSCAN.zip 3.7 MB
  • 23 - Hierarchical Clustering/207 - Introduction to Hierarchical Clustering.mp4 3.5 MB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/184 - airline-tweets.csv 3.4 MB
  • 13 - Logistic Regression/122 - 11-Logistic-Regression-Models.zip 2.1 MB
  • 16 - Tree Based Methods Decision Tree Learning/154 - 14-Decision-Trees.zip 1.9 MB
  • 15 - Support Vector Machines/144 - 13-Support-Vector-Machines.zip 1.6 MB
  • 14 - KNN K Nearest Neighbors/138 - 12-K-Nearest-Neighbors.zip 1.4 MB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/180 - Telco-Customer-Churn.csv 976.5 kB
  • 18 - Boosting Methods/173 - 16-Boosted-Trees.zip 940.0 kB
  • 23 - Hierarchical Clustering/207 - 21-Hierarchical-Clustering.zip 636.5 kB
  • 25 - PCA Principal Component Analysis and Manifold Learning/218 - digits.csv 497.2 kB
  • 18 - Boosting Methods/173 - mushrooms.csv 374.0 kB
  • 20 - Naive Bayes Classification and Natural Language Processing Supervised Learning/184 - 18-Naive-Bayes-and-NLP.zip 197.1 kB
  • 22 - KMeans Clustering/195 - palm-trees.jpg 176.9 kB
  • 25 - PCA Principal Component Analysis and Manifold Learning/218 - cancer-tumor-data-features.csv 120.8 kB
  • 23 - Hierarchical Clustering/210 - Hierarchical Clustering Coding Part Two ScikitLearn Thai.srt 80.2 kB
  • 11 - Feature Engineering and Data Preparation/109 - Dealing with Outliers Thai.srt 75.7 kB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/182 - Solution Walkthrough Supervised Learning Project Cohort Analysis Thai.srt 73.0 kB
  • 24 - DBSCAN Densitybased spatial clustering of applications with noise/217 - DBSCAN Outlier Project Exercise Solutions Thai.srt 72.9 kB
  • 5 - Pandas/46 - Pandas Project Exercise Solutions Thai.srt 70.3 kB
  • 14 - KNN K Nearest Neighbors/141 - KNN Coding with Python Part Two Choosing K Thai.srt 69.1 kB
  • 13 - Logistic Regression/137 - Logistic Regression Project Exercise Solutions Thai.srt 69.0 kB
  • 11 - Feature Engineering and Data Preparation/112 - Dealing with Missing Data Part 3 Fixing data based on Columns Thai.srt 66.8 kB
  • 19 - Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods/183 - Solution Walkthrough Supervised Learning Project Tree Models Thai.srt 66.4 kB
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  • 18 - Boosting Methods/176 - AdaBoost Coding Part One The Data German.srt 19.2 kB
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  • 15 - Support Vector Machines/149 - SVM with ScikitLearn and Python Classification Part One French.srt 18.9 kB
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  • 15 - Support Vector Machines/149 - SVM with ScikitLearn and Python Classification Part One German.srt 18.8 kB
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  • 11 - Feature Engineering and Data Preparation/110 - Dealing with Missing Data Part One Evaluation of Missing Data Italian.srt 18.7 kB
  • 8 - Data Analysis and Visualization Capstone Project Exercise/72 - Capstone Project Overview Simplified Chinese.srt 18.7 kB
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