GetFreeCourses.Co-Udemy-2022 Python for Machine Learning & Data Science Masterclass

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23 - Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4 209.2 MB
05 - Pandas/028 Pandas Project Exercise Solutions.mp4 172.6 MB
13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions.mp4 161.3 MB
08 - Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three.mp4 137.4 MB
17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two.mp4 130.4 MB
19 - Supervised Learning Capstone Project/003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis.mp4 130.1 MB
05 - Pandas/026 Pandas Pivot Tables.mp4 129.1 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4 127.9 MB
25 - PCA - Principal Component Analysis and Manifold Learning/007 PCA - Project Exercise Solution.mp4 119.5 MB
11 - Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4 117.6 MB
16 - Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4 115.8 MB
23 - Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4 115.0 MB
19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models.mp4 114.2 MB
07 - Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4 111.3 MB
08 - Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4 110.6 MB
26 - Model Deployment/003 Model Persistence.mp4 109.8 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4 109.1 MB
22 - K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4 108.2 MB
08 - Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4 106.2 MB
19 - Supervised Learning Capstone Project/002 Solution Walkthrough - Supervised Learning Project - Data and EDA.mp4 106.1 MB
06 - Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4 105.9 MB
07 - Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4 105.7 MB
11 - Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4 105.2 MB
13 - Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4 105.1 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4 105.1 MB
14 - KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4 105.0 MB
11 - Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4 103.3 MB
14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4 102.9 MB
05 - Pandas/023 Pandas Input and Output - HTML Tables.mp4 102.3 MB
20 - Naive Bayes Classification and Natural Language Processing/010 Text Classification Project Exercise Solutions.mp4 100.6 MB
04 - NumPy/002 NumPy Arrays.mp4 99.5 MB
16 - Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp4 98.7 MB
22 - K-Means Clustering/004 K-Means Clustering - Coding Part One.mp4 97.9 MB
05 - Pandas/004 DataFrames - Part One - Creating a DataFrame.mp4 97.5 MB
06 - Matplotlib/006 Matplotlib - Subplots Functionality.mp4 96.6 MB
05 - Pandas/025 Pandas Input and Output - SQL Databases.mp4 96.0 MB
25 - PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp4 95.0 MB
10 - Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp4 94.7 MB
15 - Support Vector Machines/010 Support Vector Machine Project Solutions.mp4 93.4 MB
05 - Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp4 92.9 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp4 91.2 MB
15 - Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp4 90.6 MB
10 - Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp4 89.4 MB
07 - Seaborn Data Visualizations/011 Seaborn Grid Plots.mp4 87.0 MB
05 - Pandas/014 GroupBy Operations - Part One.mp4 87.0 MB
10 - Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp4 86.4 MB
05 - Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp4 85.3 MB
17 - Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp4 85.0 MB
07 - Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp4 84.6 MB
01 - Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp4 84.5 MB
05 - Pandas/006 DataFrames - Part Three - Working with Columns.mp4 84.1 MB
10 - Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp4 81.1 MB
22 - K-Means Clustering/005 K-Means Clustering Coding Part Two.mp4 80.9 MB
22 - K-Means Clustering/007 K-Means Color Quantization - Part One.mp4 80.6 MB
05 - Pandas/021 Pandas - Time Methods for Date and Time Data.mp4 80.2 MB
22 - K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp4 79.9 MB
15 - Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp4 76.3 MB
05 - Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp4 74.4 MB
25 - PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp4 74.1 MB
05 - Pandas/013 Missing Data - Pandas Operations.mp4 73.6 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp4 73.2 MB
05 - Pandas/007 DataFrames - Part Four - Working with Rows.mp4 72.6 MB
10 - Linear Regression/006 Python coding Simple Linear Regression.mp4 70.1 MB
05 - Pandas/008 Pandas - Conditional Filtering.mp4 69.2 MB
26 - Model Deployment/006 Model API - Creating the Script.mp4 67.3 MB
01 - Introduction to Course/33985574-UNZIP-FOR-NOTEBOOKS-FINAL.zip 67.1 MB
01 - Introduction to Course/33985614-UNZIP-FOR-NOTEBOOKS-FINAL.zip 67.1 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp4 66.6 MB
10 - Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp4 66.4 MB
22 - K-Means Clustering/008 K-Means Color Quantization - Part Two.mp4 65.0 MB
18 - Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp4 63.1 MB
22 - K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp4 62.5 MB
13 - Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp4 62.4 MB
14 - KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp4 61.5 MB
07 - Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp4 61.5 MB
10 - Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp4 61.4 MB
10 - Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp4 61.3 MB
22 - K-Means Clustering/006 K-Means Clustering Coding Part Three.mp4 59.8 MB
22 - K-Means Clustering/009 K-Means Clustering Exercise Overview.mp4 59.5 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp4 59.4 MB
07 - Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp4 59.2 MB
11 - Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp4 58.9 MB
18 - Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp4 57.9 MB
02 - OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp4 57.6 MB
13 - Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp4 57.0 MB
10 - Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp4 55.7 MB
13 - Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp4 54.9 MB
10 - Linear Regression/002 Linear Regression - Algorithm History.mp4 54.8 MB
05 - Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp4 53.7 MB
10 - Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp4 53.4 MB
25 - PCA - Principal Component Analysis and Manifold Learning/006 PCA - Project Exercise Overview.mp4 52.8 MB
15 - Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp4 52.6 MB
22 - K-Means Clustering/003 K-Means Clustering Theory.mp4 52.5 MB
16 - Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp4 52.3 MB
17 - Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp4 52.1 MB
23 - Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp4 52.1 MB
07 - Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp4 51.7 MB
07 - Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp4 51.2 MB
17 - Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp4 50.7 MB
20 - Naive Bayes Classification and Natural Language Processing/006 Feature Extraction from Text - Coding with Scikit-Learn.mp4 50.4 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp4 50.3 MB
06 - Matplotlib/010 Matplotlib Exercise Questions Overview.mp4 49.0 MB
02 - OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp4 48.7 MB
20 - Naive Bayes Classification and Natural Language Processing/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp4 48.6 MB
07 - Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp4 47.9 MB
15 - Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp4 47.7 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp4 46.9 MB
15 - Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp4 46.3 MB
17 - Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp4 45.5 MB
05 - Pandas/020 Pandas - Text Methods for String Data.mp4 45.1 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp4 45.0 MB
07 - Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp4 45.0 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp4 44.5 MB
06 - Matplotlib/008 Matplotlib Styling - Colors and Styles.mp4 44.3 MB
10 - Linear Regression/010 Linear Regression - Residual Plots.mp4 44.0 MB
18 - Boosting Methods/004 AdaBoost Coding Part One - The Data.mp4 42.3 MB
18 - Boosting Methods/003 AdaBoost Theory and Intuition.mp4 41.5 MB
05 - Pandas/005 DataFrames - Part Two - Basic Properties.mp4 40.3 MB
05 - Pandas/017 Combining DataFrames - Inner Merge.mp4 40.3 MB
10 - Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp4 40.1 MB
04 - NumPy/003 NumPy Indexing and Selection.mp4 39.6 MB
05 - Pandas/027 Pandas Project Exercise Overview.mp4 39.4 MB
13 - Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp4 37.4 MB
05 - Pandas/022 Pandas Input and Output - CSV Files.mp4 37.1 MB
05 - Pandas/016 Combining DataFrames - Concatenation.mp4 36.8 MB
10 - Linear Regression/014 Polynomial Regression - Training and Evaluation.mp4 36.3 MB
10 - Linear Regression/015 Bias Variance Trade-Off.mp4 36.2 MB
11 - Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp4 36.1 MB
04 - NumPy/004 NumPy Operations.mp4 36.1 MB
13 - Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp4 36.0 MB
01 - Introduction to Course/005 Environment Setup.mp4 35.7 MB
16 - Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp4 35.6 MB
04 - NumPy/006 Numpy Exercises - Solutions.mp4 34.9 MB
06 - Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp4 34.9 MB
15 - Support Vector Machines/009 Support Vector Machine Project Overview.mp4 34.8 MB
20 - Naive Bayes Classification and Natural Language Processing/008 Natural Language Processing - Classification of Text - Part Two.mp4 34.8 MB
09 - Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp4 33.5 MB
26 - Model Deployment/007 Testing the API.mp4 33.2 MB
13 - Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp4 33.1 MB
10 - Linear Regression/020 Introduction to Cross Validation.mp4 33.0 MB
17 - Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp4 32.7 MB
13 - Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp4 32.6 MB
02 - OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp4 32.0 MB
10 - Linear Regression/007 Overview of Scikit-Learn and Python.mp4 31.4 MB
08 - Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp4 31.1 MB
06 - Matplotlib/002 Matplotlib Basics.mp4 31.1 MB
20 - Naive Bayes Classification and Natural Language Processing/009 Text Classification Project Exercise Overview.mp4 30.5 MB
19 - Supervised Learning Capstone Project/001 Introduction to Supervised Learning Capstone Project.mp4 29.8 MB
02 - OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp4 29.7 MB
25 - PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp4 29.7 MB
20 - Naive Bayes Classification and Natural Language Processing/004 Feature Extraction from Text - Part One - Theory and Intuition.mp4 29.4 MB
10 - Linear Regression/005 Linear Regression - Gradient Descent.mp4 29.2 MB
05 - Pandas/002 Series - Part One.mp4 28.6 MB
20 - Naive Bayes Classification and Natural Language Processing/007 Natural Language Processing - Classification of Text - Part One.mp4 28.3 MB
17 - Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp4 27.3 MB
05 - Pandas/012 Missing Data - Overview.mp4 27.2 MB
05 - Pandas/003 Series - Part Two.mp4 26.1 MB
05 - Pandas/024 Pandas Input and Output - Excel Files.mp4 25.9 MB
06 - Matplotlib/009 Advanced Matplotlib Commands (Optional).mp4 25.2 MB
22 - K-Means Clustering/002 Clustering General Overview.mp4 24.9 MB
10 - Linear Regression/019 Feature Scaling.mp4 24.3 MB
13 - Logistic Regression/015 Logistic Regression Exercise Project Overview.mp4 24.3 MB
17 - Random Forests/002 Random Forests - History and Motivation.mp4 24.0 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp4 23.6 MB
14 - KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp4 23.6 MB
10 - Linear Regression/017 Polynomial Regression - Model Deployment.mp4 23.2 MB
18 - Boosting Methods/006 Gradient Boosting Theory.mp4 23.0 MB
10 - Linear Regression/012 Polynomial Regression - Theory and Motivation.mp4 22.2 MB
05 - Pandas/019 Combining DataFrames - Outer Merge.mp4 22.2 MB
20 - Naive Bayes Classification and Natural Language Processing/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp4 22.0 MB
18 - Boosting Methods/002 Boosting Methods - Motivation and History.mp4 22.0 MB
13 - Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp4 21.7 MB
14 - KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp4 21.1 MB
09 - Machine Learning Concepts Overview/002 Why Machine Learning_.mp4 21.0 MB
10 - Linear Regression/021 Regularization Data Setup.mp4 20.2 MB
16 - Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp4 19.4 MB
11 - Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp4 19.0 MB
25 - PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp4 19.0 MB
26 - Model Deployment/002 Model Deployment Considerations.mp4 18.3 MB
09 - Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp4 18.1 MB
16 - Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp4 17.7 MB
26 - Model Deployment/004 Model Deployment as an API - General Overview.mp4 17.5 MB
13 - Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp4 17.3 MB
10 - Linear Regression/026 Linear Regression Project - Data Overview.mp4 16.9 MB
10 - Linear Regression/004 Linear Regression - Cost Functions.mp4 16.6 MB
05 - Pandas/018 Combining DataFrames - Left and Right Merge.mp4 16.4 MB
06 - Matplotlib/007 Matplotlib Styling - Legends.mp4 16.2 MB
13 - Logistic Regression/011 Classification Metrics - ROC Curves.mp4 16.1 MB
07 - Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp4 16.0 MB
15 - Support Vector Machines/002 History of Support Vector Machines.mp4 15.5 MB
10 - Linear Regression/018 Regularization Overview.mp4 15.5 MB
07 - Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp4 15.0 MB
03 - Machine Learning Pathway Overview/001 Machine Learning Pathway.mp4 14.1 MB
13 - Logistic Regression/002 Introduction to Logistic Regression Section.mp4 13.9 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp4 13.9 MB
21 - Unsupervised Learning/001 Unsupervised Learning Overview.mp4 13.8 MB
17 - Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp4 13.7 MB
09 - Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp4 13.2 MB
06 - Matplotlib/005 Matplotlib - Figure Parameters.mp4 13.1 MB
06 - Matplotlib/003 Matplotlib - Understanding the Figure Object.mp4 11.7 MB
07 - Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp4 10.6 MB
15 - Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp4 9.8 MB
04 - NumPy/005 NumPy Exercises.mp4 9.6 MB
17 - Random Forests/003 Random Forests - Key Hyperparameters.mp4 8.3 MB
13 - Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp4 8.0 MB
16 - Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp4 7.3 MB
20 - Naive Bayes Classification and Natural Language Processing/31640132-moviereviews.csv 7.2 MB
01 - Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP_.mp4 7.2 MB
19 - Supervised Learning Capstone Project/31389398-17-Supervised-Learning-Capstone-Project.zip 7.0 MB
05 - Pandas/001 Introduction to Pandas.mp4 6.7 MB
06 - Matplotlib/001 Introduction to Matplotlib.mp4 6.6 MB
22 - K-Means Clustering/32407448-20-Kmeans-Clustering.zip 5.8 MB
07 - Seaborn Data Visualizations/001 Introduction to Seaborn.mp4 5.7 MB
12 - Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp4 5.6 MB
09 - Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp4 5.1 MB
25 - PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp4 5.1 MB
22 - K-Means Clustering/32407452-bank-full.csv 4.9 MB
20 - Naive Bayes Classification and Natural Language Processing/001 Introduction to NLP and Naive Bayes Section.mp4 4.2 MB
26 - Model Deployment/001 Model Deployment Section Overview.mp4 4.2 MB
25 - PCA - Principal Component Analysis and Manifold Learning/33912220-23-PCA-Principal-Component-Analysis.zip 3.9 MB
17 - Random Forests/30930956-15-Random-Forests.zip 3.9 MB
14 - KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp4 3.6 MB
22 - K-Means Clustering/001 Introduction to K-Means Clustering Section.mp4 3.6 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/33643014-22-DBSCAN.zip 3.5 MB
02 - OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp4 3.4 MB
04 - NumPy/001 Introduction to NumPy.mp4 3.4 MB
20 - Naive Bayes Classification and Natural Language Processing/31640102-airline-tweets.csv 3.3 MB
18 - Boosting Methods/001 Introduction to Boosting Section.mp4 3.0 MB
17 - Random Forests/001 Introduction to Random Forests Section.mp4 2.9 MB
15 - Support Vector Machines/001 Introduction to Support Vector Machines.mp4 2.8 MB
10 - Linear Regression/001 Introduction to Linear Regression Section.mp4 2.6 MB
16 - Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp4 2.3 MB
13 - Logistic Regression/29304858-11-Logistic-Regression-Models.zip 2.0 MB
24 - DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp4 1.8 MB
16 - Tree Based Methods_ Decision Tree Learning/30205020-14-Decision-Trees.zip 1.8 MB
23 - Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp4 1.7 MB
15 - Support Vector Machines/29902052-13-Support-Vector-Machines.zip 1.5 MB
14 - KNN - K Nearest Neighbors/29434428-12-K-Nearest-Neighbors.zip 1.4 MB
19 - Supervised Learning Capstone Project/31389400-Telco-Customer-Churn.csv 953.7 KB
18 - Boosting Methods/31286608-16-Boosted-Trees.zip 918.0 KB
23 - Hierarchical Clustering/33028500-21-Hierarchical-Clustering.zip 621.6 KB
25 - PCA - Principal Component Analysis and Manifold Learning/33912190-digits.csv 485.5 KB
18 - Boosting Methods/31286610-mushrooms.csv 365.2 KB
20 - Naive Bayes Classification and Natural Language Processing/31640094-18-Naive-Bayes-and-NLP.zip 192.5 KB
22 - K-Means Clustering/33555798-palm-trees.jpg 172.7 KB
25 - PCA - Principal Component Analysis and Manifold Learning/33912194-cancer-tumor-data-features.csv 118.0 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/33643060-cluster-circles.csv 59.9 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/33643082-cluster-moons.csv 58.7 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/33643080-cluster-blobs.csv 55.9 KB
17 - Random Forests/30930966-data-banknote-authentication.csv 45.4 KB
23 - Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn__en.srt 42.3 KB
11 - Feature Engineering and Data Preparation/003 Dealing with Outliers__en.srt 41.2 KB
05 - Pandas/028 Pandas Project Exercise Solutions__en.srt 38.8 KB
19 - Supervised Learning Capstone Project/003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis__en.srt 38.7 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/33643070-cluster-two-blobs-outliers.csv 38.3 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/33643072-cluster-two-blobs.csv 38.3 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions__en.srt 38.1 KB
11 - Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns__en.srt 36.8 KB
22 - K-Means Clustering/32407456-CIA-Country-Facts.csv 32.7 KB
16 - Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model__en.srt 32.7 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods__en.srt 32.7 KB
05 - Pandas/026 Pandas Pivot Tables__en.srt 32.2 KB
04 - NumPy/002 NumPy Arrays__en.srt 31.9 KB
05 - Pandas/021 Pandas - Time Methods for Date and Time Data__en.srt 31.7 KB
11 - Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows__en.srt 31.4 KB
13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions_en.vtt 30.9 KB
08 - Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three__en.srt 30.9 KB
14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K_en.vtt 30.7 KB
22 - K-Means Clustering/004 K-Means Clustering - Coding Part One__en.srt 30.4 KB
07 - Seaborn Data Visualizations/002 Scatterplots with Seaborn__en.srt 29.7 KB
19 - Supervised Learning Capstone Project/002 Solution Walkthrough - Supervised Learning Project - Data and EDA__en.srt 29.7 KB
05 - Pandas/025 Pandas Input and Output - SQL Databases__en.srt 29.4 KB
19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models_en.vtt 29.4 KB
15 - Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics__en.srt 29.3 KB
16 - Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data__en.srt 29.3 KB
05 - Pandas/004 DataFrames - Part One - Creating a DataFrame__en.srt 29.0 KB
18 - Boosting Methods/003 AdaBoost Theory and Intuition__en.srt 28.9 KB
06 - Matplotlib/006 Matplotlib - Subplots Functionality__en.srt 28.6 KB
07 - Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn__en.srt 28.3 KB
10 - Linear Regression/006 Python coding Simple Linear Regression__en.srt 28.1 KB
26 - Model Deployment/003 Model Persistence_en.vtt 28.1 KB
17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two_en.vtt 27.9 KB
05 - Pandas/013 Missing Data - Pandas Operations__en.srt 27.4 KB
05 - Pandas/008 Pandas - Conditional Filtering__en.srt 27.1 KB
08 - Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One__en.srt 26.8 KB
18 - Boosting Methods/005 AdaBoost Coding Part Two - The Model__en.srt 26.6 KB
22 - K-Means Clustering/005 K-Means Clustering Coding Part Two__en.srt 26.5 KB
24 - DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition__en.srt 26.5 KB
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