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种子名称： GetFreeCourses.Co-Udemy-2022 Python for Machine Learning & Data Science Masterclass
文件类型： 视频
文件数目： 225个文件
文件大小： 11.25 GB
收录时间： 2023-2-8 20:41
已经下载： 3次
资源热度： 154
最近下载： 2024-5-14 04:08

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

01 - Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP_.mp47.22MB

01 - Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp484.53MB

01 - Introduction to Course/005 Environment Setup.mp435.71MB

02 - OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp429.74MB

02 - OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp457.63MB

02 - OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp432.01MB

02 - OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp43.41MB

02 - OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp448.7MB

03 - Machine Learning Pathway Overview/001 Machine Learning Pathway.mp414.1MB

04 - NumPy/001 Introduction to NumPy.mp43.37MB

04 - NumPy/002 NumPy Arrays.mp499.45MB

04 - NumPy/003 NumPy Indexing and Selection.mp439.63MB

04 - NumPy/004 NumPy Operations.mp436.06MB

04 - NumPy/005 NumPy Exercises.mp49.64MB

04 - NumPy/006 Numpy Exercises - Solutions.mp434.88MB

05 - Pandas/001 Introduction to Pandas.mp46.7MB

05 - Pandas/002 Series - Part One.mp428.62MB

05 - Pandas/003 Series - Part Two.mp426.12MB

05 - Pandas/004 DataFrames - Part One - Creating a DataFrame.mp497.48MB

05 - Pandas/005 DataFrames - Part Two - Basic Properties.mp440.28MB

05 - Pandas/006 DataFrames - Part Three - Working with Columns.mp484.08MB

05 - Pandas/007 DataFrames - Part Four - Working with Rows.mp472.59MB

05 - Pandas/008 Pandas - Conditional Filtering.mp469.21MB

05 - Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp453.72MB

05 - Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp485.32MB

05 - Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp474.37MB

05 - Pandas/012 Missing Data - Overview.mp427.24MB

05 - Pandas/013 Missing Data - Pandas Operations.mp473.6MB

05 - Pandas/014 GroupBy Operations - Part One.mp486.96MB

05 - Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp492.86MB

05 - Pandas/016 Combining DataFrames - Concatenation.mp436.84MB

05 - Pandas/017 Combining DataFrames - Inner Merge.mp440.27MB

05 - Pandas/018 Combining DataFrames - Left and Right Merge.mp416.4MB

05 - Pandas/019 Combining DataFrames - Outer Merge.mp422.17MB

05 - Pandas/020 Pandas - Text Methods for String Data.mp445.12MB

05 - Pandas/021 Pandas - Time Methods for Date and Time Data.mp480.19MB

05 - Pandas/022 Pandas Input and Output - CSV Files.mp437.15MB

05 - Pandas/023 Pandas Input and Output - HTML Tables.mp4102.34MB

05 - Pandas/024 Pandas Input and Output - Excel Files.mp425.87MB

05 - Pandas/025 Pandas Input and Output - SQL Databases.mp495.98MB

05 - Pandas/026 Pandas Pivot Tables.mp4129.09MB

05 - Pandas/027 Pandas Project Exercise Overview.mp439.43MB

05 - Pandas/028 Pandas Project Exercise Solutions.mp4172.55MB

06 - Matplotlib/001 Introduction to Matplotlib.mp46.55MB

06 - Matplotlib/002 Matplotlib Basics.mp431.07MB

06 - Matplotlib/003 Matplotlib - Understanding the Figure Object.mp411.7MB

06 - Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp434.86MB

06 - Matplotlib/005 Matplotlib - Figure Parameters.mp413.06MB

06 - Matplotlib/006 Matplotlib - Subplots Functionality.mp496.57MB

06 - Matplotlib/007 Matplotlib Styling - Legends.mp416.19MB

06 - Matplotlib/008 Matplotlib Styling - Colors and Styles.mp444.27MB

06 - Matplotlib/009 Advanced Matplotlib Commands (Optional).mp425.19MB

06 - Matplotlib/010 Matplotlib Exercise Questions Overview.mp448.99MB

06 - Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4105.86MB

07 - Seaborn Data Visualizations/001 Introduction to Seaborn.mp45.74MB

07 - Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4111.3MB

07 - Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp415.03MB

07 - Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp459.21MB

07 - Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp415.98MB

07 - Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp451.65MB

07 - Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp444.96MB

07 - Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp484.57MB

07 - Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp410.57MB

07 - Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp451.16MB

07 - Seaborn Data Visualizations/011 Seaborn Grid Plots.mp487.01MB

07 - Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp461.47MB

07 - Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp447.88MB

07 - Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4105.72MB

08 - Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp431.11MB

08 - Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4110.61MB

08 - Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4106.18MB

08 - Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three.mp4137.39MB

09 - Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp413.17MB

09 - Machine Learning Concepts Overview/002 Why Machine Learning_.mp421.04MB

09 - Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp418.08MB

09 - Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp433.53MB

09 - Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp45.11MB

10 - Linear Regression/001 Introduction to Linear Regression Section.mp42.58MB

10 - Linear Regression/002 Linear Regression - Algorithm History.mp454.82MB

10 - Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp486.37MB

10 - Linear Regression/004 Linear Regression - Cost Functions.mp416.63MB

10 - Linear Regression/005 Linear Regression - Gradient Descent.mp429.21MB

10 - Linear Regression/006 Python coding Simple Linear Regression.mp470.14MB

10 - Linear Regression/007 Overview of Scikit-Learn and Python.mp431.44MB

10 - Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp461.42MB

10 - Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp453.4MB

10 - Linear Regression/010 Linear Regression - Residual Plots.mp444.02MB

10 - Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp481.14MB

10 - Linear Regression/012 Polynomial Regression - Theory and Motivation.mp422.25MB

10 - Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp440.09MB

10 - Linear Regression/014 Polynomial Regression - Training and Evaluation.mp436.3MB

10 - Linear Regression/015 Bias Variance Trade-Off.mp436.18MB

10 - Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp455.68MB

10 - Linear Regression/017 Polynomial Regression - Model Deployment.mp423.22MB

10 - Linear Regression/018 Regularization Overview.mp415.52MB

10 - Linear Regression/019 Feature Scaling.mp424.34MB

10 - Linear Regression/020 Introduction to Cross Validation.mp432.97MB

10 - Linear Regression/021 Regularization Data Setup.mp420.16MB

10 - Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp461.3MB

10 - Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp489.37MB

10 - Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp494.65MB

10 - Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp466.4MB

10 - Linear Regression/026 Linear Regression Project - Data Overview.mp416.94MB

11 - Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp436.11MB

11 - Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4103.32MB

11 - Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp419.05MB

11 - Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4117.56MB

11 - Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4105.22MB

11 - Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp458.87MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp45.61MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp446.86MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp459.41MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp444.46MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp445.01MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp473.19MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp423.63MB

12 - Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp491.23MB

13 - Logistic Regression/002 Introduction to Logistic Regression Section.mp413.93MB

13 - Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp417.31MB

13 - Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp48.03MB

13 - Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp436.04MB

13 - Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp454.91MB

13 - Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp462.45MB

13 - Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp432.57MB

13 - Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp421.72MB

13 - Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp433.14MB

13 - Logistic Regression/011 Classification Metrics - ROC Curves.mp416.07MB

13 - Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp457.03MB

13 - Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp437.38MB

13 - Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4105.09MB

13 - Logistic Regression/015 Logistic Regression Exercise Project Overview.mp424.29MB

13 - Logistic Regression/016 Logistic Regression Project Exercise - Solutions.mp4161.29MB

14 - KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp43.65MB

14 - KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp423.55MB

14 - KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp461.55MB

14 - KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4102.86MB

14 - KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp421.12MB

14 - KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4105.03MB

15 - Support Vector Machines/001 Introduction to Support Vector Machines.mp42.79MB

15 - Support Vector Machines/002 History of Support Vector Machines.mp415.54MB

15 - Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp447.74MB

15 - Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp49.83MB

15 - Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp452.62MB

15 - Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp446.28MB

15 - Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp490.63MB

15 - Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp476.27MB

15 - Support Vector Machines/009 Support Vector Machine Project Overview.mp434.84MB

15 - Support Vector Machines/010 Support Vector Machine Project Solutions.mp493.36MB

16 - Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp42.33MB

16 - Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp435.58MB

16 - Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp47.29MB

16 - Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp419.45MB

16 - Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp417.69MB

16 - Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp452.35MB

16 - Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp498.72MB

16 - Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4115.8MB

17 - Random Forests/001 Introduction to Random Forests Section.mp42.87MB

17 - Random Forests/002 Random Forests - History and Motivation.mp424MB

17 - Random Forests/003 Random Forests - Key Hyperparameters.mp48.27MB

17 - Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp427.31MB

17 - Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp432.72MB

17 - Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp452.1MB

17 - Random Forests/007 Coding Classification with Random Forest Classifier - Part Two.mp4130.37MB

17 - Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp413.68MB

17 - Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp485.01MB

17 - Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp445.54MB

17 - Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp450.67MB

18 - Boosting Methods/001 Introduction to Boosting Section.mp42.99MB

18 - Boosting Methods/002 Boosting Methods - Motivation and History.mp421.98MB

18 - Boosting Methods/003 AdaBoost Theory and Intuition.mp441.53MB

18 - Boosting Methods/004 AdaBoost Coding Part One - The Data.mp442.25MB

18 - Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp463.11MB

18 - Boosting Methods/006 Gradient Boosting Theory.mp422.96MB

18 - Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp457.91MB

19 - Supervised Learning Capstone Project/001 Introduction to Supervised Learning Capstone Project.mp429.84MB

19 - Supervised Learning Capstone Project/002 Solution Walkthrough - Supervised Learning Project - Data and EDA.mp4106.1MB

19 - Supervised Learning Capstone Project/003 Solution Walkthrough - Supervised Learning Project - Cohort Analysis.mp4130.14MB

19 - Supervised Learning Capstone Project/004 Solution Walkthrough - Supervised Learning Project - Tree Models.mp4114.21MB

20 - Naive Bayes Classification and Natural Language Processing/001 Introduction to NLP and Naive Bayes Section.mp44.22MB

20 - Naive Bayes Classification and Natural Language Processing/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp422.04MB

20 - Naive Bayes Classification and Natural Language Processing/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp448.61MB

20 - Naive Bayes Classification and Natural Language Processing/004 Feature Extraction from Text - Part One - Theory and Intuition.mp429.4MB

20 - Naive Bayes Classification and Natural Language Processing/005 Feature Extraction from Text - Coding Count Vectorization Manually.mp40B

20 - Naive Bayes Classification and Natural Language Processing/006 Feature Extraction from Text - Coding with Scikit-Learn.mp450.39MB

20 - Naive Bayes Classification and Natural Language Processing/007 Natural Language Processing - Classification of Text - Part One.mp428.26MB

20 - Naive Bayes Classification and Natural Language Processing/008 Natural Language Processing - Classification of Text - Part Two.mp434.77MB

20 - Naive Bayes Classification and Natural Language Processing/009 Text Classification Project Exercise Overview.mp430.54MB

20 - Naive Bayes Classification and Natural Language Processing/010 Text Classification Project Exercise Solutions.mp4100.59MB

21 - Unsupervised Learning/001 Unsupervised Learning Overview.mp413.75MB

22 - K-Means Clustering/001 Introduction to K-Means Clustering Section.mp43.55MB

22 - K-Means Clustering/002 Clustering General Overview.mp424.86MB

22 - K-Means Clustering/003 K-Means Clustering Theory.mp452.49MB

22 - K-Means Clustering/004 K-Means Clustering - Coding Part One.mp497.9MB

22 - K-Means Clustering/005 K-Means Clustering Coding Part Two.mp480.85MB

22 - K-Means Clustering/006 K-Means Clustering Coding Part Three.mp459.77MB

22 - K-Means Clustering/007 K-Means Color Quantization - Part One.mp480.57MB

22 - K-Means Clustering/008 K-Means Color Quantization - Part Two.mp465.03MB

22 - K-Means Clustering/009 K-Means Clustering Exercise Overview.mp459.48MB

22 - K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp479.92MB

22 - K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4108.19MB

22 - K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp462.5MB

23 - Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp41.67MB

23 - Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp452.07MB

23 - Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4114.98MB

23 - Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4209.23MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp41.8MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4109.09MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp466.64MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp413.86MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4105.08MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp450.27MB

24 - DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4127.93MB

25 - PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp45.08MB

25 - PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp429.72MB

25 - PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp419.04MB

25 - PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp495.04MB

25 - PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp474.09MB

25 - PCA - Principal Component Analysis and Manifold Learning/006 PCA - Project Exercise Overview.mp452.77MB

25 - PCA - Principal Component Analysis and Manifold Learning/007 PCA - Project Exercise Solution.mp4119.45MB

26 - Model Deployment/001 Model Deployment Section Overview.mp44.16MB

26 - Model Deployment/002 Model Deployment Considerations.mp418.31MB

26 - Model Deployment/003 Model Persistence.mp4109.76MB

26 - Model Deployment/004 Model Deployment as an API - General Overview.mp417.48MB

26 - Model Deployment/006 Model API - Creating the Script.mp467.27MB

26 - Model Deployment/007 Testing the API.mp433.15MB