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[Udemy] 40 Days Bootcamp 40 Data Science and Machine Learning Projects (09.2021)

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种子名称: [Udemy] 40 Days Bootcamp 40 Data Science and Machine Learning Projects (09.2021)
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文件数目: 209个文件
文件大小: 13.26 GB
收录时间: 2022-2-5 05:42
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最近下载: 2024-5-17 02:49

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[Udemy] 40 Days Bootcamp 40 Data Science and Machine Learning Projects (09.2021).torrent
  • 1. Introduction To The Course/1. Introduction to the course.mp468.38MB
  • 1. Introduction To The Course/2. Udemy Course Feedback.mp42.05MB
  • 10. Project-9 Bird Species Prediction Application/1. Introduction.mp421.48MB
  • 10. Project-9 Bird Species Prediction Application/2. Improting Libraries And Data.mp457.48MB
  • 10. Project-9 Bird Species Prediction Application/3. Data Processing.mp430.95MB
  • 10. Project-9 Bird Species Prediction Application/4. 4Creating Machine Learning Model.mp494.38MB
  • 10. Project-9 Bird Species Prediction Application/5. Creating A Flask Application.mp472.09MB
  • 11. Project-10 Intel Image Classification/1. Introduction.mp425.75MB
  • 11. Project-10 Intel Image Classification/2. Importing and processing data.mp465.85MB
  • 11. Project-10 Intel Image Classification/3. Creating a Model.mp488.48MB
  • 11. Project-10 Intel Image Classification/4. Creating a Flask App.mp459.5MB
  • 12. Project-11 Sentiment Analysis Using Logistic Regression/1. Introduction.mp49.16MB
  • 12. Project-11 Sentiment Analysis Using Logistic Regression/2. Project Notebook Google Colab.mp4140.8MB
  • 12. Project-11 Sentiment Analysis Using Logistic Regression/3. Building Django App.mp471.96MB
  • 12. Project-11 Sentiment Analysis Using Logistic Regression/4. Deploying App in Heroku.mp479.15MB
  • 13. Project-12 Attrition Rate Django App/1. Introduction.mp49.25MB
  • 13. Project-12 Attrition Rate Django App/2. Creating Colab Notebook.mp4213.68MB
  • 13. Project-12 Attrition Rate Django App/3. Creating Django App.mp485.04MB
  • 13. Project-12 Attrition Rate Django App/4. Deploying App in Heroku.mp420.34MB
  • 14. Project-13 Project To Find Legendary Pokemon/1. Introduction.mp410.3MB
  • 14. Project-13 Project To Find Legendary Pokemon/2. Creating Colab Notebook.mp4177.14MB
  • 14. Project-13 Project To Find Legendary Pokemon/3. Creating Django App.mp4108.24MB
  • 14. Project-13 Project To Find Legendary Pokemon/4. Deploying App in Heroku.mp462.21MB
  • 15. Project-14 Create Face Detection Application/1. Introduction to face app.mp411.39MB
  • 15. Project-14 Create Face Detection Application/2. Creating The Face App Using OpenCV.mp4103.74MB
  • 15. Project-14 Create Face Detection Application/3. Creating The Face App Part-2.mp465.66MB
  • 15. Project-14 Create Face Detection Application/4. Creating The face app Part-3.mp494.19MB
  • 16. Project-15 Cats Vs Dogs Image Classification App/1. Introduction.mp413.17MB
  • 16. Project-15 Cats Vs Dogs Image Classification App/2. Creating Project Notebook.mp4244.28MB
  • 16. Project-15 Cats Vs Dogs Image Classification App/3. Building Model.mp474.68MB
  • 16. Project-15 Cats Vs Dogs Image Classification App/4. Building Flask App.mp451.89MB
  • 16. Project-15 Cats Vs Dogs Image Classification App/5. Flask App Deployment.mp465.82MB
  • 17. Project-16 Customer Revenue Prediction/1. Introduction.mp413.29MB
  • 17. Project-16 Customer Revenue Prediction/2. Colab Notebook.mp4201.74MB
  • 17. Project-16 Customer Revenue Prediction/3. Creating Flask App.mp4117.96MB
  • 17. Project-16 Customer Revenue Prediction/4. Deploying Flask App.mp466.46MB
  • 17. Project-16 Customer Revenue Prediction/5. Creating an app using streamlit.mp451.26MB
  • 18. Project-17 Gender From Voice Prediction/1. Introduction.mp410.55MB
  • 18. Project-17 Gender From Voice Prediction/2. Creating Project Notebook.mp4243.01MB
  • 18. Project-17 Gender From Voice Prediction/3. Creating Project App Django.mp4106.68MB
  • 18. Project-17 Gender From Voice Prediction/4. Deploying The App.mp461.09MB
  • 19. Project-18 Restaurant Recommendation System/1. Introduction.mp421.28MB
  • 19. Project-18 Restaurant Recommendation System/2. Creating Colab Notebook.mp4116.65MB
  • 19. Project-18 Restaurant Recommendation System/3. Exploratory Data Analysis.mp485.45MB
  • 19. Project-18 Restaurant Recommendation System/4. Data Analysis 2.mp4242.84MB
  • 2. Project-1 Pan Card Tempering Detector/1. Introduction To Pan Card Tempering Detector.mp44.26MB
  • 2. Project-1 Pan Card Tempering Detector/2. Loading libraries and dataset.mp431.48MB
  • 2. Project-1 Pan Card Tempering Detector/3. Creating the pancard detector with opencv.mp4106.96MB
  • 2. Project-1 Pan Card Tempering Detector/4. Creating the Flask App.mp414.93MB
  • 2. Project-1 Pan Card Tempering Detector/5. Creating Important functions.mp429.08MB
  • 2. Project-1 Pan Card Tempering Detector/6. Deploy the app in Heroku.mp445.52MB
  • 2. Project-1 Pan Card Tempering Detector/7. Testing the deployed pan card detector.mp45.71MB
  • 20. Project-19 Check Happiness Ranking Using Machine Learning/1. Introduction to Happiness Ranking.mp416.33MB
  • 20. Project-19 Check Happiness Ranking Using Machine Learning/2. Project Notebook.mp4240.54MB
  • 20. Project-19 Check Happiness Ranking Using Machine Learning/3. Creating Django App.mp4138.28MB
  • 20. Project-19 Check Happiness Ranking Using Machine Learning/4. Deploying Django App.mp459.4MB
  • 21. Project-20 Predicting Forest Fire/1. Introduction To Forest Fire.mp419.42MB
  • 21. Project-20 Predicting Forest Fire/2. Project Notebook.mp4207.15MB
  • 21. Project-20 Predicting Forest Fire/3. Project Notebook Part-2.mp476.38MB
  • 21. Project-20 Predicting Forest Fire/4. Creating Django App.mp4117.09MB
  • 21. Project-20 Predicting Forest Fire/5. Deploying Django App.mp457.59MB
  • 22. Project-21 Black Friday Sale Prediction/1. Importing libraries and data.mp436.44MB
  • 22. Project-21 Black Friday Sale Prediction/2. Understanding data.mp437.56MB
  • 22. Project-21 Black Friday Sale Prediction/3. Model building part1.mp449.29MB
  • 22. Project-21 Black Friday Sale Prediction/4. Model building part2.mp458.84MB
  • 23. Project-22 Sentiment Analysis Using NLP/1. Importing libraries and data.mp465.13MB
  • 23. Project-22 Sentiment Analysis Using NLP/2. Text normalization.mp474.96MB
  • 23. Project-22 Sentiment Analysis Using NLP/3. Lemmatization.mp445.4MB
  • 23. Project-22 Sentiment Analysis Using NLP/4. Data preprocessing.mp4103.67MB
  • 23. Project-22 Sentiment Analysis Using NLP/5. Model Building.mp487.09MB
  • 24. Project-23 Parkinson Syndrome prediction/1. Importing libraries and data.mp439.75MB
  • 24. Project-23 Parkinson Syndrome prediction/2. Understanding the data.mp473.38MB
  • 24. Project-23 Parkinson Syndrome prediction/3. Data visualization.mp454.91MB
  • 24. Project-23 Parkinson Syndrome prediction/4. Model building part 1.mp474.58MB
  • 24. Project-23 Parkinson Syndrome prediction/5. Model building part 2.mp4104.84MB
  • 25. Project-24 Fake News Classifier/1. Importing libraries and data.mp432.14MB
  • 25. Project-24 Fake News Classifier/2. Data preprocessing.mp440.75MB
  • 25. Project-24 Fake News Classifier/3. Text cleaning.mp460.98MB
  • 25. Project-24 Fake News Classifier/4. Vectorizer.mp418.59MB
  • 25. Project-24 Fake News Classifier/5. Model Building.mp4101.89MB
  • 26. Project-25 Toxic Comment Classifier/1. Importing libraries and data.mp447.82MB
  • 26. Project-25 Toxic Comment Classifier/2. Understanding data.mp431.93MB
  • 26. Project-25 Toxic Comment Classifier/3. Data visualization and preprocessing.mp480.52MB
  • 26. Project-25 Toxic Comment Classifier/4. Balancing the target column.mp451.5MB
  • 26. Project-25 Toxic Comment Classifier/5. Model building.mp475.5MB
  • 26. Project-25 Toxic Comment Classifier/6. Model evaluation.mp444.57MB
  • 27. Project-26 Movie Ratings Prediction/1. Importing libraries and data.mp485.72MB
  • 27. Project-26 Movie Ratings Prediction/2. Understanding data.mp492.19MB
  • 27. Project-26 Movie Ratings Prediction/3. Data visualization.mp468.62MB
  • 27. Project-26 Movie Ratings Prediction/4. Data preprocessing.mp470.49MB
  • 27. Project-26 Movie Ratings Prediction/5. Model building.mp4114.48MB
  • 28. Project-27 Indian Air Quality Prediction/1. Importing libraries and data.mp459.53MB
  • 28. Project-27 Indian Air Quality Prediction/2. Understanding data.mp471.72MB
  • 28. Project-27 Indian Air Quality Prediction/3. Data visualization.mp492.54MB
  • 28. Project-27 Indian Air Quality Prediction/4. Data preprocessing.mp422.75MB
  • 28. Project-27 Indian Air Quality Prediction/5. Feature Engineering.mp4113.84MB
  • 28. Project-27 Indian Air Quality Prediction/6. Model building part1.mp4123.39MB
  • 28. Project-27 Indian Air Quality Prediction/7. Model building part2.mp449.45MB
  • 29. Project-28 Covid 19 Data Analysis/1. Importing libraries and data.mp462.02MB
  • 29. Project-28 Covid 19 Data Analysis/2. data preprocessing.mp4119.22MB
  • 29. Project-28 Covid 19 Data Analysis/3. Data Analysis part1.mp4102.52MB
  • 29. Project-28 Covid 19 Data Analysis/4. Data Analysis part2.mp4134.97MB
  • 3. Project-2 Dog breed prediction/1. Introduction to dog breed prediction.mp415.92MB
  • 3. Project-2 Dog breed prediction/2. Importing the data and libraries.mp456.82MB
  • 3. Project-2 Dog breed prediction/3. Data Preprocessing.mp429.97MB
  • 3. Project-2 Dog breed prediction/4. Build and Train Model.mp473.21MB
  • 3. Project-2 Dog breed prediction/5. Testing the model.mp418.76MB
  • 3. Project-2 Dog breed prediction/6. Creating the Flask App.mp428.12MB
  • 3. Project-2 Dog breed prediction/7. Running the app in system.mp417.28MB
  • 30. Project-29 Customer Churning Prediction/1. Importing libraries and data.mp4113.33MB
  • 30. Project-29 Customer Churning Prediction/2. Understanding data.mp468.94MB
  • 30. Project-29 Customer Churning Prediction/3. Data visualization.mp4160.55MB
  • 30. Project-29 Customer Churning Prediction/4. Model building.mp4147.35MB
  • 30. Project-29 Customer Churning Prediction/5. Hypertuning.mp4102.19MB
  • 31. Project-30 Build A Powerful Chatbot/1. Importing libraries.mp439.07MB
  • 31. Project-30 Build A Powerful Chatbot/2. Understanding data.mp450.71MB
  • 31. Project-30 Build A Powerful Chatbot/3. Data visualization.mp474.32MB
  • 31. Project-30 Build A Powerful Chatbot/4. Text normalization.mp4112.8MB
  • 31. Project-30 Build A Powerful Chatbot/5. Creating a application.mp477.02MB
  • 32. Project-31 Video Game Sales Prediction App/1. Introduction.mp416.61MB
  • 32. Project-31 Video Game Sales Prediction App/2. Google Colab part-1.mp4202.1MB
  • 32. Project-31 Video Game Sales Prediction App/3. Google Colab part-2.mp447.06MB
  • 32. Project-31 Video Game Sales Prediction App/4. Creating Django App.mp4147.42MB
  • 32. Project-31 Video Game Sales Prediction App/5. Heroku App Deployment.mp464.58MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/1. Introduction.mp411.98MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/2. Machine Learning model building part1.mp462.47MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/3. Machine Learning model building part 2.mp4100.91MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/4. Machine Learning model building part 3.mp4110.8MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/5. Creating Django Application part1.mp476.58MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/6. Creating Django Application part 2.mp466.8MB
  • 33. Project-32 Car Selling Price Prediction App- Deploy On Heruko/7. Deploying on Heroku.mp453.55MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/1. Introduction.mp414.92MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/2. Introductory Machine Learning model building.mp495.65MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/3. Feature Building and Selection.mp4103.36MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/4. Model Building.mp412.37MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/5. Django Application Introduction.mp443.01MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/6. Django Application building.mp4111.38MB
  • 34. Project-33 Affair Prediction App - Deploy On Heruko/7. Deploying on Heroku.mp440.43MB
  • 35. Project-34 Mushroom Classification App - Deploy On Heruko/1. Introduction.mp414.23MB
  • 35. Project-34 Mushroom Classification App - Deploy On Heruko/2. Importing libraries and Understanding data.mp4129.54MB
  • 35. Project-34 Mushroom Classification App - Deploy On Heruko/3. Building the model.mp498.68MB
  • 35. Project-34 Mushroom Classification App - Deploy On Heruko/4. Building Django Application.mp4105.96MB
  • 35. Project-34 Mushroom Classification App - Deploy On Heruko/5. Deploying on Heroku.mp458.82MB
  • 36. Project-35 Mobile App Rating Prediction Django App- Deploy On Heruko/1. Introduction.mp412.14MB
  • 36. Project-35 Mobile App Rating Prediction Django App- Deploy On Heruko/2. Introduction to libraries and dataset.mp4108.74MB
  • 36. Project-35 Mobile App Rating Prediction Django App- Deploy On Heruko/3. Preprocessing the data.mp474.92MB
  • 36. Project-35 Mobile App Rating Prediction Django App- Deploy On Heruko/4. building the model.mp450.92MB
  • 36. Project-35 Mobile App Rating Prediction Django App- Deploy On Heruko/5. Django Application.mp494.85MB
  • 36. Project-35 Mobile App Rating Prediction Django App- Deploy On Heruko/6. Deploying to Heroku.mp448.16MB
  • 37. Project-36 Heart Attack Risk Prediction With Auto ML/1. Introduction to the Project.mp423.76MB
  • 37. Project-36 Heart Attack Risk Prediction With Auto ML/2. Importing Libraries and Datasets.mp415.71MB
  • 37. Project-36 Heart Attack Risk Prediction With Auto ML/3. Data Analysis.mp456.06MB
  • 37. Project-36 Heart Attack Risk Prediction With Auto ML/4. Model Building Part 1.mp446.71MB
  • 37. Project-36 Heart Attack Risk Prediction With Auto ML/5. Model Building Part 2.mp433.66MB
  • 37. Project-36 Heart Attack Risk Prediction With Auto ML/6. Model building and Predictions using Auto ML( Eval ML ).mp461.4MB
  • 38. Project-37 Credit Card Fraud Detection using PyCaret/1. Introduction to the Project.mp429.54MB
  • 38. Project-37 Credit Card Fraud Detection using PyCaret/2. Importing Libraries and DataSet.mp427.4MB
  • 38. Project-37 Credit Card Fraud Detection using PyCaret/3. Data Analysis.mp443.37MB
  • 38. Project-37 Credit Card Fraud Detection using PyCaret/4. Model Building using ML.mp417.39MB
  • 38. Project-37 Credit Card Fraud Detection using PyCaret/5. Model Building and Prediction using PyCaret (AutoML).mp497.05MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/1. Introduction to the Project.mp421.88MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/2. Importing Libraries and DataSet.mp445.19MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/3. Data Analysis.mp431.65MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/4. Feature Engineering 1.mp443.61MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/5. Feature Engineering 2.mp450.65MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/6. Feature Selection.mp425.01MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/7. Model Building using ML.mp438.48MB
  • 39. Project-38 Flight Fare Detection using Auto SK Learn/8. Model Building and Prediction using Auto SK Learn.mp441.86MB
  • 4. Project-3 Image Watermarking With Logo/1. Introduction.mp415.14MB
  • 4. Project-3 Image Watermarking With Logo/2. Importing libraries and logo.mp425.21MB
  • 4. Project-3 Image Watermarking With Logo/3. Create text and image watermark.mp475.94MB
  • 4. Project-3 Image Watermarking With Logo/4. Creating the app.mp422.78MB
  • 4. Project-3 Image Watermarking With Logo/5. Deploying the app in Heroku.mp429.99MB
  • 40. Project-39 Petrol Price Forecasting using Auto Keras/1. Introduction to the Project.mp430.57MB
  • 40. Project-39 Petrol Price Forecasting using Auto Keras/2. Importing Libraries and Data Set.mp420.03MB
  • 40. Project-39 Petrol Price Forecasting using Auto Keras/3. Data Analysis and splitting of Data.mp439.96MB
  • 40. Project-39 Petrol Price Forecasting using Auto Keras/4. Data Preprocessing.mp436.82MB
  • 40. Project-39 Petrol Price Forecasting using Auto Keras/5. Model Building and Prediction using LSTM model.mp432.58MB
  • 40. Project-39 Petrol Price Forecasting using Auto Keras/6. Model Building and prediction using ARIMA and Auto Keras (Auto ML).mp437.53MB
  • 41. Project-40 Bank Customer Churn Prediction using H2O Auto ML/1. Introduction to the Project.mp433.05MB
  • 41. Project-40 Bank Customer Churn Prediction using H2O Auto ML/2. Importing Libraries and DataSet.mp425.04MB
  • 41. Project-40 Bank Customer Churn Prediction using H2O Auto ML/3. Data Analysis.mp465.22MB
  • 41. Project-40 Bank Customer Churn Prediction using H2O Auto ML/4. Feature Engineering.mp416.5MB
  • 41. Project-40 Bank Customer Churn Prediction using H2O Auto ML/5. Model Building and Prediction using ANN.mp434.17MB
  • 41. Project-40 Bank Customer Churn Prediction using H2O Auto ML/6. Model Building and Prediction using H2O Auto ML(Auto ML).mp491.96MB
  • 5. Project-4 Traffic sign classification/1. Introduction.mp430.82MB
  • 5. Project-4 Traffic sign classification/2. importing the data and libraries.mp457.51MB
  • 5. Project-4 Traffic sign classification/3. Image processing.mp445.09MB
  • 5. Project-4 Traffic sign classification/4. Creating and testing the model.mp462.06MB
  • 5. Project-4 Traffic sign classification/5. Creating model for test set.mp447.47MB
  • 6. Project-5 Create A Text Extraction Application/1. Introduction to text extraction.mp418.51MB
  • 6. Project-5 Create A Text Extraction Application/2. Importing libraries and data.mp433.94MB
  • 6. Project-5 Create A Text Extraction Application/3. Extracting the test from image.mp440.7MB
  • 6. Project-5 Create A Text Extraction Application/4. Modifiying the extractor.mp469.94MB
  • 6. Project-5 Create A Text Extraction Application/5. Creating the extractor app.mp438.63MB
  • 6. Project-5 Create A Text Extraction Application/6. Running the extractor app.mp414.86MB
  • 7. Project-6 Plant Disease Prediction Application/1. Introduction.mp435.12MB
  • 7. Project-6 Plant Disease Prediction Application/2. Importing libraries and data.mp431.8MB
  • 7. Project-6 Plant Disease Prediction Application/3. Understanding the data and data preprocessing.mp449.77MB
  • 7. Project-6 Plant Disease Prediction Application/4. Model building.mp429.71MB
  • 7. Project-6 Plant Disease Prediction Application/5. Creating an app using streamlit.mp451.26MB
  • 8. Project-7 Vehicle Detection & Counting Application/1. Introduction.mp425.81MB
  • 8. Project-7 Vehicle Detection & Counting Application/2. Importing libraries and data.mp422.45MB
  • 8. Project-7 Vehicle Detection & Counting Application/3. Transforing Images and creating output.mp499.06MB
  • 8. Project-7 Vehicle Detection & Counting Application/4. Creating a Flask APP Vehicle.mp484.02MB
  • 9. Project-8 Create Face Swapping Application/1. Introduction.mp420.38MB
  • 9. Project-8 Create Face Swapping Application/2. Importing libraries and data.mp436.19MB
  • 9. Project-8 Create Face Swapping Application/3. Data preprocessing and creating output.mp4127.67MB
  • 9. Project-8 Create Face Swapping Application/4. Creating A Flask APP.mp478.52MB