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| 1. Installing and Authenticating.mp4 | 15.4 MB | ||
| 1. Installing and Authenticating.srt | 1.8 KB | ||
| 1. Introduction to Avocado Prices Dataset.mp4 | 6.5 MB | ||
| 1. Introduction to Avocado Prices Dataset.srt | 1.2 KB | ||
| 1. Introduction to House Prices - Advanced Regression Techniques Competition.mp4 | 17.5 MB | ||
| 1. Introduction to House Prices - Advanced Regression Techniques Competition.srt | 4.1 KB | ||
| 1. Introduction to Survival Analysis.mp4 | 18.7 MB | ||
| 1. Introduction to Survival Analysis.srt | 2.8 KB | ||
| 1. Introduction to Telco Customer Churn Dataset.mp4 | 19.7 MB | ||
| 1. Introduction to Telco Customer Churn Dataset.srt | 3.9 KB | ||
| 1. Introduction to the Iris Species Dataset.mp4 | 7.1 MB | ||
| 1. Introduction to the Iris Species Dataset.srt | 1.6 KB | ||
| 1. Introduction to the Titanic - Machine Learning from Disaster Competition.mp4 | 50.7 MB | ||
| 1. Introduction to the Titanic - Machine Learning from Disaster Competition.srt | 12.2 KB | ||
| 1. Kaggle Categories and Performance Tiers.mp4 | 20.1 MB | ||
| 1. Kaggle Categories and Performance Tiers.srt | 5.7 KB | ||
| 1. Kaggle Competitions.mp4 | 57 MB | ||
| 1. Kaggle Competitions.srt | 12 KB | ||
| 1. New York City Taxi Trip Duration Prediction and Anomaly Detection Notebook.html | 204.8 B | ||
| 1. Types of Datasets on Kaggle.mp4 | 34.5 MB | ||
| 1. Types of Datasets on Kaggle.srt | 5.5 KB | ||
| 1. Types of Kaggle Kernels.mp4 | 15 MB | ||
| 1. Types of Kaggle Kernels.srt | 3.8 KB | ||
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| 10. Kernel Metadata File.mp4 | 8.8 MB | ||
| 10. Kernel Metadata File.srt | 1.1 KB | ||
| 11. Push and Pull a Kernel.mp4 | 8.6 MB | ||
| 11. Push and Pull a Kernel.srt | 1.1 KB | ||
| 12. Checking the Status and Output of a Kernel.mp4 | 10.2 MB | ||
| 12. Checking the Status and Output of a Kernel.srt | 1.2 KB | ||
| 13. Creating and Running a New Kernel.mp4 | 14.2 MB | ||
| 13. Creating and Running a New Kernel.srt | 1.4 KB | ||
| 14. Creating and Running a New Kernel Version.mp4 | 17.8 MB | ||
| 14. Creating and Running a New Kernel Version.srt | 1.2 KB | ||
| 15. Configurations.mp4 | 18.3 MB | ||
| 15. Configurations.srt | 2.7 KB | ||
| 2. Avocado Price Forecasting using Facebook Prophet.mp4 | 39.2 MB | ||
| 2. Avocado Price Forecasting using Facebook Prophet.srt | 8.6 KB | ||
| 2. Censorship.mp4 | 26.6 MB | ||
| 2. Censorship.srt | 4.6 KB | ||
| 2. Coding an Iris Species Classifier in Sci-kit Learn.mp4 | 39.8 MB | ||
| 2. Coding an Iris Species Classifier in Sci-kit Learn.srt | 10.8 KB | ||
| 2. Competition Formats.mp4 | 24.4 MB | ||
| 2. Competition Formats.srt | 5.3 KB | ||
| 2. House Prices Dataset Description.mp4 | 40.1 MB | ||
| 2. House Prices Dataset Description.srt | 7.3 KB | ||
| 2. Implementing a Model to Predict Survival of Titanic Passengers.mp4 | 68 MB | ||
| 2. Implementing a Model to Predict Survival of Titanic Passengers.srt | 18.5 KB | ||
| 2. Kaggle API with Competitions.mp4 | 11.4 MB | ||
| 2. Kaggle API with Competitions.srt | 1.4 KB | ||
| 2. Kaggle Medals.mp4 | 9 MB | ||
| 2. Kaggle Medals.srt | 2.3 KB | ||
| 2. Searching for Datasets.mp4 | 26.3 MB | ||
| 2. Searching for Datasets.srt | 4.1 KB | ||
| 2. Searching for Kernels.mp4 | 22.1 MB | ||
| 2. Searching for Kernels.srt | 4.4 KB | ||
| 2. Telco Customer Churn Pipeline Data Analysis and Visualization.mp4 | 53.8 MB | ||
| 2. Telco Customer Churn Pipeline Data Analysis and Visualization.srt | 10.2 KB | ||
| 3. Avocado Price Forecasting using Facebook Prophet Project Notebook.html | 204.8 B | ||
| 3. Creating a Dataset.mp4 | 32.7 MB | ||
| 3. Creating a Dataset.srt | 6.4 KB | ||
| 3. House Price Prediction using Random Forest.mp4 | 121.3 MB | ||
| 3. House Price Prediction using Random Forest.srt | 23.6 KB | ||
| 3. Iris Species Classifier Project Notebook.html | 204.8 B | ||
| 3. Joining a Competition.mp4 | 14.1 MB | ||
| 3. Joining a Competition.srt | 3.5 KB | ||
| 3. Kernel Editor.mp4 | 11.1 MB | ||
| 3. Kernel Editor.srt | 2.3 KB | ||
| 3. Listing Competitions and Competition Files.mp4 | 19.5 MB | ||
| 3. Listing Competitions and Competition Files.srt | 2.6 KB | ||
| 3. More on Kaggle Progression.mp4 | 8.7 MB | ||
| 3. More on Kaggle Progression.srt | 2.2 KB | ||
| 3. Telco Customer Churn Pipeline Data Preprocessing.mp4 | 32.8 MB | ||
| 3. Telco Customer Churn Pipeline Data Preprocessing.srt | 5.7 KB | ||
| 3. The Survival Function and the Hazard Function.mp4 | 18.3 MB | ||
| 3. The Survival Function and the Hazard Function.srt | 2.8 KB | ||
| 3. Titanic - Machine Learning from Disaster Project Notebook.html | 204.8 B | ||
| 4. Data Sources.mp4 | 7.3 MB | ||
| 4. Data Sources.srt | 1.2 KB | ||
| 4. Downloading a Competition.mp4 | 11.4 MB | ||
| 4. Downloading a Competition.srt | 1.5 KB | ||
| 4. Forming a Team.mp4 | 11.3 MB | ||
| 4. Forming a Team.srt | 3.2 KB | ||
| 4. House Price Prediction using Random Forest Project Notebook.html | 204.8 B | ||
| 4. Kaplan-Meier Estimate and Nelson Aalen Fitter.srt | 3.5 KB | ||
| 4. Organizations and Dataset Collaborations.mp4 | 25 MB | ||
| 4 | 250.8 KB | ||
| 4. Kaplan-Meier Estimate and Nelson Aalen Fitter.mp4 | 22 MB | ||
| 4. Organizations and Dataset Collaborations.srt | 3.5 KB | ||
| 4. Survival Analysis to predict Churn using Kaplan-Meier Estimate.mp4 | 50.2 MB | ||
| 4. Survival Analysis to predict Churn using Kaplan-Meier Estimate.srt | 9.3 KB | ||
| 5. Collaborating on Kernels.mp4 | 6.6 MB | ||
| 5. Collaborating on Kernels.srt | 1.3 KB | ||
| 5. Kaggle API with Datasets.mp4 | 12.1 MB | ||
| 5. Kaggle API with Datasets.srt | 1 KB | ||
| 5. Kaggle Datasets - Technical Specifications.mp4 | 11.6 MB | ||
| 5. Kaggle Datasets - Technical Specifications.srt | 1.4 KB | ||
| 5. Making a Submission.mp4 | 16 MB | ||
| 5. Making a Submission.srt | 4.4 KB | ||
| 5. Survival Regression - Cox Proportional Hazard Regression Model.mp4 | 52.2 MB | ||
| 5. Survival Regression - Cox Proportional Hazard Regression Model.srt | 7.8 KB | ||
| 5. Survival Regression Analysis using Cox Proportional Hazard Regression Model.mp4 | 78.1 MB | ||
| 5. Survival Regression Analysis using Cox Proportional Hazard Regression Model.srt | 13.6 KB | ||
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| 6. Data Leakage.mp4 | 9.8 MB | ||
| 6. Data Leakage.srt | 2.9 KB | ||
| 6. Listing and Downloading Datasets.mp4 | 16.3 MB | ||
| 6. Listing and Downloading Datasets.srt | 2 KB | ||
| 6. More on Kaggle Kernels.mp4 | 21.5 MB | ||
| 6. More on Kaggle Kernels.srt | 5.2 KB | ||
| 6. Survival Regression Analysis to Predict Churn Project Notebook.html | 204.8 B | ||
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| 7. Creating and Maintaining Datasets.mp4 | 23 MB | ||
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| 7. Creating and Maintaining Datasets.srt | 2.6 KB | ||
| 7. Kaggle Kernels - Technical Specifications.mp4 | 7.9 MB | ||
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| 8. Kaggle API with Kernels.mp4 | 14 MB | ||
| 8. Kaggle API with Kernels.srt | 1.3 KB | ||
| 9. Listing Kaggle Kernels.mp4 | 17.4 MB | ||
| 9. Listing Kaggle Kernels.srt | 2.2 KB | ||
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| ▲ 172 total files | |||
Description
This career-ready Masterclass is designed to help you gain hands-on and in-depth exposure to the domain of Data Science by adopting the learn by doing approach. And the best way to land your dream job is to build a portfolio of projects. And the best platform for a Data Scientist is Kaggle!
Over the years, Kaggle has become the most popular community for Data Scientists. Kaggle not only helps you learn new skills and apply new techniques, but it now plays a crucial role in your career as a Data Professional.
This course will give you in-depth hands-on experience with a variety of projects that include the necessary components to become a proficient data scientist. By completing the projects in this course, you will gain hands-on experience with these components and have a set of projects to reflect what you have learned. These components include the following:
Data Analysis and Wrangling using NumPy and Pandas.
Exploratory Data Analysis using Matplotlib and Seaborn.
Machine Learning using Scikit Learn.
Deep Learning using TensorFlow.
Time Series Forecasting using Facebook Prophet.
Time Series Forecasting using Scikit-Time.
This course primarily focuses on helping you stand out by building a portfolio comprising of a series of Jupyter Notebooks in Python that utilizes Competitions and Public Datasets hosted on the Kaggle platform. You will set up your Kaggle profile that will help you stand out for future employment opportunities.
Who this course is for:
Beginner Python Developers who want to get into Data Science.
Data Scientists looking forward to expand their skillset.
Data Scientists and Aspiring Data Scientists who wish to create a strong portfolio for potential career opportunities.
Requirements
Intermediate Python Programming Skills.
Last Updated 4/2021
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 9.01 GB | Neozoon | 2 years | 11 | 0 | |
| 3.6 GB | freecoursewb | 4 years | 0 | 0 | |
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Udemy - Kaggle - Get The Best Data Science, Machine Learning Profile Posted by
freecoursewb in Other
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1.5 GB | freecoursewb | 4 years | 0 | 0 |
| 1.1 GB | freecoursewb | 4 years | 0 | 0 | |
| 4.6 GB | tutsgalaxy | 6 years | 10 | 6 |
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