Udemy - Data Science Real World Projects in Python

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Data Science Real World Projects in Python [TutsNode.com] - Data Science Real World Projects in Python 2. Project 1-- Predict Fare of Airlines Tickets using Machine Learning
  • 29. How to Cross Validate your model.mp4 (123.5 MB)
  • 21. Intuition Behind Linear Regression- Part 3.srt (28.4 KB)
  • 10. Intuition Behind Random Forest Part-1.srt (26.6 KB)
  • 16. Intuition Behind Decision Tree- Part 4.srt (23.4 KB)
  • 1. Introduction to Business Problem & Dataset.srt (2.0 KB)
  • 2. Datasets & Resources.html (0.2 KB)
  • 29. How to Cross Validate your model.srt (23.1 KB)
  • 14. Intuition Behind Decision Tree- Part 2.srt (21.9 KB)
  • 17. Intuition Behind Decision Tree- Part 5.srt (20.6 KB)
  • 15. Intuition Behind Decision Tree- Part 3.srt (20.0 KB)
  • 7. Perform Label Encoding on data.srt (18.9 KB)
  • 28. Intuition Behind Cross Validation- Part 2.srt (17.6 KB)
  • 22. Intuition Behind KNN- Part 1.srt (17.5 KB)
  • 6. Handle Categorical Data & Feature Encoding.srt (16.4 KB)
  • 3. Understanding Data & data-preprocessing.srt (16.2 KB)
  • 19. Intuition Behind Linear Regression- Part 1.srt (15.9 KB)
  • 11. Intuition Behind Random Forest Part-2.srt (15.4 KB)
  • 4. Extract Derived Features from Data.srt (15.1 KB)
  • 12. Applying Random Forest on Data & Automate predictions.srt (14.8 KB)
  • 13. Intuition Behind Decision Tree- Part 1.srt (14.0 KB)
  • 20. Intuition Behind Linear Regression- Part 2.srt (14.0 KB)
  • 27. Intuition Behind Cross Validation- Part 1.srt (13.6 KB)
  • 18. Intuition Behind Decision Tree- Part 6.srt (13.3 KB)
  • 5. Perform Data Pre-processing.srt (11.0 KB)
  • 25. Intuition Behind KNN- Part 4.srt (11.0 KB)
  • 23. Intuition Behind KNN- Part 2.srt (10.9 KB)
  • 26. Play with multiple Algorithms & dumping your model.srt (10.8 KB)
  • 8. How to handle Outliers in Data.srt (10.4 KB)
  • 7. Perform Label Encoding on data.mp4 (108.6 MB)
  • 24. Intuition Behind KNN- Part 3.srt (9.9 KB)
  • 9. Select best Features using Feature Selection Technique.srt (7.0 KB)
  • 6. Handle Categorical Data & Feature Encoding.mp4 (85.4 MB)
  • 21. Intuition Behind Linear Regression- Part 3.mp4 (81.8 MB)
  • 4. Extract Derived Features from Data.mp4 (80.4 MB)
  • 10. Intuition Behind Random Forest Part-1.mp4 (77.8 MB)
  • 12. Applying Random Forest on Data & Automate predictions.mp4 (75.0 MB)
  • 16. Intuition Behind Decision Tree- Part 4.mp4 (73.5 MB)
  • 3. Understanding Data & data-preprocessing.mp4 (70.4 MB)
  • 28. Intuition Behind Cross Validation- Part 2.mp4 (67.3 MB)
  • 14. Intuition Behind Decision Tree- Part 2.mp4 (63.2 MB)
  • 17. Intuition Behind Decision Tree- Part 5.mp4 (62.1 MB)
  • 15. Intuition Behind Decision Tree- Part 3.mp4 (60.7 MB)
  • 26. Play with multiple Algorithms & dumping your model.mp4 (60.3 MB)
  • 8. How to handle Outliers in Data.mp4 (53.6 MB)
  • 5. Perform Data Pre-processing.mp4 (53.5 MB)
  • 11. Intuition Behind Random Forest Part-2.mp4 (50.8 MB)
  • 20. Intuition Behind Linear Regression- Part 2.mp4 (44.3 MB)
  • 27. Intuition Behind Cross Validation- Part 1.mp4 (41.7 MB)
  • 22. Intuition Behind KNN- Part 1.mp4 (41.1 MB)
  • 19. Intuition Behind Linear Regression- Part 1.mp4 (40.9 MB)
  • 13. Intuition Behind Decision Tree- Part 1.mp4 (40.6 MB)
  • 18. Intuition Behind Decision Tree- Part 6.mp4 (39.6 MB)
  • 25. Intuition Behind KNN- Part 4.mp4 (38.0 MB)
  • 9. Select best Features using Feature Selection Technique.mp4 (37.0 MB)
  • 23. Intuition Behind KNN- Part 2.mp4 (33.8 MB)
  • 24. Intuition Behind KNN- Part 3.mp4 (28.4 MB)
  • 1. Introduction to Business Problem & Dataset.mp4 (21.7 MB)
3. Project 2----- Predict Password Strength using Natural Language Processing
  • 8. Intuition behind Logistic Regression --part 2.srt (17.3 KB)
  • 7. Intuition behind Logistic Regression --part 1.srt (15.2 KB)
  • 3. Exploring your data.srt (10.9 KB)
  • 6. Apply TF-IDF on data.srt (10.2 KB)
  • 9. Apply Logistic Regression on Data.srt (8.9 KB)
  • 4. Intuition behind TF-IDF --part 1.srt (8.2 KB)
  • 5. Intuition behind TF-IDF --part 2.srt (8.0 KB)
  • 10. Checking Accuracy of Model.srt (4.6 KB)
  • 1. Introduction to Business Problem & Dataset.srt (1.8 KB)
  • 2. Datasets & Resources.html (0.2 KB)
  • 6. Apply TF-IDF on data.mp4 (60.6 MB)
  • 7. Intuition behind Logistic Regression --part 1.mp4 (58.0 MB)
  • 3. Exploring your data.mp4 (57.1 MB)
  • 9. Apply Logistic Regression on Data.mp4 (53.0 MB)
  • 8. Intuition behind Logistic Regression --part 2.mp4 (44.1 MB)
  • 5. Intuition behind TF-IDF --part 2.mp4 (34.6 MB)
  • 10. Checking Accuracy of Model.mp4 (28.2 MB)
  • 4. Intuition behind TF-IDF --part 1.mp4 (24.8 MB)
  • 1. Introduction to Business Problem & Dataset.mp4 (13.7 MB)
4. Project 3-- Predict Stock Prices using Time Series Analysis
  • 6. Intuition behind MA model --ARIMA part 2.srt (13.4 KB)
  • 9. Apply Auto-Arima on data.srt (9.6 KB)
  • 4. Data preparation for Time Series Forecasting.srt (9.6 KB)
  • 8. Intuition behind Integrating -- ARIMA part 4.srt (9.4 KB)
  • 7. Intuition behind AR model -- ARIMA part 3.srt (7.3 KB)
  • 3. Analyzing Time Series data.srt (6.0 KB)
  • 5. Intuition behind ARIMA --part 1.srt (5.1 KB)
  • 10. Evaluating Time Series Model.srt (3.9 KB)
  • 1. Introduction to Business Problem & Dataset.srt (2.2 KB)
  • 2. Datasets & Resources.html (0.2 KB)
  • 9. Apply Auto-Arima on data.mp4 (82.3 MB)
  • 4. Data preparation for Time Series Forecasting.mp4 (66.8 MB)
  • 6. Intuition behind MA model --ARIMA part 2.mp4 (49.1 MB)
  • 3. Analyzing Time Series data.mp4 (39.5 MB)
  • 10. Evaluating Time Series Model.mp4 (28.3 MB)
  • 7. Intuition behind AR model -- ARIMA part 3.mp4 (27.1 MB)
  • 8. Intuition behind Integrating -- ARIMA part 4.mp4 (26.6 MB)
  • 1. Introduction to Business Problem & Dataset.mp4 (22.6 MB)
  • 5. Intuition behind ARIMA --part 1.mp4 (20.1 MB)
1. Introduction to this course
  • 1. Intro to this course.srt (4.2 KB)
  • 1. Intro to this course.mp4 (20.8 MB)

Description


Description

Are you looking to land a top-paying job in Data Science?
Or are you a seasoned AI practitioner who want to take your career to the next level?
Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence?

If the answer is yes to any of these questions, then this course is for you!

Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology.

The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.

1.Task #1 @Predict Price of Airlines Industry : Develop an AI model to predict Fare of Airlines at various Routes.

2.Task #2 @Predict the strength of a Password: Predict the category of Password whether it is Strong, Good or Weak.

3.Task #3 @Predict Prices of a Stock: Develop time series forecasting models to predict future Stock prices.

Why should you take this Course?

It explains Projects on real Data and real-world Problems. No toy data! This is the simplest & best way to become a Data Scientist/AI Engineer/ ML Engineer

It shows and explains the full real-world Data. Starting with importing messy data, cleaning data,merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning , NLP & Time Series and Data Presentation.
It gives you plenty of opportunities to practice and code on your own. Learning by doing.
In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion
Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.

Who this course is for:

One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis.

Requirements

Basic knowledge of programming is recommended. However, You can follow my Basics of Python Course which is free of cost therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science and directly apply these skills to solve real world challenging business problems.

Last Updated 1/2021



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Udemy - Data Science Real World Projects in Python

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2.4 GB
seeders:27
leechers:17
Udemy - Data Science Real World Projects in Python

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