Udemy - Beginning With Machine Learning & Data Science In Python [TP]

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[Tutorialsplanet.NET] Udemy - Beginning with Machine Learning & Data Science in Python 1. Working with Machine Learning
  • 1. Exploring Machine Learning and its Types.mp4 (7.3 MB)
  • 1. Exploring Machine Learning and its Types.vtt (5.8 KB)
  • 2. Machine Learning Foundations.html (0.2 KB)
  • 3. Install Anaconda.mp4 (8.8 MB)
  • 3. Install Anaconda.vtt (5.6 KB)
  • 4. Python Versions.html (0.2 KB)
  • 5. Python and Jupyter Demo.mp4 (17.7 MB)
  • 5. Python and Jupyter Demo.vtt (9.2 KB)
  • 5.1 A quick tour of IPython Notebook.zip.zip (102.8 KB)
  • 6. Python Basics.html (0.2 KB)
2. Understanding Data Wrangling
  • 1. Introduction.mp4 (498.6 KB)
  • 1. Introduction.vtt (0.3 KB)
  • 10. Summary.mp4 (539.4 KB)
  • 10. Summary.vtt (0.4 KB)
  • 2. Reading from a CSV.mp4 (16.1 MB)
  • 2. Reading from a CSV.vtt (5.9 KB)
  • 2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip (395.7 KB)
  • 2.2 311-service-requests.zip.zip (8.3 MB)
  • 3. Selecting data and finding the most common complaint type.mp4 (25.1 MB)
  • 3. Selecting data and finding the most common complaint type.vtt (6.6 KB)
  • 3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip (38.8 KB)
  • 4. Which borough has the most noise complaints.mp4 (19.5 MB)
  • 4. Which borough has the most noise complaints.vtt (6.2 KB)
  • 4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip (18.1 KB)
  • 5. Which weekday do people bike the most.mp4 (17.0 MB)
  • 5. Which weekday do people bike the most.vtt (5.7 KB)
  • 5.1 bikes.csv.csv (13.5 KB)
  • 5.2 Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb.zip.zip (77.8 KB)
  • 6. Which month was the snowiest.mp4 (20.4 MB)
  • 6. Which month was the snowiest.vtt (6.6 KB)
  • 6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip (78.4 KB)
  • 7. Cleaning Messy Data.mp4 (32.0 MB)
  • 7. Cleaning Messy Data.vtt (9.4 KB)
  • 7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip (11.2 KB)
  • 8. How to deal with timestamps.mp4 (16.4 MB)
  • 8. How to deal with timestamps.vtt (4.4 KB)
  • 8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip (4.4 KB)
  • 8.2 popularity-contest.tsv.tsv (185.2 KB)
  • 9. Loading data from SQL databases.mp4 (13.4 MB)
  • 9. Loading data from SQL databases.vtt (7.4 KB)
  • 9.1 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip (4.2 KB)
  • 9.2 weather_2012_sqlite.zip.zip (1.4 KB)
  • 9.3 weather_2012.csv.csv (492.0 KB)
3. Linear Regression
  • 1. Introduction.mp4 (1.7 MB)
  • 1. Introduction.vtt (1.2 KB)
  • 10. Model evaluation.mp4 (10.7 MB)
  • 10. Model evaluation.vtt (4.8 KB)
  • 11. Handling categorical features.mp4 (19.8 MB)
  • 11. Handling categorical features.vtt (8.5 KB)
  • 12. Summary.mp4 (5.5 MB)
  • 12. Summary.vtt (2.8 KB)
  • 2. What is linear regression.mp4 (2.8 MB)
  • 2. What is linear regression.vtt (1.7 KB)
  • 3. The advertising dataset.mp4 (7.1 MB)
  • 3. The advertising dataset.vtt (3.1 KB)
  • 3.1 linear regression.zip.zip (176.2 KB)
  • 4. EDA questions on advertising data.mp4 (4.7 MB)
  • 4. EDA questions on advertising data.vtt (1.8 KB)
  • 5. Simple Linear Regression.mp4 (21.9 MB)
  • 5. Simple Linear Regression.vtt (9.8 KB)
  • 6. Hypothesis testing and p-values.mp4 (7.8 MB)
  • 6. Hypothesis testing and p-values.vtt (2.9 KB)
  • 7. R squared.mp4 (5.8 MB)
  • 7. R squared.vtt (2.6 KB)
  • 8. Multiple linear regression.mp4 (15.3 MB)
  • 8. Multiple linear regression.vtt (5.2 KB)
  • 9. Model and feature selection.mp4 (7.1 MB)
  • 9. Model and feature selection.vtt (3.3 KB)
4. Logistic Regression
  • 1. Introduction.mp4 (891.3 KB)
  • 1. Introduction.vtt (0.5 KB)
  • 10. Summary.mp4 (896.8 KB)
  • 10. Summary.vtt (0.4 KB)
  • 2. Predicting a continuous response.mp4 (11.6 MB)
  • 2. Predicting a continuous response.vtt (4.1 KB)
  • 2.1 logistic regression.zip.zip (1.3 MB)
  • 3. Quick refresher on linear regression.mp4 (4.9 MB)
  • 3. Quick refresher on linear regression.vtt (1.3 KB)
  • 4. Predicting a categorical response.mp4 (15.7 MB)
  • 4. Predicting a categorical response.vtt (5.8 KB)
  • 5. Using logistic regression.mp4 (11.4 MB)
  • 5. Using logistic regression.vtt (3.9 KB)
  • 6. Probability, odds, log-odds.mp4 (15.1 MB)
  • 6. Probability, odds, log-odds.vtt (5.5 KB)
  • 7. What is logistic regression.mp4 (10.9 MB)
  • 7. What is logistic regression.vtt (4.8 KB)
  • 8. Interpreting logistic regression.mp4 (16.3 MB)
  • 8. Interpreting logistic regression.vtt (6.3 KB)
  • 9. Using logistic regression with categorical features.mp4 (7.2 MB)
  • 9. Using logistic regression with categorical features.vtt (2.7 KB)
5. Cross Validation
  • 1. Introduction.mp4 (891.7 KB)
  • 1. Introduction.vtt (0.4 KB)
  • 2. Traintest split.mp4 (7.5 MB)
  • 2. Traintest split.vtt (3.6 KB)
  • 2.1 cross validation.zip.zip (23.8 KB)
  • 3. K-fold cross-validation.mp4 (8.0 MB)
  • 3. K-fold cross-validation.vtt (3.7 KB)
  • 4. Cross-validation continued.mp4 (15.9 MB)
  • 4. Cross-validation continued.vtt (7.0 KB)
  • 5. Summary.mp4 (4.9 MB)
  • 5. Summary.vtt (2.1 KB)
6. Regularization
  • 1. Introduction.mp4 (1.2 MB)
  • 1. Introduction.vtt (0.7 KB)
  • 2. Overfitting.mp4 (4.7 MB)
  • 2. Overfitting.vtt (2.4 KB)
  • 2.1 regularization.zip.zip (366.7 KB)
  • 3. Overfitting with linear models.mp4 (12.5 MB)
  • 3. Overfitting with linear models.vtt (

Description

Udemy - Beginning With Machine Learning & Data Science In Python [TP]



85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). Naturally, 85% of the interview questions comes from these topics as well.
This is a concise course created by UNP to focus on what matter most. This course will help you create a solid foundation of the essential topics of data science. With a solid foundation, you will be able to go a long way, understand any method easily, and create your own predictive analytics models.
At the end of this course, you will be able to:

Get your hands dirty by building machine learning models
Master logistic and linear regression, the workhorse of data science
Build your foundation for data science
Fast-paced course with all the basic & intermediate level concepts
Learn to manage data using standard tools like Pandas
This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.

Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. Concepts of over fitting, regularization etc. are discussed in details. These fundamental understandings are crucial as these can be applied to almost every machine learning methods.

This course also provide an understanding of the industry standards, best practices for formulating, applying and maintaining data driven solutions. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Next data wrangling and EDA with Pandas are discussed with hands on examples. Next linear and logistic regression is discussed in details and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next.

Final learning are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.



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Udemy - Beginning With Machine Learning & Data Science In Python [TP]

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Download torrent
542.8 MB
seeders:1
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Udemy - Beginning With Machine Learning & Data Science In Python [TP]

Torrent hash: D578B560D44F80A0AA8CE48784B508D41C255A70