23 OCT 2025 - We are back! If you have been following us over the last few years, you will know that the last 2 months have been rough. We website was practically not loading. Sorry for the mess. We are back though and everything should run smoothly now. New servers. Updated domains. And new owners. We invite you all to start uploading torrents again!
TORRENT DETAILS
[Tutorialsplanet.NET] Udemy - Beginning With Machine Learning & Data Science In Python
TORRENT SUMMARY
Status:
All the torrents in this section have been verified by our verification system
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.
VISITOR COMMENTS (0 )
FILE LIST
Filename
Size
1. Working with Machine Learning/1. Exploring Machine Learning and its Types.mp4
7.3 MB
1. Working with Machine Learning/1. Exploring Machine Learning and its Types.vtt
5.8 KB
1. Working with Machine Learning/2. Machine Learning Foundations.html
166 B
1. Working with Machine Learning/3. Install Anaconda.mp4
8.8 MB
1. Working with Machine Learning/3. Install Anaconda.vtt
5.6 KB
1. Working with Machine Learning/4. Python Versions.html
166 B
1. Working with Machine Learning/5. Python and Jupyter Demo.mp4
17.7 MB
1. Working with Machine Learning/5. Python and Jupyter Demo.vtt
9.2 KB
1. Working with Machine Learning/5.1 A quick tour of IPython Notebook.zip.zip
102.8 KB
1. Working with Machine Learning/6. Python Basics.html
166 B
2. Understanding Data Wrangling/1. Introduction.mp4
498.6 KB
2. Understanding Data Wrangling/1. Introduction.vtt
281 B
2. Understanding Data Wrangling/10. Summary.mp4
539.4 KB
2. Understanding Data Wrangling/10. Summary.vtt
396 B
2. Understanding Data Wrangling/2. Reading from a CSV.mp4
16.1 MB
2. Understanding Data Wrangling/2. Reading from a CSV.vtt
5.9 KB
2. Understanding Data Wrangling/2.1 Chapter 1 - Reading from a CSV.ipynb.zip.zip
395.7 KB
2. Understanding Data Wrangling/2.2 311-service-requests.zip.zip
8.3 MB
2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.mp4
25.1 MB
2. Understanding Data Wrangling/3. Selecting data and finding the most common complaint type.vtt
6.6 KB
2. Understanding Data Wrangling/3.1 Chapter 2 - Selecting data finding the most common complaint type.ipynb.zip.zip
38.8 KB
2. Understanding Data Wrangling/4. Which borough has the most noise complaints.mp4
19.5 MB
2. Understanding Data Wrangling/4. Which borough has the most noise complaints.vtt
6.2 KB
2. Understanding Data Wrangling/4.1 Chapter 3 - Which borough has the most noise complaints (or, more selecting data).ipynb.zip.zip
18.1 KB
2. Understanding Data Wrangling/5. Which weekday do people bike the most.mp4
17 MB
2. Understanding Data Wrangling/5. Which weekday do people bike the most.vtt
5.7 KB
2. Understanding Data Wrangling/5.1 bikes.csv.csv
13.5 KB
2. Understanding Data Wrangling/5.2 Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb.zip.zip
77.8 KB
2. Understanding Data Wrangling/6. Which month was the snowiest.mp4
20.4 MB
2. Understanding Data Wrangling/6. Which month was the snowiest.vtt
6.6 KB
2. Understanding Data Wrangling/6.1 Chapter 5 - String Operations- Which month was the snowiest.ipynb.zip.zip
78.4 KB
2. Understanding Data Wrangling/7. Cleaning Messy Data.mp4
32 MB
2. Understanding Data Wrangling/7. Cleaning Messy Data.vtt
9.4 KB
2. Understanding Data Wrangling/7.1 Chapter 6 - Cleaning up messy data.ipynb.zip.zip
11.2 KB
2. Understanding Data Wrangling/8. How to deal with timestamps.mp4
16.4 MB
2. Understanding Data Wrangling/8. How to deal with timestamps.vtt
4.4 KB
2. Understanding Data Wrangling/8.1 Chapter 7 - How to deal with timestamps.ipynb.zip.zip
4.4 KB
2. Understanding Data Wrangling/8.2 popularity-contest.tsv.tsv
185.2 KB
2. Understanding Data Wrangling/9. Loading data from SQL databases.mp4
13.4 MB
2. Understanding Data Wrangling/9. Loading data from SQL databases.vtt
7.4 KB
2. Understanding Data Wrangling/9.1 Chapter 8 - Loading data from SQL databases.ipynb.zip.zip
4.2 KB
2. Understanding Data Wrangling/9.2 weather_2012_sqlite.zip.zip
1.4 KB
2. Understanding Data Wrangling/9.3 weather_2012.csv.csv
492 KB
3. Linear Regression/1. Introduction.mp4
1.7 MB
3. Linear Regression/1. Introduction.vtt
1.2 KB
3. Linear Regression/10. Model evaluation.mp4
10.7 MB
3. Linear Regression/10. Model evaluation.vtt
4.8 KB
3. Linear Regression/11. Handling categorical features.mp4
19.8 MB
3. Linear Regression/11. Handling categorical features.vtt
8.5 KB
3. Linear Regression/12. Summary.mp4
5.5 MB
3. Linear Regression/12. Summary.vtt
2.8 KB
3. Linear Regression/2. What is linear regression.mp4
2.8 MB
3. Linear Regression/2. What is linear regression.vtt
1.7 KB
3. Linear Regression/3. The advertising dataset.mp4
7.1 MB
3. Linear Regression/3. The advertising dataset.vtt
3.1 KB
3. Linear Regression/3.1 linear regression.zip.zip
176.2 KB
3. Linear Regression/4. EDA questions on advertising data.mp4
4.7 MB
3. Linear Regression/4. EDA questions on advertising data.vtt
1.8 KB
3. Linear Regression/5. Simple Linear Regression.mp4
21.9 MB
3. Linear Regression/5. Simple Linear Regression.vtt
9.8 KB
3. Linear Regression/6. Hypothesis testing and p-values.mp4
7.8 MB
3. Linear Regression/6. Hypothesis testing and p-values.vtt
2.9 KB
3. Linear Regression/7. R squared.mp4
5.8 MB
3. Linear Regression/7. R squared.vtt
2.6 KB
3. Linear Regression/8. Multiple linear regression.mp4
15.3 MB
3. Linear Regression/8. Multiple linear regression.vtt
5.2 KB
3. Linear Regression/9. Model and feature selection.mp4
7.1 MB
3. Linear Regression/9. Model and feature selection.vtt
3.3 KB
4. Logistic Regression/1. Introduction.mp4
891.3 KB
4. Logistic Regression/1. Introduction.vtt
469 B
4. Logistic Regression/10. Summary.mp4
896.8 KB
4. Logistic Regression/10. Summary.vtt
371 B
4. Logistic Regression/2. Predicting a continuous response.mp4
11.6 MB
4. Logistic Regression/2. Predicting a continuous response.vtt
![[Tutorialsplanet.NET] Udemy - Beginning with Machine Learning & Data Science in Python](https://f.t0r.space/cover/23664538.webp)
