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Indomitable Tech
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Добавлен 26 июн 2022
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V8/V8 Object Oriented Programming
This is the eighth video out of 9 videos in the series Python for Non-Technicals. In this video we discuss what is object oriented programming (OOP) and why to use object oriented programming. How object oriented programming makes our code modular and increases reusability of code. How to create a class in python and how to inherit class (class inheritance). How to create class attributes and class methods. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or write to me at shounak.python@gmail.com. I also have other playlists which deal with Machine Learning, Deep Learning, Data Visualization, API c...
Просмотров: 21
Видео
V7/V8 Reading from and Writing to Files
Просмотров 37Год назад
This is the seventh video out of 9 videos in the series Python for Non-Technicals. In this video we discuss how to read different types of files in python and how to write data to that file. We discuss text, csv, excel, json and pickle file formats. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or writ...
V6/V8 Python Functions
Просмотров 11Год назад
This is the sixth video out of 9 videos in the series Python for Non-Technicals. In this video we discuss What are functions in python. How functions help make code modular and increases code reusability. How to write functions with and without arguments which execute a certain block of code and which may or may not return a value. The notebook discussed is hosted on Github. Link is in descript...
V5/V8 Loops: while loop and for loop and list and dictionary comprehension
Просмотров 4Год назад
This is the fifth video out of 9 videos in the series Python for Non-Technicals. In this video we discuss What are loops and why to use them. How to use while loop. How to use for loop. We also discuss on how to use list and dictionary comprehension. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or wri...
V4/V8 Operators and Conditional Statements
Просмотров 6Год назад
This is the fourth video out of 9 videos in the series Python for Non-Technicals. In this video we discuss What are the different operators in python and how to use them. What are conditional statements and how to use them. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or write to me at shounak.python@...
V3/V8 Data Type Methods
Просмотров 16Год назад
This is the third video out of 9 videos in the series Python for Non-Technicals. In this video we discuss The handy methods related to data types. These are good-to-have skills when using python. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or write to me at shounak.python@gmail.com. I also have other...
V2/V8 Data Types, Variables, Constants & Typecasting
Просмотров 36Год назад
This is the second video out of 9 videos in the series Python for Non-Technicals. In this video we discuss What are data types and what are the different data types in python. What are variables and constants. What is typecasting in python. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or write to me a...
V1/V8 Install Anaconda, Python and Create Virtual Environment
Просмотров 27Год назад
This is the first video out of 9 videos in the series Python for Non-Technicals. In this video we discuss How to use python. How to install anaconda and use Jupyter notebook. How to install python from python.org. How to create virtual environment. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or write...
V0/V8 What is Python ? A short introduction to the playlist.
Просмотров 71Год назад
This is the intro video out of 9 videos in the series Python for Non-Technicals. In this video we discuss What is python? Why should you learn python? This is an introduction to the playlist and how this playlist is structured. The notebook discussed is hosted on Github. Link is in description. If you have any queries, please drop the queries in the comment section or write to me at shounak.pyt...
ML6: Clustering | Line by Line Machine Learning Code Implementation in Python
Просмотров 453Год назад
ML6: Clustering. This is the sixth video in the playlist: Comprehensive ML. This video deals with implementing end to end clustering technique in Jupyter Notebook using Python. We use K Means, DBSCAN, OPTICS and Affinity Propagation. We read the data, clean and preprocess the data before building and evaluating the data. Solved Notebook Link: github.com/shounak8/youtube_notebooks/blob/master/ma...
ML5: Multivariate Anomaly Detection | Line by Line Machine Learning Code Implementation in Python
Просмотров 656Год назад
ML5: Multivariate Anomaly Detection. This is the fifth video in the playlist: Comprehensive ML. This video deals with implementing end to end multivariate anomaly detection technique in Jupyter Notebook using Python. We use Isolation Forest, One Class SVM and Kernel Density. We read the data, clean and preprocess the data before building and evaluating the data. Solved Notebook Link: github.com...
ML4: Univariate Anomaly Detection (Machine Learning) | Line by Line Code Implementation in Python
Просмотров 550Год назад
ML4: Univariate Anomaly Detection (Machine Learning Methods). This is the fourth video in the playlist: Comprehensive ML. This video deals with implementing end to end univariate anomaly detection (machine learning methods) technique in Jupyter Notebook using Python. We use Isolation Forest, One Class SVM and Kernel Density. We read the data, clean and preprocess the data before building and ev...
ML3: Univariate Anomaly Detection (Statistical Methods) | Line by Line Code Implementation in Python
Просмотров 77Год назад
ML3: Univariate Anomaly Detection (Statistical Methods). This is the third video in the playlist: Comprehensive ML. This video deals with implementing end to end univariate anomaly detection (statistical methods) technique in Jupyter Notebook using Python. We use Standard Deviation , Z Score and Inter Quartile Range IQR. We read the data, clean and preprocess the data before building and evalua...
ML2: Classification | Line by Line Machine Learning Code Implementation in Python
Просмотров 17Год назад
ML2: Classification. This is the second video in the playlist: Comprehensive ML. This video deals with implementing end to end classification technique in Jupyter Notebook using Python. We use Logistic Regression, Random Forest Classifier (RFC), Support Vector Classifier (SVC) and K Nearest Neighbors (KNN) Classifier. We read the data, clean and preprocess the data before building and evaluatin...
ML1: Regression | Line by Line Code Implementation in Python
Просмотров 70Год назад
ML 1: Regression. This is the first video in the playlist Comprehensive ML and deals with implementing end to end regression technique in Jupyter Notebook using Python. We use Linear Regression, Random Forest Regressor and Support Vector Regressor. We read the data, clean and preprocess the data before building and evaluating the data. Solved Notebook Link: github.com/shounak8/youtube_notebooks...
Doom Eternal Level 5: Super Gore Nest (All Collectibles)
Просмотров 142 года назад
Doom Eternal Level 5: Super Gore Nest (All Collectibles)
Doom Eternal Level 3: Cultist Base (All Collectibles)
Просмотров 142 года назад
Doom Eternal Level 3: Cultist Base (All Collectibles)
Doom Eternal Level 4: Doom Hunter Base (All Collectibles)
Просмотров 152 года назад
Doom Eternal Level 4: Doom Hunter Base (All Collectibles)
Doom Eternal Level 2: Exultia (All Collectibles)
Просмотров 312 года назад
Doom Eternal Level 2: Exultia (All Collectibles)
Doom Eternal Level 1: Hell on Earth (All Collectibles)
Просмотров 122 года назад
Doom Eternal Level 1: Hell on Earth (All Collectibles)
SC2 - WOL 14: Engine of Destruction (Valhalla)
Просмотров 312 года назад
SC2 - WOL 14: Engine of Destruction (Valhalla)
SC2 - WOL 13: Cutthroat (Deadman's Port)
Просмотров 452 года назад
SC2 - WOL 13: Cutthroat (Deadman's Port)
SC2 - WOL 19: Maw of the Void (Sigma Quadrant)
Просмотров 382 года назад
SC2 - WOL 19: Maw of the Void (Sigma Quadrant)
SC2 - WOL 21_1: Belly of the Beast (Char)
Просмотров 182 года назад
SC2 - WOL 21_1: Belly of the Beast (Char)
SC2 - WOL 21_2: Shatter the Sky (Char)
Просмотров 232 года назад
SC2 - WOL 21_2: Shatter the Sky (Char)
hello Sir , i have an issue with optional file upload . When i am trying to test that endpoint without a file it never hits the else block rather it returns Value error, Expected UploadFile, received: <class 'str'> this is how my method looks like : @app.post('/file-upload-optional') async def upload_file(file: Optional[UploadFile] = File(None)): if file is None: return {"message": "No file uploaded"} else: return {"filename": file.filename} it would be great if you can provide some in sight here .
Thanks for your video! super helpful! I struggled for a while and am now getting a "no module named thinc" error. I tried installing thinc and it's deprecated. Any advice on how to update since your video? thank you!!
Super class Sir❤❤❤
Good
great video, thanks!
Very nicely done. Is it possible to show how you would perform hyper parameter tuning for this?
or only a single time of coding in Git Bash will do?
Hi did you found the answer? In my case, I'm using Anaconda, and my python.exe is located inside my environment. I don't understand where root should be located, because he's using directly the bash from git. In the kaggle's documentation for Windows it says that the file should be in c:\Users\{username}\.kaggle.
nevermind, I watched the video again, and I saw the path in the top of the bash window
do we need to rewrite the code in git bash when we open another new Jupyter file?
Thank you very much very easy explanation
Thanku it was very helpful...
Great video. Now I quite understand how it works. Do you mind if I ask you a question? I have a project using folium. Instead of the division of colors that have been determined according to the system, the user can enter the desired numbers then the map will be divided with the colors according to the number entered by the user. How do I do this?
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this sounds little dumb... but is there any reason you used conv1D for also image classification? isnt adding one more conv2d layer more useful for image classificatino training?
Please also make some tutorials on one hot encoding for image dataset
Hi Bipasha, one hot encoding is used for converting categorical values to binary independent (0 or 1) column values. I have not yet seen any example where it is used in image datasets.
Oh.... Thankyou
🔥🔥🔥
I'll to thank you, keep going plz !
How can I set those Exceptions globally?
Hi ! Can you pls elaborate the question because I did not completely understand the question. If I am understanding your question correctly, you can create functions/methods which return the error message and raise exception.