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Sane's Academy of Artificial Intelligence
India
เข้าร่วมเมื่อ 11 ม.ค. 2015
This Channel is run by Sanjay Sane, owner of Sane's Academy of A.I. and Statistics
Column Transformers | Python | Sci-kit Learn | Data Pre-Processing
This video demonstrates the use of column transformers in sci-kit learn library of Python using make_column_transformer function. This can be used for data pre-processing.
Dataset link: github.com/sanjayssane/Machine-Learning/commit/595d4a2014e3d1e86b6f263abd333a6aa2745a7c
Code Link: github.com/sanjayssane/Machine-Learning/commit/61f069b28c98aa3e86baa2067601075b1a22dfe2
Dataset link: github.com/sanjayssane/Machine-Learning/commit/595d4a2014e3d1e86b6f263abd333a6aa2745a7c
Code Link: github.com/sanjayssane/Machine-Learning/commit/61f069b28c98aa3e86baa2067601075b1a22dfe2
มุมมอง: 84
วีดีโอ
Inferencing Interface on ML Model | streamlit App
มุมมอง 1598 หลายเดือนก่อน
This video demonstrates how to create an interface for inferencing on any Machine Learning Model using streamlit app. Data set Link: github.com/sanjayssane/Machine-Learning/blob/master/Concrete_Data.csv Links of two files used: best_model.py: github.com/sanjayssane/Machine-Learning/blob/master/best_model.py infer1.py: github.com/sanjayssane/Machine-Learning/blob/master/infer1.py
Scatter Plot with Streamlit | Interactive Graphics
มุมมอง 72611 หลายเดือนก่อน
This video demonstrates how to generate scatter plot with Python package streamlit. For understanding the contents of this video, the audience needs to be aware of matplotlib and seaborn libraries of Python. Code: github.com/sanjayssane/Machine-Learning/blob/master/scatter_plot1.py Dataset: github.com/sanjayssane/Machine-Learning/blob/master/Cars93.csv Streamlit Package Documentation: docs.stre...
Stack Ensembling - Part III | Grid Searching with Stacking | Sci-kit Learn
มุมมอง 67ปีที่แล้ว
This video is the third one in the series of video on Stack Ensembling. Here is the list of all the videos: Part 1: th-cam.com/video/PB_dJEs-i-s/w-d-xo.html Part 2: th-cam.com/video/knz3MCWb8_k/w-d-xo.html Part 3: th-cam.com/video/uDI5RC3vQA8/w-d-xo.html Code : github.com/sanjayssane/Machine-Learning/blob/master/Stack_Ensembler3.ipynb Dataset: github.com/sanjayssane/Machine-Learning/blob/master...
Stack Ensembling - Part II | Pass-Through option | Scikit-Learn
มุมมอง 102ปีที่แล้ว
This video is the second one of a series of three videos on model stacking using scikit-learn options. Code: github.com/sanjayssane/Machine-Learning/blob/master/Stack_Ensembler2.ipynb Dataset: github.com/sanjayssane/Machine-Learning/blob/master/BreastCancer.csv Here is the list of all the videos: Part 1: th-cam.com/video/PB_dJEs-i-s/w-d-xo.html Part 2: th-cam.com/video/knz3MCWb8_k/w-d-xo.html P...
Stack Ensembling - Part I | Sci-kit Learn
มุมมอง 110ปีที่แล้ว
This video is the first one of a series of three videos on model stacking using scikit-learn options. Code: github.com/sanjayssane/Machine-Learning/blob/master/Stack_Ensembler1.ipynb Dataset: github.com/sanjayssane/Machine-Learning/blob/master/BreastCancer.csv Here is the list of all the videos: Part 1: th-cam.com/video/PB_dJEs-i-s/w-d-xo.html Part 2: th-cam.com/video/knz3MCWb8_k/w-d-xo.html Pa...
Closer Look at K-Fold CV | Deeper Understanding of functions cross_val_predict & cross_val_score
มุมมอง 328ปีที่แล้ว
This video demonstrates how the sklearn functions cross_val_predict and cross_val_score work internally. Code link: github.com/sanjayssane/Machine-Learning/blob/master/cv_predict.ipynb Dataset link: github.com/sanjayssane/Machine-Learning/blob/master/pizza.csv
Linear Optimization with Python (PuLP) | Linear Programming Problem(LPP)
มุมมอง 8Kปีที่แล้ว
This video demonstrates the usage of Python package PuLP with Linear Programming Problem (LPP). You can also watch the video related to Excel option link of which is given below Link of Excel Option: th-cam.com/video/AUhFvjqOU1U/w-d-xo.html The problem statement can be accessed at the link : github.com/sanjayssane/MicrosoftExcelTips/blob/main/OPT_EXCEL.pdf Link of Code: github.com/sanjayssane/M...
Pipeline in GridSearchCV | Scikit-Learn
มุมมอง 531ปีที่แล้ว
This video demonstrates the usage of Pipeline syntax in GridSearchCV (scikit-learn). Pre-requisite video link (Pipelines): th-cam.com/video/bjWhQhfUJPU/w-d-xo.html Grid Search video link: th-cam.com/video/YUK7OGvwlVc/w-d-xo.html Code link: github.com/sanjayssane/Machine-Learning/blob/master/pipe_gs_split.py Dataset Link: github.com/sanjayssane/Machine-Learning/blob/master/Kyphosis.csv
Importance of Pipelines | Sci-Kit Learn | Python
มุมมอง 100ปีที่แล้ว
This video demonstrates the usage of Pipeline syntax in sci-kit learn library of Python. Code link: github.com/sanjayssane/Machine-Learning/blob/master/pipe_tt_split.py Dataset link: github.com/sanjayssane/Machine-Learning/blob/master/Kyphosis.csv
Hyper-parameter Tuning | GridSearchCV | Sci-Kit Learn
มุมมอง 153ปีที่แล้ว
This video demonstrates the hyperparameter tuning with GridSearchCV function from sci-kit learn package of Python. If you want to watch the video related to K-Fold CV please watch it on th-cam.com/video/TETgQisBELw/w-d-xo.html Code link: github.com/sanjayssane/Machine-Learning/blob/master/gscv.py Dataset link: github.com/sanjayssane/Machine-Learning/blob/master/Kyphosis.csv
Stratification | Why to Stratify? | stratify=y option
มุมมอง 237ปีที่แล้ว
This video demonstrates the usage of the option "stratify=y" in the train_test_split function in sckit-learn library of Python and also explains as to why the stratification is necessary. Link to the code: github.com/sanjayssane/Machine-Learning/blob/master/strat.py Link to the dataset: github.com/sanjayssane/Machine-Learning/blob/master/BreastCancer.csv
K-Fold Cross-Validation | cross_val_score
มุมมอง 395ปีที่แล้ว
This video demonstrates the method of K-Fold cross-validation along-with the scikit-learn function cross_val_score. Code Link: github.com/sanjayssane/Machine-Learning/blob/master/KFold_svm.py Data Link: github.com/sanjayssane/Machine-Learning/blob/master/Kyphosis.csv
Multi-Output Perceptron with TensorFlow
มุมมอง 380ปีที่แล้ว
This video demonstrates how to code for multiple output problem using TensorFlow with Functional API. Notebook link: github.com/sanjayssane/Machine-Learning/blob/master/Energy_2_output_model.ipynb
Non-Sequential Neural Network with TensorFlow | Functional API
มุมมอง 385ปีที่แล้ว
This video demonstrates the usage of Model Function in Functional API of TensorFlow Python library for building simple non-sequential neural networks. Notebook Link: github.com/sanjayssane/Machine-Learning/blob/9683b459b3b3168b131d376aa06cca7ec7e9d412/FunctionalAPI.ipynb Dataset Link: github.com/sanjayssane/Machine-Learning/blob/master/BreastCancer.csv
Simple Training Custom Loop in TensorFlow | Linear Regression
มุมมอง 188ปีที่แล้ว
Simple Training Custom Loop in TensorFlow | Linear Regression
Reshaping the Data in R | Making the data Longer and Wider in R | Package tidyr
มุมมอง 7502 ปีที่แล้ว
Reshaping the Data in R | Making the data Longer and Wider in R | Package tidyr
Clinical Reporting using R | Package r2rtf
มุมมอง 4.7K2 ปีที่แล้ว
Clinical Reporting using R | Package r2rtf
Simple Graphics with Shiny | Interactive Histogram | Interactive Boxplot
มุมมอง 1.2K2 ปีที่แล้ว
Simple Graphics with Shiny | Interactive Histogram | Interactive Boxplot
Presenting any ML Model with Interactivity | ipwidgets | ML Project Presentation
มุมมอง 5482 ปีที่แล้ว
Presenting any ML Model with Interactivity | ipwidgets | ML Project Presentation
Linear Optimization in Excel with Solver Add-in | LPP in Excel
มุมมอง 39K2 ปีที่แล้ว
Linear Optimization in Excel with Solver Add-in | LPP in Excel
Interact Function in ipwidgets | Simple Interactive Graphics with Python
มุมมอง 1622 ปีที่แล้ว
Interact Function in ipwidgets | Simple Interactive Graphics with Python
Simple What-If Tool with Jupyter Widgets | ipwidgets
มุมมอง 6822 ปีที่แล้ว
Simple What-If Tool with Jupyter Widgets | ipwidgets
Precision Score | F1-Score | Model Evaluation
มุมมอง 832 ปีที่แล้ว
Precision Score | F1-Score | Model Evaluation
Thank you!
Thanks for your video. Please help. How to find MAXy for ax1+bx2+cx3+dx4=y ?
Congratulations sir!
How can I contact you!
You can email me to ssane@saneaiacad.in
Thank you for this video. Needed it very much.
Glad it was helpful!
Is this question 4 decision variable and 4 constraints
Dziękuję za pomoc, pozdrawiam serdecznie, życzę miłego dnia.
Welcome Bro!
kocham pana
tylko trochę za wolno
Dobra robota przyjacielu
Thank you. How can we insert space between group of records while creating rtf file? And also I'm not getting footnote for every page, for last page only it is populating. Is there any option to get a footnote for every page?
You are God Send man, you just helped me a lot!
Thank you Sir for valuable content
Excellent! Your way of teaching is amazing, so that after understanding from you, it looks very simple! :) Thanks for being there.
How did you randomly input the figures under EXECUTIVE, STUDENT, and OFFICE(the figures above the profit per unit row)
You can input any values in those cells manually, just to test your formulas related to sumproduct for your answer. But please mind it well that irrespective of what values you enter, your optimal answer isn't going to change when you are going to click the 'solve' button.
super :)
*Promo sm* 💔
Sorry, I didn't understand your feedback. Can you please explain?
7:35, the formula for pooled variance is the numerator divided by n1+n2-2
Yes, I missed it. Thanks for ponting. Also thanks for interest fully watching my video
Great video, very helpful. Thank you!
Glad it was helpful!
Very nice video with good example
Thank you! Clear and concise
Welcome
sir what could be test cases if I take this for automation? what should be my approach what should be my automation's end goal?
Automation's end goal should be a solution generation and also operationalizing it by the costs and profits calculated. The calculated figures can be stored and presented on dashboard
Interesting! 👌👌👍
Thanks for the video. HOw can we read the rtf file and extract tables out of it?
This cannot be done in so straight forward way.
THANK YOU FOR EXPLAINING THE PART THAT HOW TO ADD THE SOLVER ICON IN THE EXCEL MENO AS MINE DID NOT HAVE THIS ICON.
Thank you so much sir. This video is helped me a lot.
Glad it helped
Mr. Sanjay Sane. Do you have Knime PY scripting to run FB Prophet time series predictor? I am asking this because I cannot find any node nor component in Knime public node & component repository. Look forward to receive your feedback. Thank you Sir!
Sorry, I haven't explored FB prophet time series till now.
Thank you sir 😊
Amazing Video Sir The way of teaching is really awesome
Thank You
Excelente video!
Thanks
Excellent video. Explained in very simple language
Thanks for the feedback
Sir..i m attending CDAC's AI course now .The way you explain ML algorithm step by step before coding is excellent. especially the concepts of bagging boosting and Stacking...Expecting more video like this...I m recommending your channel to my colleagues too.
thanks sir, this helped me with my school project
You are most welcome
Interesting!
Kudos. Very well explained. Seems like building a smallish neural network might be the next step.
Yes
Is there a way to do this in Knime?
Not figured out, yet
Hi Sir, i have a doubt, how to create a row gap if there are multiple sections in the report.
As of now there is no option in r2rtf as it is in PROC REPORT's COMPUTE block.
Hi Sir, this is a very helpful video about r2rtf. I understood this and tried to generate the rtf, it worked. thanks a lot for taking time to share your knowledge. The only thing i noticed in the video is that for the rtf_colheader() for bottom border, in the video within the code it is not used, in the second rtf_colheader() i suppose we need to use the border_bottom('single',3).
Thanks for your feedback. I will surely look into the details of the border related options suggested by you.
Nice.
Thank.
Welcome
Really very useful video sir...keep doing such videos...
Thank you, I will
Thank you sir for this wonderful content!
My pleasure!
Interesting!
Very clearly explained! 👍
Interesting concept in Machine Learning!
Simple and clear presentation Sane sir 🙏
Great initiative sir!
Hi Sir. I was a student in your evening class, and I learned ML algorithms from you in R. It's been ages since we met but I have cherished those lectures ever since I was your student, I use that knowledge every day in my job in fact ! Thanks a lot for making this channel!
Keep up the great work. Industry 4.0 would be a big game changer in coming 2-3 years and your videos on AI/ML will be great start point to learn and hone the skills better in those topics.
Yes. Videos are informative and your these efforts are encouraging sir! Indeed they are inspiring. ☺️