- Видео 109
- Просмотров 363 304
RegenerativeToday
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Добавлен 27 мар 2020
This channel is dedicated to learners who wants to learn Data Science and Data Analytics. Most of the videos will be about Data Science tutorial. Starting from Python Data Analysis libraries like Numpy, Pandas, Matplotlib snd Scikit-Learn. The plan is to eventually move to Machine learning and deep earning tutorials. We will also develop algorithm and problem solving skills together. Our plan is to create a group of Data Scientists and Data analysts to learn from each other and help each other grow. We hope to be interactive in this this channel through constructive comments. Please subscribe and stay tuned if you want to keep learning with us.
Human Brain on a Robot Body?
"Human Brain on a Robot Body?" explores the cutting-edge intersection of neuroscience and robotics. This intriguing concept delves into the future of integrating human cognitive functions with advanced robotic systems. Imagine a future where a human brain can control a robotic body, offering limitless possibilities for mobility, precision, and new forms of interaction. This video examines the latest scientific research and technological advancements. Discover how scientists and engineers are working to create a seamless interface between human thought and robotic action, and what this means for the future of human-robot collaboration. With expert insights and futuristic scenarios, this co...
Просмотров: 59
Видео
Principal Component Analysis in Python - Two Use Cases in Details
Просмотров 53День назад
"Principal Component Analysis in Python - Two Use Cases in Detail" offers a comprehensive guide to mastering PCA, a powerful dimensionality reduction technique. This video provides an in-depth exploration of PCA with practical Python implementations, showcasing two detailed use cases. First, we demonstrate PCA for enhancing data visualization by reducing complex datasets into 2D or 3D formats, ...
K Means Clustering in Python | How K Means Works | Find the Right K | Unsupervised Learning 2
Просмотров 131Месяц назад
"K Means Clustering in Python | How K Means Works | Find the Right K | Unsupervised Learning 2" is your ultimate guide to mastering K Means Clustering, a fundamental technique in unsupervised learning. In this video, we break down the K Means algorithm step-by-step, explaining how it partitions data into clusters based on similarity. Learn how to implement K Means in Python using libraries like...
What is Unsupervised Machine Learning | Unsupervised Learning 1
Просмотров 43Месяц назад
Unsupervised Learning is another big part of Machine Learning. In Unsupervised Learning, prediction is not the goal. The idea is to understand the data better and find a structure in a big dataset. Unsupervised Learning can be used to prepare data for Supervised Learning for a lot of time. #artificialintelligence #machinelearning #datascience #unsupervisedlearning
Do We Need to Learn Machine Learning / Deep Learning Anymore | Can AI Do the Model Now?
Просмотров 126Месяц назад
In an era where artificial intelligence rapidly evolves, many question if learning machine learning (ML) and deep learning (DL) are still necessary. With AI technologies advancing, automated tools now assist in model development, making it easier for non-experts to implement complex algorithms. However, understanding the fundamentals of ML and DL remains crucial for innovation and problem-solvi...
XGBoost Regressor in Python - sklearn
Просмотров 117Месяц назад
This is a complete tutorial on XGBoost regressor in Python - sklearn. Each step is explained in detail. The complete working code on XGBoost Classifier in sklearn is here: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/XGBoost_Regressor.ipynb The dataset used in this tutorial: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/Housing.csv The Offici...
XGBoost Classifier in Python - Multiple Disease Prediction
Просмотров 101Месяц назад
XGBoost or eXtreme Gradient Boosting is a very popular implementation of the Gradient Boosting algorithm. This tutorial shows some important aspects of it, parameters, and the implementation of a classification model in Python. The dataset used in this tutorial: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/multiple_disease_dataset.csv The complete code of this tutoria...
Ada Boost Classifier in Python
Просмотров 70Месяц назад
This is a complete tutorial on Ada Boost Classifier in Python's scikit-learn package. The dataset used in this tutorial: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/multiple_disease_dataset.csv The complete code of this tutorial: github.com/rashida048/Machine-Learning-Tutorials-Scikit-Learn/blob/main/AdaBoostClassifier.ipynb The video explains in detail how the Ada B...
Ada Boost Algorithm Clearly Explained
Просмотров 532 месяца назад
This video explains the Ada Boost machine learning algorithm. I took one simple dataset and showed all the calculations step by step very clearly. The prerequisite to understand this algorithm is to understand decision tree algorithm. Hopefully, after watching this video you will be able to visualize what goes on behind the scene of this machine learning algorithm even if you use a library or p...
Gradient Boosting Machine Classifier in Python
Просмотров 1142 месяца назад
Gradient Boosting Machine is a popular ensemble machine learning method. It uses a series of weak models sequentially to improve the model's performance based on the errors of the previous model. The idea is to find out if there is any pattern in the residuals or errors and use that to improve the model's performance further. In my last video, I explained the intuition behind the Gradient Boost...
Gradient Boosting Machine - Easy Explanation | Regression in Python
Просмотров 2072 месяца назад
Gradient Boosting Machine is an ensemble method in machine learning. It uses a series of weak models sequentially to improve the model accuracy based on the errors of the previous model. The idea is to find out if there is any pattern in the residuals or errors. In this tutorial, you will get the intuition on how the gradient boosting machine works with an example and then we will develop a reg...
K Nearest Neighbors Algorithm in Python | Classification | Regression | How to Choose the Right K
Просмотров 2703 месяца назад
K-Nearest Neighbor algorithm is one of the simplest and still popular machine learning models out there. If a simple model can do the job, I would never go for a harder one. Simply because simple models are easier to explain to the stakeholders. This tutorial will cover everything you need to know to use a KNN classifier and a KNN Regressor for your project. This tutorial starts with a overview...
Learning Rate Scheduler in Keras and TensorFlow -
Просмотров 1093 месяца назад
The Learning rate is arguably the most important hyperparameter in TensorFlow models. Keeping the learning rate the same throughout the model training may work but not always. If you notice any curve for losses in the training periods, it is normal for the model to learn really fast in the beginning and very slow at the end. So, models learn at different rates in the different stages of trainin...
Wide and Deep Learning in TensorFlow | Deep Learning Tutorial
Просмотров 1193 месяца назад
If used correctly wide and deep learning techniques in neural networks can make it very efficient. It provides you with the flexibility of passing some features directly to the output and passing some features through a deep neural network. Or, you can pass the same features to the output directly and through a neural network at the same time. The complete code for this tutorial will be found h...
Neural Network With Functional API in TensorFlow | Deep Learning
Просмотров 1484 месяца назад
Neural Network With Functional API in TensorFlow | Deep Learning
Saving And Loading TensorFlow Models - ModelCheckpoint Callback | Deep Learning With TensorFlow
Просмотров 2284 месяца назад
Saving And Loading TensorFlow Models - ModelCheckpoint Callback | Deep Learning With TensorFlow
Analyzing Deep Learning Models with TensorBoard | TensorFlow, Keras, and Python
Просмотров 2475 месяцев назад
Analyzing Deep Learning Models with TensorBoard | TensorFlow, Keras, and Python
Callbacks, Early Stopping, Live Loss Plotting | Deep Learning | Keras, TensorFlow, and Python
Просмотров 2335 месяцев назад
Callbacks, Early Stopping, Live Loss Plotting | Deep Learning | Keras, TensorFlow, and Python
Regression Using TensorFlow, Keras, and Python - Complete Step by Step Tutorial
Просмотров 2386 месяцев назад
Regression Using TensorFlow, Keras, and Python - Complete Step by Step Tutorial
Build a Neural Network with TensorFlow, Keras, and Python
Просмотров 7226 месяцев назад
Build a Neural Network with TensorFlow, Keras, and Python
Cost Functions For Classification Models - Machine Learning
Просмотров 3076 месяцев назад
Cost Functions For Classification Models - Machine Learning
Cost Function Options for Regression Models- Machine Learning and Deep Learning
Просмотров 1557 месяцев назад
Cost Function Options for Regression Models- Machine Learning and Deep Learning
Neural Networks Explained Clearly - Step By Step
Просмотров 3537 месяцев назад
Neural Networks Explained Clearly - Step By Step
Feature Selection in Python - Wrapper Method
Просмотров 2577 месяцев назад
Feature Selection in Python - Wrapper Method
Feature Selection in Python - Feature Importance
Просмотров 3268 месяцев назад
Feature Selection in Python - Feature Importance
Feature Selection in Python - SelectKBest
Просмотров 8198 месяцев назад
Feature Selection in Python - SelectKBest
Feature Selection in Python - Correlation Matrix
Просмотров 4898 месяцев назад
Feature Selection in Python - Correlation Matrix
Natural Language Processing | Twitter Sentiment Analysis in Python
Просмотров 3748 месяцев назад
Natural Language Processing | Twitter Sentiment Analysis in Python
Excellent Explanation and with great concept...Thank you very much👍
thank you very much this helped me a lot hopefully, I will get a good grade !! :)))
Great video. Please can you share the insurance data? It's not visible in the description. Thank you
Why did we use poly.fit, when we already used poly.fit_transform 08:37
could u plz provide the Dataset being used in the video
why did u choose degree 6?
That's just an estimate. degree is a hyperparameter here that you need to try different values to find the right one for you. Look at this video where you will find a method to tune the hyperparameter faster: ruclips.net/video/km71sruT9jE/видео.html
Thanks for the video. A question: is poly.fit(X_poly_train, y_train) necessary?
Audio is not clear and your voice is also not audible
hi, I'm not able to find your video on improving the R2 score. Can you show me the video? Thanks
You can watch this one that shows how to fine tune hyperparameters that should improve R2 score: ruclips.net/video/F13Wbfkpwlw/видео.html
Thank you! this is excellent
It is best video for new learners
Madam can you prefer me any job link regarding AI ML jobs to me
nice video, thanks for your effort ❤
Madam need kmeans clustering and hierarchical clustering
Coming next..Thank you!!
I enjoy your tutorials. Thank you
I think its the best video of Correlation Matrix and Heatmap in Python, i have seen many videos but you teaching skill is best . Keep upload the videos and spread the knowledge , we will support you.
Your videos are truly remarquable. I’ve been also amazed by your simple explanations and tutorials. Do you offer mentorship for people wanting to learn more about data science or online sessions as a service? If so it would be great to get more informations. Keep up the good work!
sister thank you. one request kindly make a list of your videos in chronological order so that we know which video to follow first.
Please check the playlist. Each playlists are almost organized. Here is the playlist for the Supervised Machine Learning: ruclips.net/video/Hs_03ALPgjk/видео.html
@@regenerativetoday4244 thanks
at last something hands on with real problems. I'm tired of maths abstract bullshit formulas....
thank you! how to know p_value and if the coefficient is significant or not?
Thank you...
wonderful way to simplify a diffcult topic to beginners. keep it up!
Take better mic or make any post processing bruh. Your audio quality ve not increased for 2 years
Mam you explained so good!! Mam how can I see the equation made by my Decision Tree?
Amazing Mam!! very Wonderfully explained!!
I gone through all the videos of machine learning in your RUclips channel madam but I need some guidance to get on job experience how it works
Do you have any specific question? If you want a job, you simply need to apply for jobs. If you can get some referral that helps.
@@regenerativetoday4244 I completed my btech graduation madam now I am practicing ai&ml in NIMSE institute Hyderabad
@@regenerativetoday4244 I need guidance for machine learning and deep learning notes
Madam I need guidance lines for AI&ML please help me
Thank you mam for such a wonderful learning!! I want to know further how can I improve my model accuracy with train score 0.75 and test score -1.12 ??
First is trying to tune hyperparameters, and also it is normal practice to try different models to find out which model works best for the dataset. Feel free to have a look at this video where you will find a technique for hyperparameter tuning: ruclips.net/video/km71sruT9jE/видео.html
@@regenerativetoday4244 Thank you so much you have explained it Amazingly and this video made me very happy! Thank you for this video all the rest!!
Request for more Machine Learning Videos in details.
Thank you Mam you taught me linear regression in very simple and expert way!! I saw many videos and you will be amazed to know that you are the BEST!!
I discovered your Pandas MultiIndex and XS while browsing through the RUclips. Very well explained and it really helped me to build these concepts.
Just an Brilliant Explanation Mam
Data isn't my background, but these videos help me understand how to structurally get there. Is there a way to export the predicted charges into a data population for further review. Also, is there a way to adjust the scatter plot dots by a filter on one of the independent variables (i.e. any record where age is 17, make the the plot red color). Thank you!
very detailed ...nice
how do input new input values and predict a value for them
Awesome as usual! You deserve way more recognition. As an HR professional, your videos help me apply ML into the HR field for classification problems and you have the best explanation for KNN. Thank you!
Thank you for the excellent post; what about other statistics like R-squared and correlation coefficient? Have you thought about the multivariate polynomial equation model? As you mentioned, training is overfitting but validation is very poor. Any suggestions are welcome.
If you are using this for a real world project, first try with different polynomial first and if you still do not get good results try other models. Usually for real world projects we try several different models with different parameters and finalize the best one.
@@regenerativetoday4244 Yes, I am using a real-world problem and trying to start with it before trying others.
@@regenerativetoday4244 Actually, I want to establish an empirical equation, as most of the other models are black boxes without equations.
Very well explained 🎉🎉 Thanks you so much 🎉🎉🎉
Thank you so much, u really explained everything so well
thank you so much
Very good video. About the model, dont you need to check if R-square need an adjust to trust his income?
There are a few different ways to check the model prediction. R-squared error is one of them. It is common for machine learning models to use mean squared error or mean absolute error as well.
Thanks for the amazing insights!
Please can you send me any link for case study using python polynomial regression (or multi polynomial) with data ? I want to practice.
Here it is: ruclips.net/video/nqNdBlA-j4w/видео.html
@RegnerativeToday Do you mind sharing your note book publicly so we learners can clone and play with it? Thank you in advance.
I added the notebook in the description box. Also here for you: github.com/rashida048/TensorFlow-Tutorial/blob/main/TensorFlow_NN.ipynb. Thank you!
How do we access the dataset used?
my last stop. Great job done.
goated thank you!
I really like you, can you guide me how to build a KNN model and choose its parameter K?
This channel is super under-rated
Its showing a error as "df isn't defined "