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Super Data Science
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Добавлен 27 дек 2022
Welcome to the Super Data Science RUclips channel!
We focus on teaching foundational concepts of machine learning and data science in a way that is accessible and engaging for learners of all levels.
Our lessons cover many topics, including linear regression, logistic regression, and other classification algorithms, clustering, neural networks, deep learning, and even artificial intelligence.
We believe that having a solid foundation in these concepts is essential for anyone looking to get started with machine learning and data science. We aim to help you build that foundation through clear explanations and practical examples.
So if you’re interested in learning about machine learning and data science, subscribe and join us on this journey to becoming an expert in these exciting fields!
We focus on teaching foundational concepts of machine learning and data science in a way that is accessible and engaging for learners of all levels.
Our lessons cover many topics, including linear regression, logistic regression, and other classification algorithms, clustering, neural networks, deep learning, and even artificial intelligence.
We believe that having a solid foundation in these concepts is essential for anyone looking to get started with machine learning and data science. We aim to help you build that foundation through clear explanations and practical examples.
So if you’re interested in learning about machine learning and data science, subscribe and join us on this journey to becoming an expert in these exciting fields!
AI & The Neuron in Deep Learning: How Artificial Neurons Mimic the Human Brain
Welcome to this deep dive into the world of neurons, the fundamental building blocks of artificial neural networks. In this video, we explore how neurons in AI mimic the human brain, powering the incredible capabilities of deep learning models. Discover the structure of a neuron, how signals are transmitted through synapses, and the crucial role of activation functions in machine learning. Whether you're new to AI or looking to deepen your understanding, this video will provide you with valuable insights into how neurons work in artificial intelligence. Join us on this fascinating journey and unlock the secrets of AI learning!
Course Link HERE: community.superdatascience.com/c/dl-az
You can...
Course Link HERE: community.superdatascience.com/c/dl-az
You can...
Просмотров: 16
Видео
From Non-Linear to Linear: Mastering Dimensional Mapping using Kernel SVM (Support Vector Machine)
Просмотров 334 часа назад
In this tutorial, we dive deep into the world of machine learning and explore the Kernel Support Vector Machine (Kernel SVM) Trick, and learn how to handle nonlinearly separable datasets by mapping them to higher dimensions using the Support Vector Machine (SVM) algorithm. We start with a simple one-dimensional example and extend the concept to two-dimensional and three-dimensional spaces. You'...
Avoid These Clustering Mistakes with K-Means++!
Просмотров 619 часов назад
Are you struggling with inconsistent clustering results in your machine learning projects? In this comprehensive tutorial, we delve into the K-Means algorithm, a powerful enhancement of the traditional K-Means clustering method. This tutorial will help you overcome one of the biggest challenges in clustering-random initialization-by leveraging the K-Means technique to achieve more reliable, sta...
False Positives & False Negatives
Просмотров 598Год назад
False Positives & False Negatives Welcome to this video on False Positives and False Negatives in machine learning, two critical concepts in evaluating the performance of a model. False Positives and False Negatives are errors that occur when a model predicts a positive or negative outcome incorrectly. Find out more here! Course Link HERE: sds.courses/machine-learning-az You can find us also he...
Eclat Algorithm Association Rule Learning
Просмотров 1,6 тыс.Год назад
Eclat algorithm Association Rule Learning Welcome to this video on the Eclat Algorithm, a powerful tool in the field of Association Rule Learning. Eclat stands for "Equivalence Class Clustering and bottom-up Lattice Traversal" and is used to identify frequent itemsets from a given dataset. This algorithm is widely used in market basket analysis, customer segmentation, and recommendation systems...
The Multi Armed Bandit Problem
Просмотров 1,6 тыс.Год назад
The Multi Armed Bandit Problem Welcome to this video on Multi-Armed Bandit problem. The Multi Armed Bandit is a popular framework in the field of machine learning. It has many practical applications, from online advertising to clinical trials and beyond. In this video, we'll explore the basics of Multi-Armed Bandit and how it can be applied to solve real-world problems. Find out more in this vi...
The Machine Learning Process
Просмотров 265Год назад
The Machine Learning Process Welcome to this video on The Machine Learning Process! Looking to learn about the process that can be replicated with most projects related to Machine Learning? This involves Data Preprocessing, Modeling, and Evaluation. Find out more in this video on the core elements of the Machine Learning Process in this video! Course Link HERE: sds.courses/python-ml-level-1 You...
Decision Tree Classification
Просмотров 362Год назад
Decision Tree Classification Looking to learn more about a popular algorithm in Machine Learning? Explore information about Decision Tree Classification in this video. Decision Tree Classification works by recursively splitting the data into smaller subsets based on the most significant feature until a stopping criterion is met. Find out more here! Course Link HERE: sds.courses/machine-learning...
Types of Natural Language Processing NLP
Просмотров 575Год назад
Types of Natural Language Processing NLP Looking to find out more information about the types of Natural Language Processing? Natural Language Processing is a key area to understand for Machine Learning and Data Science. Find out more in this video! Course Link HERE: sds.courses/machine-learning-az You can find us also here: Website: www.superdatascience.com/ Facebook: groups/super...
Upper Confidence Bound UCB Algorithm
Просмотров 4,5 тыс.Год назад
Upper Confidence Bound UCB Algorithm Welcome to this video about the Upper Confidence Bound algorithm! The UCB (Upper Confidence Bound) algorithm is a commonly used algorithm in machine learning, and it works by estimating the mean reward of each arm and choosing the arm with the highest upper confidence bound based on the estimated variance. Looking to find out more with easy-to-understand con...
Accuracy Paradox
Просмотров 362Год назад
Accuracy Paradox Looking to learn more about the Accuracy Paradox? The paradox highlights the importance of understanding the distribution of data and not relying solely on overall accuracy metrics to evaluate model performance. Want to understand it, broken down into simple concepts? Check out this video! Course Link HERE: sds.courses/machine-learning-az You can find us also here: Website: www...
Naive Bayes Intuition Part 3
Просмотров 104Год назад
Naive Bayes Intuition Part 3 Welcome to the final part of our 3-part Naive Bayes series! Looking to find out more about this powerful algorithm used in Machine Learning and Data Science? Find out more in this video, or check out parts 1 & 2 first! Course Link HERE: sds.courses/machine-learning-az You can find us also here: Website: www.superdatascience.com/ Facebook: groups/superda...
Types of Kernel Functions
Просмотров 995Год назад
Types of Kernel Functions Looking to learn more about the Types of Kernel Functions in ML? Kernel functions are an esential component of many machine learning algorithms, including support vector machines (SVM) and kernel principal component analysis (KPCA). They are used to transform input data into a higher-dimensional feature space to make it easier to classify. Find out more in this video! ...
K Nearest Neighbour Algorithm - What is K-NN?
Просмотров 237Год назад
K Nearest Neighbour Algorithm K-NN Looking to learn more about one of the most popular ML models? The K-NN algorithm is used for classification and regression tasks. It works by finding the k closest data points to a new input and predicting its label based on the majority class or average value of its neighbors. The K-NN algorithm is easy to implement and can be used for a wide range of applic...
K-Means Clustering
Просмотров 253Год назад
K-Means Clustering Have you heard of K-Means Clustering? K-Means Clustering is a popular unsupervised Machine Learning technique used to group similar data points together into clusters. The algorithm works by iteratively assigning data points to the nearest cluster centroid and updating the centroid based on the new cluster members. Find out more in this video! Course Link HERE: sds.courses/py...
Evaluating Regression Model Performance
Просмотров 492Год назад
Evaluating Regression Model Performance
Bag of Words Model for Natural Language Processing
Просмотров 391Год назад
Bag of Words Model for Natural Language Processing
Dendograms for Hierarchical Clustering Part 2
Просмотров 165Год назад
Dendograms for Hierarchical Clustering Part 2
Splitting Data Into a Training Set And Test Set
Просмотров 190Год назад
Splitting Data Into a Training Set And Test Set
Dendograms for Hierarchical Clustering Part 1
Просмотров 257Год назад
Dendograms for Hierarchical Clustering Part 1
Kernel Support Vector Machine - What is Kernel SVM?
Просмотров 500Год назад
Kernel Support Vector Machine - What is Kernel SVM?
Classical vs Deep Learning Models for Natural Language Processing
Просмотров 336Год назад
Classical vs Deep Learning Models for Natural Language Processing
Luv it..... So well summarized...
loved the smoothness and clarity in which it was all explained! thanks a lot for this videos.
That was an amazing and clear explanation, especially for non-technical learners. Thank you so much
I am not able to Understand Adjusted RSquare, can someone help.
P(Defect|Mach1) = 0.83%
thanks
Simply explained, Thanks !
The clearest explanation of the OLS regression I heard. Thanks a lot!
you really dont need to tell ChatGPT "Could you please" lol it's a machine with no feelings and no character. As of YET! 😂
lol
thank you soo much , started following you from now on !
makes so much sense, ty
so far so good
Thanks for the explanation. I found your simple yet powerful explanation very useful. Is it possible for you to add me to your mailing list?
INFORMATIVE & ENJOYABLE video - you've earned yet another subscriber ! ONE COMMENT (a correction you may wish to make at your earliest opportunity, since this has been out a year): GIVEN your intro set of 30 (THIRTY) data points, your "Elbow Method" Graph (timestamp 3:26) couldn't possibly end up with WCSS = 0 at only 10 (TEN) clusters :-O INSTEAD the "Zero WCSS Point" on the x-axis would be for precisely 30 clusters, each with 1 data point which by definition of WCSS has a value of 0 (ZERO). You explain this VERBALLY quite nicely, but I think your GRAPH then contradicts what you've explained, because at Clusters = 10 there would be approximately 3 data points in each cluster (on average) hence a NON-ZERO WCSS for each cluster. I look forward to watching more of your videos - you're an EXCELLENT presenter !! - Mark Vogt, Principal Solution Architect/Data Scientist - Avanade (avanade.com)
I just have one question regarding the dataset "Ads_CTR_Optimisation". I am not able to understand why more than 1 ad is selected per round in some cases.
what do you mean by variable? a variable, the variable? or just numbers, or observations? So many fkin terminology, words, words! Anyway, so you have the A (Y axis) and B (X axis) variables.By adding a variable you mean adding C, so multinomial? If I have 2 variables, adjusted R2 should be the same as normal one? Its not.
This is awesome! I am happy that I stumbled upon your video! Thank you!
Greater R^2 values are NOT necessarily better
Can you provide any resources of how to apply Thompson Sampling to the Taxi problem in OpenAI Gym? I would like to compare its performance to the epsilon-greedy.
Super
Video starts at 2:45
Great!
well done, explained very clear!
i don't understand why elbow method is optimal for dbscan and k-mean ? Someone can explain this. Thank you!
Wooow woooow..this is excellent..loved every bit of the video, especially on code generation..I am blessed to be alive today in this era of learning and knowledge transfer...hahaha..#LifeIsGood
I just started watching this video, I'll let you guys know when i finished it
It was great , thanks !
So ordinary least squares is used to fit the linear regression. It defines the best line as the line that minimises the sum of the residuals squared. Would I be accurate for comparing OLS to Gradient Descent? As to say that when fitting a linear regression I am free to choose between OLS and Gradient Descent as two different optimisation algorithms?
This course is amazing!
Rahmat, I am from UZB
Great job explaining this concepts in a very practical yet detailed way!
Thanks
this is the worst explanation of DTR, YOU DONT KNOW HOW TO CLEARLY EXPLAIN IT
Very brief, very accurate and nicely explained. Thanks!
Hey great video.. can you also put in the link or descriptions of other models that you have discussed before .and going to discuss in future.. so it will be easy to find them, learn about it and compare them so one can select the one that would be suitable for his or her case of study.. 😀
Thanks a lot ! Nice video！
The example of the wild guess game is very helpful for me to understand the concept. Thank you!
Nice work! You deserve more than the current amount of views. Just watched statquests video which claimed that R^2 could not be less than 0 which is super wrong. Your work is right and clear for R square.
Very useful, Thanks for sharing tips.
80% of this video is devoted to coding. Not really relevant for me or most of the people I work with.
Exceptionally well explained! Thank you very much!
Noted that you wrote 'Could you please explain... instead of just 'Explain...' I assume being polite doesn't effect the quality of the answer. Or, scary thought, does it?