In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors — that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate
Below is the demo in English…
They hold there 2 conference in December 2020
GIDS.AI&ML/Data Live is an online training event covering the latest in big data, fast data, stream processing, NoSQL, Artificial Intelligence (AI), machine learning, deep learning, time series, sequential data, Natural Language Processing (NLP), chatbots, conversational UI, neural networks
2. PyCon Hyderabad 2020 https://pyconf.hydpy.org/2020/
Virtual Python Conference are being held in Hyderabad. This is very good conference, i have attend same in the last year. They had a good session on numpy, pandas, etc.,
If you cannot attend this conference, don’t worry subscribe to my channel I will create video on the updates of the conference. https://www.youtube.com/channel/UCtcEuGgCTWzzXBGvwCKm5VA
I created YouTube videos on Machine Learning using Jupyter notebook.
Hands on Machine Learning to program in jupyter notebook and easy to learn. This is my youtube channel for data science and Machine Learning for Beginners in English, हिंदी (Hindi) and తెలుగు (Telugu)
I have covered video tutorials in three languages, viz., English, Hindi and Telugu.
Following are playlist in English:
Following are playlist in Hindi:
This is Perceptron algorithm Demo in English, हिंदी (Hindi), తెలుగు(Telugu)
Perceptron set the foundations for Neural Network models in the 1980s. The algorithm was developed by Frank Rosenblatt and was encapsulated in the paper “Principles of Neuro-dynamics: Perceptrons and the Theory of Brain Mechanisms” published in 1962.
github Demo: https://github.com/kmeeraj/machinelearning/blob/develop/algorithms/Perceptron%20Gradient%20Descent.ipynb
github repository: https://github.com/kmeeraj/machinelearning/tree/develop
This Eigenfaces demo in English, हिंदी (Hindi) and తెలుగు (Telugu)
Demo on Eigen Values and Eigen Vectors (Hindi) — हिंदी: https://www.youtube.com/watch?v=ckI-o...
Demo on SVD (Hindi) — हिंदी: https://www.youtube.com/watch?v=qJ8Vq...
Demo on PCA (Hindi) — हिंदी: https://www.youtube.com/watch?v=aaarX...
Demo on Eigen Values and Eigen Vectors (Telugu) — తెలుగు: https://www.youtube.com/watch?v=N4TbU...
Demo on SVD (Telugu) — తెలుగు: https://www.youtube.com/watch?v=ckQUS...
Demo on PCA (Telugu) — తెలుగు: https://www.youtube.com/watch?v=oXFQ8...
I have attended GIDS.AI conference on 3rd december 2020. It was exciting conference and i could learn many things like research and development that is happening at Global scale. I am presenting the list of below session, with information that i could gather, hope it might be a help.
Brief description in English, हिंदी (Hindi), తెలుగు(Telugu)
Following are the sessions that are conducted:
Welcome to the session: Data Mesh: Beyond the Data Lake
Neal Ford is a director, software…
Maximum Likelihood Estimation (MLE) is a tool we use in machine learning to achieve a very common goal. The goal is to create a statistical model which can perform some task on yet unseen data.
The task might be classification, regression, or something else, so the nature of the task does not define MLE. The defining characteristic of MLE is that it uses only existing data to estimate parameters of the model. This is in contrast to approaches which exploit prior knowledge besides existing data.
We have samples x1,… xn, and assume that given they come under distribution, associated with…
Today we will discuss Eigen Decomposition. we know that A V is equal to lambda V, where lambda is eigen value, and v is eigen vector.
A is d x d matrix. There d eigen values λ1, λ2… λd and there are d eigen value v1, v2… vd.
When A matrix has certain structures where eigen values will start decreasing. we can write the above equations as
A [v1, v2…. vd] = [λ1v1, λ2v2… λdvd]
AV = VΛ
Where Λ is Diagonal matrix
A = VΛ(V inverse)
When, A is real and symmetric, Λ is orthogonal and V inverse becomes…
In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one.
Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression…