Selva Prabhakaran (ML+)

Learn everything related to AI - Machine Learning, Statistics, Python, Deep Learning, R programming language, Data Science and Data Engineering.
This channel will primarily concentrate on two areas:
(i) Know the foundational ML concepts and algorithms in detail. This covers the intuition, concept, theory and the math behind the algorithms. Make tough AI concepts simple and clear.
(ii) Apply the techniques in practice, via code walk through and approach a problem using multiple strategies and how to present your results.
People learn Data Science in bits and pieces with differing levels of quality. So the focus is to structure the lessons and cover the concepts in-depth.
If you are looking for a structured End-to-End approach to learn Data Science and AI from scratch, check out our complete university access offering at: edu.machinelearningplus.com/s/store

21 күн бұрын

Complete SQL course for Data Science - From Scratch

2 ай бұрын

Zero Inflated Regression - Teach your models to predict zeros | Hidden Gems of Data Science

2 ай бұрын

Poisson Distribution using Example | #27 in Statistics for Data Science

2 ай бұрын

Exercise: Binomial Distribution (solved) | #26 in Statistics for Data Science

2 ай бұрын

Understanding Binomial Distribution using Examples | #25 in Statistics for Data Science

2 ай бұрын

Bernoulli Distribution Clearly Explained | #24 in Statistics for Data Science

3 ай бұрын

Kernel Density Estimation | #23 in Statistics for Data Science

3 ай бұрын

Box Cox Transformation | #22 in Statistics for Data Science

3 ай бұрын

Skewness and Kurtosis | #16 in Statistics for Data Science

3 ай бұрын

Central Limit Theorem | #19 in Statistics for Data Science

3 ай бұрын

Z-Score and Standardization | #17 in Statistics for Data Science

3 ай бұрын

Proof that R Squared is Squared Pearson Correlation Coefficient | Hidden Gems of Data Science

3 ай бұрын

R Squared is Problematic Regression metric | Hidden Gems of Data Science

3 ай бұрын

CDF of Normal Distribution | #15 in Statistics for Data Science

3 ай бұрын

PDF of Normal Distribution | #14 in Statistics for Data Science

Пікірлер

@rksps17 күн бұрын

please remove the thumbnail feature at the end of your videos. what's the use of it if last 20 seconds content if we are not able to see it.

@rksps17 күн бұрын

please remove the thumbnail feature at the end of your videos. what's the use of it if last 20 seconds content if we are not able to see it.

@rksps17 күн бұрын

please remove the thumbnail feature at the end of your videos. what's the use of it if last 20 seconds content are not able to seen.

@rksps17 күн бұрын

please remove the thumbnail feature at the end of your videos. what's the use of it if last 20 seconds content are not able to seen.

@user-fk8wl4uk7b8 күн бұрын

jesus christ bless you

@user-fk8wl4uk7b8 күн бұрын

jesus christ bless you

@ramu150610 күн бұрын

Excellent and clear cut explanation...

@machinelearningplus10 күн бұрын

Thanks 👍

@kevon21710 күн бұрын

Great explanation!

@machinelearningplus9 күн бұрын

Thanks!

@user-tg2gm1ih9g17 күн бұрын

Yes, cython will let you interact with code libraries written in C. If you do 99% of your work in those C libraries, not in Python; you will get a good increase in speed. Of course you may need a lot of Python code to make sure your data is in a format compatible with those C libraries, and then more code to convert the C results back into Python format ... Why not simply program in C ? you can easily create dictionaries and sets -- these are just hash tables you can easily create tuples, linked lists or trees -- these are just just a "struct" maybe you feel that the python for i in range(n): ... is SO much cleaner than the C for (i=0; i<n; i++) {... maybe you can't endure life without this python loop: for fruit in fruits : ... maybe you simply can't comprehend this C code: startFruitLoop(); while (fruit = nextFruit()) { ... or maybe this C code is just morally offensive for (count=0; count < numFruits; count++) { fruit = fruits[i]; ... maybe you just H A T E all those braces "{}" and semicolons ";" Learn C ... for no other reason than: it looks good on your resume. But you may be surprised. C is a good programming language. If you ever need something to execute QUICKLY; C is a great choice.

@iaroslavd.91619 күн бұрын

useful tutorial. Thanks )

@MwendaNdungu20 күн бұрын

Could I get the DataSQL.csv file that you runned earlier in your class.?

@medhasu_Ai20 күн бұрын

❤ 😊

@aadhyatiwari968823 күн бұрын

One of the best explanations out here! Thankyou

@AB-cd5gd27 күн бұрын

Wow best tuto ever, would it work and actually provide improvement on front end part like tkinter?

@machinelearningplus25 күн бұрын

Yes

@biggriz24Ай бұрын

Very useful and clear explanations on these concepts, thank you

@gemini_537Ай бұрын

Gemini 1.5 Pro: This video is about how to convert Python code to Cython and achieve significant speed improvements. The video starts by explaining what Cython is and how it works. Cython is a language that allows you to write Python code with C-like syntax. This means that you can take advantage of the speed of C while still being able to write code in a more readable Python style. The video then goes through a step-by-step process of how to convert a Python function to Cython. The process involves creating a new file with a .pyx extension and pasting the Python code into it. Then, you need to use the `scython` library to compile the .pyx file into a C extension module. Once the C extension module is created, you can import it into your Python code and use the Cython function just like any other Python function. The video also shows how to improve the performance of your Cython code by using decorators. Decorators are a special type of function that can be used to modify the behavior of other functions. In Cython, there are a number of decorators that can be used to optimize code for speed. For example, the `@cython.nogil` decorator can be used to tell Cython that the function does not need to acquire the Python Global Interpreter Lock (GIL). This can improve the performance of the function by allowing it to run concurrently with other Python threads. Overall, this video is a great resource for anyone who wants to learn how to speed up their Python code using Cython. By following the steps outlined in the video, you can achieve significant performance improvements without having to rewrite your code in C. Here are the key points covered in the video: * Cython is a language that allows you to write Python code with C-like syntax. * Cython code can be significantly faster than pure Python code. * The process of converting Python code to Cython involves creating a .pyx file and compiling it into a C extension module. * Cython decorators can be used to further improve the performance of Cython code.

@rajithachevvala4720Ай бұрын

Please can you share the solution for challenge in comment section.