Data Science & Machine Learning
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Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 77 633 obunachidan iborat bo'lib, TaΚΌlim toifasida 2 017-o'rinni va Hindiston mintaqasida 3 821-o'rinni egallagan.
π Auditoriya koβrsatkichlari va dinamika
Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβsib, 77 633 obunachiga ega boβldi.
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- Tematik yoβnalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
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βJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataβ
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77 633
Obunachilar
-2024 soatlar
-957 kun
-14230 kun
Postlar arxiv
πThe Ultimate Guide to the Pandas Library for Data Science in Python
ππ
https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/
A Visual Intro to NumPy and Data Representation
.
Link : ππ
https://jalammar.github.io/visual-numpy/
Matplotlib Cheatsheet ππ
https://github.com/rougier/matplotlib-cheatsheet
SQL Cheatsheet ππ
https://websitesetup.org/sql-cheat-sheet/
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Start working on any project if you are a beginner and want to grow your career as a data scientist
You will learn much more as you practice and work on projects from yourself
You can find dataset in this channel or go to kaggle to find any random dataset and just work on it
Learning concepts is fine but most of the learnings come from projects
I know that might feel boring at first time but as you move forward, it become interesting
K-means vs DBScan ML Algorithm
DBScan is more robust to noise.
DBScan is better when the amount of clusters is difficult to guess.
K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.
What is the curse of dimensionality? Why do we care about it?
Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.
Dimensionality reduction techniques
Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
T-distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Fourier and Wavelet Transforms
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Chatbot project using ML
Before using this you have to install
Tensorflow, keras , pickle, nltk by using pip install in command prompt
Fake news Detection Machine Learning Project with 92%Accuracy
it contain compressed file in which "jupyter notebook file and dataset"β
Adaptive_Computation_and_Machine_Learning_Ralf_Herbrich_Learning.pdf2.69 MB
Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
π Welcome to @datastructuresandalgoofficial π
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In this Channel you will get all materials related Data Structures and Algorithms(DSA).
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https://t.me/datastructuresandalgoofficial
π Welcome to @datastructuresandalgoofficial π
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Free Data Science courses from Udemy and Udacity
ππ
Intro to Data Science
https://imp.i115008.net/rn2beD
Data Analysis and Visualization
https://imp.i115008.net/JrBjZR
Data Analysis with R by Facebook
https://imp.i115008.net/gbJr5r
Introduction to Data Science using Python
https://ern.li/OP/1qvkxbfaxqj
Intro to Data for Data Science
https://ern.li/OP/1qvkxbfbmf8
Data Science with Analogies, Algorithms and Solved Problems
https://ern.li/OP/1qvkxbfcehz
Introduction to Data Science for Complete Beginners
https://bit.ly/3sh4oPO
ENJOY LEARNING ππ
Source codes for data science projects ππ
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
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