1. Get started with microsoft data analytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐25
This free 10-part course on #GitHub will guide you from concept to #code as you start building #AI #agents:
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐14โค3๐จโ๐ป2
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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course lecture on building Transformers from first principles:
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0
The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0
The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐13๐ฅ2
๐จ๐ปโ๐ป One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
โ๏ธ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
โ
Why should we use it?
๐ข For learning: If you're looking to learn algorithms in action, this is great.
๐ข For practice: You can take the codes, run them, and modify them to better understand.
๐ข For projects : You can even use the codes here in real-life or academic projects.
๐ข For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
โ๐ณ๏ธโ๐ The Algorithms - Python
โ๐ฑ GitHub-Repos
โ
โ
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐14โค3
Pandas Introduction to Advanced.pdf
854.8 KB
๐จ๐ปโ๐ป You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐24๐ฅ2
Find these FREE AI Courses here ๐
https://www.mltut.com/best-resources-to-learn-artificial-intelligence/
https://www.mltut.com/best-resources-to-learn-artificial-intelligence/
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐18๐ฅ3โค1๐ฏ1
Exercises in Machine Learning
This book contains 75+ exercises
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
This book contains 75+ exercises
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammer โ
๐12๐ฅ1๐1
Linear Algebra
The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.
Completely FREE.
Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf
The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.
Completely FREE.
Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐11๐ฏ5๐พ2
Forwarded from Data Science Machine Learning Data Analysis Books
#MachineLearning Systems โ Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/
open-source textbook focuses on how to design and implement AI systems effectively
open-source textbook focuses on how to design and implement AI systems effectively
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/DataScienceMโ
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๐9
Introduction to Machine Learning Class Notes by Huy Nguyen
https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf
https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐11โค1
This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesโsuch as feature engineering or balancing response variablesโor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
https://dafriedman97.github.io/mlbook/content/introduction.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐11โค2๐ฏ1
Stanfordโs Machine Learning - by Andrew Ng
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐17โค2
"Machine Learning & LLMs for Beginners"
Don't miss these 2 books of 100-pages. Both are #FREE to read.
๐ The Hundred-Page Machine Learning Book:
themlbook.com/wiki/doku.php
๐ The Hundred-Page Language Model Book:
thelmbook.com
Don't miss these 2 books of 100-pages. Both are #FREE to read.
themlbook.com/wiki/doku.php
thelmbook.com
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammer๐
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๐9
Stanford's "Design and Analysis of Algorithms" Winter 2025
Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/
Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐10โค1
"Introduction to Probability for Data Science"
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.me/CodeProgrammerโ
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๐18๐ฏ3๐ฅ1
SciPy.pdf
206.4 KB
Unlock the full power of SciPy with my comprehensive cheat sheet!
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
๐ฏ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
#Python #SciPy #MachineLearning #DataScience #CheatSheet #ArtificialIntelligence #Optimization #LinearAlgebra #SignalProcessing #BigData
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๐11๐1
9 machine learning concepts for ML engineers!
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1๏ธโฃ 4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
2๏ธโฃ 4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isnโt that complicated.
- Implemented here: https://lnkd.in/g33mASMM
3๏ธโฃ Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
4๏ธโฃ Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
5๏ธโฃ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
6๏ธโฃ 15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
7๏ธโฃ 11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
8๏ธโฃ 11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
9๏ธโฃ NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
๐ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
- While testing a model in production might sound risky, ML teams do it all the time, and it isnโt that complicated.
- Implemented here: https://lnkd.in/g33mASMM
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization
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โค10๐8๐ฏ1
PySpark power guide.pdf
1.2 MB
๐ช๐ต๐ ๐๐๐ฒ๐ฟ๐ ๐๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ
If youโre working with large datasets, tools like Pandas can hit limits fast. Thatโs where ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ comes inโdesigned to scale effortlessly across big data workloads.
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ?
PySpark is the Python API for Apache Sparkโa powerful engine for distributed data processing. It's widely used to build scalable ETL pipelines and handle millions of records efficiently.
๐ช๐ต๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ ๐ฎ ๐ ๐๐๐-๐๐ฎ๐๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐:
โ๏ธ Scales to handle massive datasets
โ๏ธ Designed for distributed computing
โ๏ธ Blends SQL with Python for flexible logic
โ๏ธ Perfect for building end-to-end ETL pipelines
โ๏ธ Supports integrations like Hive, Kafka, and Delta Lake
๐ค๐๐ถ๐ฐ๐ธ ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
If youโre working with large datasets, tools like Pandas can hit limits fast. Thatโs where ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ comes inโdesigned to scale effortlessly across big data workloads.
๐ช๐ต๐ฎ๐ ๐ถ๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ?
PySpark is the Python API for Apache Sparkโa powerful engine for distributed data processing. It's widely used to build scalable ETL pipelines and handle millions of records efficiently.
๐ช๐ต๐ ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐๐ ๐ฎ ๐ ๐๐๐-๐๐ฎ๐๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐:
โ๏ธ Scales to handle massive datasets
โ๏ธ Designed for distributed computing
โ๏ธ Blends SQL with Python for flexible logic
โ๏ธ Perfect for building end-to-end ETL pipelines
โ๏ธ Supports integrations like Hive, Kafka, and Delta Lake
๐ค๐๐ถ๐ฐ๐ธ ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ:
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Example").getOrCreate()
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.filter(df["age"] > 30).show()
#PySpark #DataEngineering #BigData #ETL #ApacheSpark #DistributedComputing #PythonForData #DataPipelines #SparkSQL #ScalableAnalytics
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๐13โค2