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Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
نمایش بیشتر📈 تحلیل کانال تلگرام Machine Learning & Artificial Intelligence | Data Science Free Courses
کانال Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 66 773 مشترک است و جایگاه 2 441 را در دسته آموزش و رتبه 431 را در منطقه ماليزيا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 66 773 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 27 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 489 و در ۲۴ ساعت گذشته برابر 7 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 0.61% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.78% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 408 بازدید دریافت میکند. در اولین روز معمولاً 524 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند sellerflash, waybienad, pricing, buybox, buyer تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 28 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
66 773
مشترکین
+724 ساعت
+1137 روز
+48930 روز
آرشیو پست ها
Data Science Bookcamp Ten Python projects.pdf11.72 MB
Deep Learning for Natural Language Processing (2022).pdf9.56 MB
Important Topics to become a data scientist
[Advanced Level]
👇👇
1. Mathematics
Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification
2. Probability
Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution
3. Statistics
Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression
4. Programming
Python:
Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn
R Programming:
R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny
DataBase:
SQL
MongoDB
Data Structures
Web scraping
Linux
Git
5. Machine Learning
How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage
6. Deep Learning
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification
7. Feature Engineering
Baseline Model
Categorical Encodings
Feature Generation
Feature Selection
8. Natural Language Processing
Text Classification
Word Vectors
9. Data Visualization Tools
BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense
10. Deployment
Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django
Join @datasciencefun to learn important data science and machine learning concepts
ENJOY LEARNING 👍👍
Most Machine Learning articles on Medium are really very bad quality and repetitive. Titles are usually clickbaits. Most start with a story which is utter nonsense and totally not required. In some 5-10% content is useful but most are fully useless. Sorry if I hurt feelings. Agree 👍
Python Workout 50 Essential Exercises
📓 book
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems
📚 book
To become a Machine Learning Engineer:
• Python
• numpy, pandas, matplotlib, Scikit-Learn
• TensorFlow or PyTorch
• Jupyter, Colab
• Analysis > Code
• 99%: Foundational algorithms
• 1%: Other algorithms
• Solve problems ← This is key
• Teaching = 2 × Learning
• Have fun!
DISADVANTAGE OF MACHINE LANGUAGE
Here are some of the main disadvantages of machine languages:
• Machine Dependent - the internal design of every computer is different from every other type of computer, machine language also differs from one computer to another. Hence, after becoming proficient in the machine language of one type of computer, if a company decides to change to another type, then its programmer will have to learn a new machine language and would have to rewrite all existing program.
• Difficult to Modify - it is difficult to correct or modify this language. Checking machine instructions to locate errors is very difficult and time consuming.
• Difficult to Program - a computer executes machine language program directly and efficiently, it is difficult to program in machine language. A machine language programming must be knowledgeable about the hardware structure of the computer.
ADVANTAGE OF MACHINE LANGUAGE
The only advantage of machine language is that the program of machine language runs very fast because no translation program is required for the CPU.
MACHINE LANGUAGE
The instructions in binary form, which can be directly understood by the computer (CPU) without translating them, is called a machine language or machine code.
Machine language is also known as first generation of programming language. Machine language is the fundamental language of the computer and the program instructions in this language is in the binary form (that is 0's and 1's).
This language is different for different computers.
It is not easy to learn the machine language.
Where to get data for your next machine learning project?
An overview of 8 amazing resources to accelerate your next project with data!
📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.
📌 Papers with Code Datasets
An exclusive collection of 4053 machine learning datasets with a supreme search and a good composition of datasets .
📌 Kaggle Dataset
Explore, analyze, and share quality data.
📌 Big Bad NLP Datasets
One of the best sources for sophisticated Natural Language Processing datasets
📌 Hugging Face Datasets
Well known for NLP but good news hugging face is expanding and they can add datasets for machine learning soon, they have 921 datasets.
📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources
📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.
📌 Azure public data sets
This one has public data sets for testing and prototyping.
📌 Carnegie Mellon University
A listing of 750 databases, datasets, and research support tools.
Bonus: This articles on Kdnuggets covers around 80 datasets sources of the datasets. Enjoy machine learning.
📚 Title: Python for Beginners (2023)
📸 Book Cover: Click Here
Pandas for Everyone (2023).pdf13.28 MB
Machine Learning Interview Cheat sheets.pdf6.19 MB
Keyboard Shortcuts for Data Scientists.pdf5.13 MB
Machine Learning in Microservices
Mohamed Abouahmed, 2023
Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE
https://developers.google.com/machine-learning/crash-course
https://www.kaggle.com/learn/overview
https://forums.fast.ai/t/recommended-python-learning-resources/26888
https://www.fast.ai/
https://imp.i115008.net/JrBjZR
https://ern.li/OP/1qvkxbfaxqj
ENJOY LEARNING 👍👍
Artificial Neural Networks with Java
Igor Livshin, 2019
