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Сеть:📈 Аналитический обзор Telegram-канала Machine Learning & Artificial Intelligence | Data Science Free Courses
Канал Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 68 676 подписчиков, занимая 2 397 место в категории Образование и 417 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 68 676 подписчиков.
Согласно последним данным от 25 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 242, а за последние 24 часа — 13, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.23%. В первые 24 часа после публикации контент обычно набирает 0.85% реакций от общего числа подписчиков.
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- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, waybienad, pricing, buybox, buyer.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Благодаря высокой частоте обновлений (последние данные получены 26 октября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
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Посты канала
When a dataset contains many features, dimensionality reduction can represent the data using fewer dimensions while retaining important information.
A common technique is:
PCA — Principal Component Analysis
Benefits can include:
• Simplifying data
• Visualization
• Reducing noise
• Improving computational efficiency
39. Model Selection
Different algorithms work better for different problems.
For example:
Linear Regression → Simple numerical prediction
Random Forest → Complex tabular relationships
K-Means → Customer segmentation
Neural Networks → Complex patterns and large-scale data
The best model isn't always the most complicated one.
40. Model Deployment
A trained model is useful only when it can be used in a real application.
Double Tap ❤️ For More
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0.959408 ₽ · /balance_help
| 2 | Cross-validation provides a more reliable estimate of model performance.
In K-Fold Cross-Validation, the data is divided into K parts.
The model trains and validates multiple times using different portions of the data.
This helps determine whether the model performs consistently.
27. Confusion Matrix
A confusion matrix evaluates classification predictions.
It contains:
• True Positive (TP) → Correct positive prediction
• True Negative (TN) → Correct negative prediction
• False Positive (FP) → Incorrect positive prediction
• False Negative (FN) → Incorrect negative prediction
It forms the foundation for several classification metrics.
28. Accuracy
Accuracy measures the proportion of predictions that are correct.
Accuracy = Correct Predictions / Total Predictions
It can be useful when classes are reasonably balanced.
But accuracy can be misleading for highly imbalanced datasets.
29. Precision
Precision answers:
"Of everything the model predicted as positive, how much was actually positive?"
Important when false positives are costly.
Example:
Fraud detection where investigating every false alarm is expensive.
30. Recall
Recall answers:
"Of all the actual positive cases, how many did the model successfully identify?"
Important when missing a positive case is costly.
Example:
Detecting fraudulent transactions or serious diseases.
31. F1 Score
F1 Score combines Precision and Recall into a single metric.
It is especially useful when you need a balance between precision and recall.
A high F1 score generally means the model performs well on both.
32. ROC-AUC
ROC-AUC measures how well a classification model separates positive and negative classes across different classification thresholds.
A higher AUC generally indicates better discrimination.
It is useful when comparing classification models, especially when the decision threshold can change.
33. Regularization
Regularization helps prevent overfitting by penalizing overly complex models.
Common techniques:
L1 Regularization → Lasso
L2 Regularization → Ridge
The goal is not simply to make the model smaller, but to improve its ability to generalize to unseen data.
34. Decision Trees
A Decision Tree makes predictions by repeatedly splitting data based on feature values.
Example:
Income > ₹10L?
Yes → Higher-risk segment
No → Lower-risk segment
Trees are easy to understand and can handle both classification and regression tasks.
35. Random Forest
Random Forest combines many decision trees.
Each tree makes a prediction and the ensemble combines their results.
Advantages:
• Powerful
• Handles nonlinear relationships
• Less prone to overfitting than a single deep tree
• Works well with many tabular datasets
36. Boosting
Boosting builds models sequentially, with each new model focusing on mistakes made by previous models.
Popular algorithms include:
• XGBoost
• LightGBM
• CatBoost
• AdaBoost
Boosting is widely used for structured/tabular data.
37. Clustering
Clustering groups similar data points without predefined labels.
Example:
A business might group customers into:
• Budget customers
• Regular customers
• High-value customers
K-Means is one of the most commonly used clustering algorithms.
38. Dimensionality Reduction | 790 |
| 3 | • Mean Squared Error
• Mean Absolute Error
• Log Loss
• Cross-Entropy Loss
16. Cost Function
A cost function generally represents the overall error across a set of training examples.
For example, in linear regression, Mean Squared Error can be used to measure the average prediction error.
The model attempts to minimize the cost.
17. Gradient Descent
Gradient Descent is an optimization algorithm used to minimize the model's error.
Think of it like standing on a mountain and trying to reach the lowest point.
The algorithm repeatedly adjusts model parameters in a direction that reduces the loss.
18. Learning Rate
The learning rate controls how large each update is during optimization.
Too large:
❌ The model may overshoot the optimal solution.
Too small:
❌ Training may become very slow.
A suitable learning rate helps the model converge efficiently.
19. Parameters vs Hyperparameters
Parameters
Values learned automatically by the model during training.
Examples:
• Regression coefficients
• Neural-network weights
Hyperparameters
Settings chosen before or during model training.
Examples:
• Learning rate
• Number of trees
• Maximum tree depth
• Number of clusters
20. Feature Engineering
Feature engineering means creating or transforming features to help a model learn better.
Example:
Instead of using:
Purchase Date
Create:
Customer Tenure
Other examples:
• Extracting month from a date
• Creating total purchase amount
• Creating customer tenure
• Combining related variables
Good features can significantly improve model performance.
21. Feature Scaling
Features may have very different numerical ranges.
Example:
Age → 18–70
Salary → 20,000–500,000
Scaling transforms features into comparable ranges.
Common techniques:
• Standardization
• Min-Max Scaling
Scaling is particularly important for algorithms such as KNN, SVM, and many optimization-based models.
22. Encoding Categorical Variables
Machine learning algorithms generally need numerical representations of categorical information.
Example:
Gender:
Male
Female
Could be represented using numerical encoding.
Common techniques:
• One-Hot Encoding
• Ordinal Encoding
• Target Encoding
The appropriate method depends on the data and algorithm.
23. Missing Values
Real-world datasets often contain missing information.
Example:
Age:
25
32
Missing
41
Possible approaches:
• Remove rows
• Fill with mean
• Fill with median
• Fill with mode
• Use model-based imputation
The correct approach depends on why the values are missing and the characteristics of the dataset.
24. Outliers
An outlier is a data point that is significantly different from the rest.
Example:
Most salaries → ₹4–15 lakh
One salary → ₹500 lakh
Outliers can sometimes represent errors, but they can also contain valuable information.
Don't automatically remove them.
25. Data Leakage
Data leakage occurs when information that should not be available during prediction accidentally enters the training process.
Example:
Trying to predict whether a customer will default, but including a variable that is only recorded after the customer defaults.
This can produce excellent test results that fail in the real world.
26. Cross-Validation | 482 |
| 4 | The model learns from these examples and predicts the salary for someone with 4 years of experience.
Two major types:
Regression → Predict a number
Classification → Predict a category
5. Unsupervised Learning
Here, the data does not have a predefined target.
The model tries to discover hidden patterns or groups.
Example:
A company gives customer purchasing data to an algorithm and asks it to identify different customer groups.
Common techniques:
• K-Means Clustering
• Hierarchical Clustering
• PCA
6. Regression
Regression predicts a continuous numerical value.
Examples:
• House price
• Sales
• Revenue
• Temperature
• Customer lifetime value
Common algorithms:
• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
7. Classification
Classification predicts a category or class.
Examples:
• Spam / Not Spam
• Fraud / Not Fraud
• Customer will churn / Won't churn
• Disease / No Disease
Common algorithms:
• Logistic Regression
• Decision Tree
• Random Forest
• SVM
• KNN
• Naive Bayes
8. Training Data
Training data is the data used to teach the model.
The model analyzes the training examples and learns relationships between features and the target.
More importantly, a good model shouldn't simply memorize the training data.
9. Test Data
Test data is unseen data used to determine how well the trained model performs on new examples.
A common approach is:
Training Data → Learn
Test Data → Evaluate
The goal is to perform well on data the model hasn't seen before.
10. Validation Data
Validation data is used during model development to:
• Compare models
• Tune parameters
• Select features
• Choose the best configuration
A common setup is:
Training → Validation → Test
The test set should ideally remain untouched until final evaluation.
11. Overfitting
Overfitting occurs when a model learns the training data too closely, including noise and random patterns.
Result:
Training performance → Very high
New-data performance → Poor
Example:
A student memorizes every question from a practice test but cannot solve new questions.
Common solutions:
• More training data
• Simpler model
• Regularization
• Cross-validation
• Feature selection
12. Underfitting
Underfitting occurs when a model is too simple to capture the important patterns in the data.
Result:
Training performance → Poor
Test performance → Poor
The model hasn't learned enough.
13. Bias and Variance
These describe two important sources of model error.
High Bias → Model is too simple
Usually associated with underfitting.
High Variance → Model is too sensitive to training data
Usually associated with overfitting.
The goal is to find a good balance between them.
14. Bias-Variance Tradeoff
Increasing model complexity can reduce bias but increase variance.
Reducing complexity can reduce variance but increase bias.
A good ML model tries to achieve a useful balance rather than simply maximizing complexity.
15. Loss Function
A loss function measures how wrong the model's prediction is.
Example:
Actual price = ₹50 lakh
Predicted price = ₹45 lakh
The loss function measures the difference between them.
The training process tries to minimize this loss.
Common examples: | 543 |
| 5 | 🚀 Machine Learning: Important Concepts You Should Know
Machine Learning can seem overwhelming because there are many algorithms and techniques. The key is to first understand the core concepts behind how ML works.
1. What is Machine Learning?
Machine Learning is a branch of AI where computers learn patterns from data and use those patterns to make predictions or decisions.
Example:
Give a model historical house data containing size, location, and price. The model learns the relationship between these factors and can predict the price of a new house.
2. Features
Features are the input variables used by a machine learning model.
Example:
For house-price prediction:
• House size
• Number of bedrooms
• Location
• Age of house
These are the features.
Think of features as the information the model uses to make a prediction.
3. Target Variable
The target is what the model is trying to predict.
Example:
Features → Size, bedrooms, location
Target → House price
In supervised learning, the target is usually represented as y, while features are represented as X.
4. Supervised Learning
In supervised learning, the model learns from data where the correct answer is already known.
Example:
Experience | Salary
1 year | ₹4L
3 years | ₹7L
5 years | ₹11L | 694 |
| 6 |  🗺️ 6 stops to a data career.
Generative AI → Spreadsheets → SQL → AI assistants & agents → Python → Machine Learning. Then a capstone on your own dataset.
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| 7 | ✅ SQL for Data Science 🗄️📊
👉 SQL is one of the most important skills for Data Scientists and Data Analysts.
Almost every company stores data inside databases, and SQL helps retrieve and analyze that data.
🔹 1. What is SQL?
SQL = Structured Query Language
👉 Used to:
✔ Store data
✔ Retrieve data
✔ Filter data
✔ Analyze data
🔥 2. Common Database Systems
✔ MySQL
✔ PostgreSQL
✔ SQLite
✔ Microsoft SQL Server
🔹 3. Basic SQL Query
✅ SELECT Statement
Used to retrieve data from a table.
SELECT * FROM employees;
👉 ** means all columns.
🔹 4. Select Specific Columns
SELECT name, salary FROM employees;
🔹 5. WHERE Clause ⭐
Used for filtering data.
SELECT * FROM employees
WHERE salary > 50000;
🔹 6. ORDER BY
Sort data.
SELECT * FROM employees
ORDER BY salary DESC;
✔ ASC → Ascending
✔ DESC → Descending
🔹 7. Aggregate Functions ⭐
Used for calculations.
Function: COUNT()
Purpose: Count rows
Function: SUM()
Purpose: Total
Function: AVG()
Purpose: Average
Function: MAX()
Purpose: Highest value
Function: MIN()
Purpose: Lowest value
✅ Example
SELECT AVG(salary)
FROM employees;
🔹 8. GROUP BY ⭐
Used to group data.
SELECT department, AVG(salary)
FROM employees
GROUP BY department;
🔹 9. Why SQL is Important?
✔ Most asked interview skill
✔ Used daily by analysts & data scientists
✔ Essential for working with databases
🎯 Today’s Goal
✔ Learn SELECT queries
✔ Filter using WHERE
✔ Use aggregate functions
✔ Understand GROUP BY
👉 SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v 🗄️🔥
💬 Tap ❤️ for more! | 1 970 |
| 8 |  ⚡️ Kandinsky 6.0 Video vs Competitors: Benchmark Analysis
The flagship of the new open-source Kandinsky 6.0 Video lineup — the Pro model with 29 billion parameters — shows significant improvements over its predecessor and competitors.
📊 Benchmark results:
vs Kandinsky 5.0:
• Clear advantage across all criteria
• Improved visual quality
• Better prompt adherence
• Enhanced camera movement control
vs LTX 2.5 (open-source):
• Preferred in visual quality and speech quality
vs Veo 3.1 Fast (closed-source):
• Superior in image animation tasks
• Better visual quality
• Improved source image adherence
• More natural camera movement
🏗 Technical architecture:
• Dual-stream architecture (video + audio)
• Audio pretrained on 40M tracks; joint training on 7M audio-video segments
• 44 kHz audio output
• Full HD resolution support
• Lip-sync technology
• Lite version (3B) runs on consumer GPUs (16–32 GB VRAM)
🔧 Deployment:
Open-source under MIT license. Available through:
• Diffusers
• FastVideo
• ComfyUI
• SGLang
• vLLM-omni
Anton Frolov, Sber: "We're releasing it free of charge and without restrictions."
🔗 Hugging Face | 2 931 |
| 9 | Machine Learning Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus (Gradients, Optimization)
| | |-- Probability and Statistics
| | |-- Matrix Operations
| |
| |-- Programming
| | |-- Python (NumPy, Pandas, Scikit-learn)
| | |-- R (Optional for Statistical Modeling)
| | |-- SQL (For Data Extraction)
|
|-- Data Preprocessing
| |-- Data Cleaning
| |-- Feature Engineering
| | |-- Encoding Categorical Data
| | |-- Feature Scaling (Standardization, Normalization)
| | |-- Handling Missing Values
| |-- Dimensionality Reduction (PCA, LDA)
|
|-- Supervised Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | |-- Ridge and Lasso Regression
| |-- Classification
| | |-- Logistic Regression
| | |-- Decision Trees
| | |-- Support Vector Machines (SVM)
| | |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
|
|-- Unsupervised Learning
| |-- Clustering
| | |-- K-Means
| | |-- Hierarchical Clustering
| | |-- DBSCAN
| |-- Dimensionality Reduction
| | |-- Principal Component Analysis (PCA)
| | |-- t-SNE
| |-- Association Rules (Apriori, FP-Growth)
|
|-- Reinforcement Learning
| |-- Markov Decision Processes
| |-- Q-Learning
| |-- Deep Q-Learning
| |-- Policy Gradient Methods
|
|-- Model Evaluation and Optimization
| |-- Train-Test Split and Cross-Validation
| |-- Performance Metrics
| | |-- Accuracy, Precision, Recall, F1-Score
| | |-- ROC-AUC
| | |-- Mean Squared Error (MSE), R-squared
| |-- Hyperparameter Tuning
| | |-- Grid Search
| | |-- Random Search
| | |-- Bayesian Optimization
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Perceptrons
| | |-- Backpropagation
| |-- Convolutional Neural Networks (CNN)
| | |-- Image Classification
| | |-- Object Detection (YOLO, SSD)
| |-- Recurrent Neural Networks (RNN)
| | |-- LSTM
| | |-- GRU
| |-- Transformers (Attention Mechanisms, BERT, GPT)
| |-- Tools and Frameworks (TensorFlow, PyTorch)
|
|-- Advanced Topics
| |-- Transfer Learning
| |-- Generative Adversarial Networks (GANs)
| |-- Reinforcement Learning with Neural Networks
| |-- Explainable AI (SHAP, LIME)
|
|-- Applications of Machine Learning
| |-- Recommender Systems (Collaborative Filtering, Content-Based)
| |-- Fraud Detection
| |-- Sentiment Analysis
| |-- Predictive Maintenance
| |-- Autonomous Vehicles
|
|-- Deployment of Models
| |-- Flask, FastAPI
| |-- Cloud Deployment (AWS SageMaker, Azure ML)
| |-- Containerization (Docker, Kubernetes)
| |-- Model Monitoring and Retraining
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AI, Machine Learning and Deep Learning
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| 10 |  🚀 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐧 𝟐𝟎𝟐𝟔
🎯 Choose Your Learning Track:
💻 Java Full Stack + AI Engineering
🌐 MERN Full Stack + AI Engineering
Placement Highlights: ₹41 LPA highest package | ₹7.4 LPA average package | 2,000+ students placed | 500+ hiring partners
🔗 𝗕𝗼𝗼𝗸 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 :- https://pdlink.in/4fWJVID
⚡ AI is creating new career opportunities—start building the skills companies need in 2026! | 4 812 |
| 11 |  Machine Learning Roadmap | 4 597 |
| 12 |  SQL & Python Cheatsheet for Beginners ❤️ | 4 056 |
| 13 | ![#Ad #AI_Models 🔥 GigaChat 3.5 Reasoning [Open-Source] ℹ️ Overview: New LLM that thinks before it answers. Breaks problems in](https://cdn.tlmtr.io/_next/image?url=https%3A%2F%2Fn1.tlmtr.cc%2Fv%2F_u74IP8VHJ7vIO8AuGlc4bjQucvvMGYIZhPDTZs5S5So%3Fty%3Dx&w=64&q=75){kind=small} #Ad
#AI_Models
🔥 GigaChat 3.5 Reasoning [Open-Source]
ℹ️ Overview:
New LLM that thinks before it answers. Breaks problems into stages, builds plans, checks results, and self-corrects using automated verification.
🔗 Source:
Hugging Face fp8 | bf16
📝 Model Specs:
✪ Built on GigaChat 3.5 Ultra with multiple step-by-step reasoning paths
✪ Proprietary linear attention for efficient long contexts
✪ Token-efficient: 37% fewer tokens than DeepSeek V4 Flash Preview
✪ Benchmarks: IFBench 44→77, Natural Plan 64→80, LiveCodeBench v6 56→85
✪ MIT License | 3 781 |
| 14 | ✅ Programming Languages, Libraries & Tools Every Tech Field Uses 👨💻🚀
🧠 DATA SCIENCE & MACHINE LEARNING
1. Python → Pandas, NumPy, TensorFlow, PyTorch
2. R → ggplot2, dplyr, caret
3. SQL → PostgreSQL, MySQL
4. Julia → Flux, Pluto
🤖 ARTIFICIAL INTELLIGENCE
1. Python → Keras, OpenCV, LangChain
2. C++ → OpenCV, CUDA
3. Java → Deeplearning4j
🌐 WEB DEVELOPMENT
1. JavaScript → React, Node.js, Express.js
2. TypeScript → Next.js, Angular
3. PHP → Laravel
4. Python → Django, Flask
📱 APP DEVELOPMENT
1. Kotlin → Android SDK, Jetpack Compose
2. Swift → SwiftUI, UIKit
3. Dart → Flutter
4. JavaScript → React Native
🎮 GAME DEVELOPMENT
1. C++ → Unreal Engine
2. C# → Unity
3. Lua → Roblox Studio
4. Python → Pygame
🔐 CYBER SECURITY
1. Python → Scapy, Requests
2. Bash → Linux Tools
3. PowerShell → Windows Automation
4. Go → Networking Tools
☁️ CLOUD & DEVOPS
1. Go → Docker, Kubernetes
2. Python → Ansible, Boto3
3. Shell Script → Linux Automation
4. YAML → CI/CD Pipelines
💬 Tap ❤️ if this helped you! | 3 111 |
| 15 |  𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀
Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles.
📅 Date: 24 September 2026
⏰ Time: 7:00 PM–9:00 PM IST
🌐 Mode: Online
🎓 Certificate: Available to all attendees
Eligibility :- Graduates Passing In 2025 or earlier
🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇
https://pdlink.in/4xAMeGW
⚡ Register now and take your first step towards a successful career in AI! | 2 863 |
| 16 | ✅ 🔤 A–Z of Machine Learning
A – Artificial Neural Networks
Computing systems inspired by the human brain, used for pattern recognition.
B – Bagging
Ensemble technique that combines multiple models to improve stability and accuracy.
C – Cross-Validation
Method to evaluate model performance by partitioning data into training and testing sets.
D – Decision Trees
Models that split data into branches to make predictions or classifications.
E – Ensemble Learning
Combining multiple models to improve overall prediction power.
F – Feature Scaling
Techniques like normalization to standardize data for better model performance.
G – Gradient Descent
Optimization algorithm to minimize the error by adjusting model parameters.
H – Hyperparameter Tuning
Process of selecting the best model settings to improve accuracy.
I – Instance-Based Learning
Models that compare new data to stored instances for prediction.
J – Jaccard Index
Metric to measure similarity between sample sets.
K – K-Nearest Neighbors (KNN)
Algorithm that classifies data based on closest training examples.
L – Logistic Regression
Statistical model used for binary classification tasks.
M – Model Overfitting
When a model performs well on training data but poorly on new data.
N – Normalization
Scaling input features to a specific range to aid learning.
O – Outliers
Data points that deviate significantly from the majority and may affect models.
P – PCA (Principal Component Analysis)
Technique for reducing data dimensionality while preserving variance.
Q – Q-Learning
Reinforcement learning method for learning optimal actions through rewards.
R – Regularization
Technique to prevent overfitting by adding penalty terms to loss functions.
S – Support Vector Machines
Supervised learning models for classification and regression tasks.
T – Training Set
Data used to fit and train machine learning models.
U – Underfitting
When a model is too simple to capture underlying patterns in data.
V – Validation Set
Subset of data used to tune model hyperparameters.
W – Weight Initialization
Setting initial values for model parameters before training.
X – XGBoost
Efficient implementation of gradient boosted decision trees.
Y – Y-Axis
In learning curves, represents model performance or error rate.
Z – Z-Score
Statistical measurement of a value's relationship to the mean of a group.
Double Tap ♥️ For More | 2 772 |
| 17 | Sure! Here’s the revised version with the asterisks replaced by double asterisks:
🧠 Skills & Techniques for Data Science, Machine Learning & AI!
📊 Core Data Science Skills
▪️ Probability & Statistics – Foundation of Data Insights
▪️ Hypothesis Testing – Validating Assumptions
▪️ Regression Analysis – Predictive Modeling
▪️ A/B Testing – Experimentation for Business Impact
▪️ Data Cleaning – Turning Raw Data into Usable Insights
🤖 Machine Learning Techniques
▪️ Linear & Logistic Regression – Predictive Models
▪️ Decision Trees / Random Forest – Classification & Prediction
▪️ K-means / Hierarchical Clustering – Grouping Data
▪️ PCA – Dimensionality Reduction
▪️ Cross-validation – Reliable Model Testing
🧠 AI & GenAI Skills
▪️ Prompt Engineering – Getting Best from LLMs
▪️ OpenAI APIs – Building AI-powered Apps
▪️ Hugging Face Transformers – NLP at Scale
▪️ Computer Vision – Image Recognition & Detection
▪️ Reinforcement Learning – Training Agents with Rewards
💾 Data Tools & Platforms
▪️ SQL – Querying Structured Data
▪️ MongoDB – Flexible NoSQL Storage
▪️ Spark / Hadoop – Big Data Processing
▪️ AWS / GCP / Azure – Cloud Data Solutions
🚢 Deployment & MLOps
▪️ Flask / FastAPI – Serving ML Models
▪️ Docker – Containerization
▪️ Kubernetes – Scaling Deployments
▪️ Git – Version Control
▪️ CI/CD – Continuous Integration & Delivery
🎯 What Makes You Valuable
▪️ Clean Data → Clear Insights
▪️ Measurable ROI → Business Impact
▪️ Faster Decisions → Competitive Advantage
React ❤️ for more! | 3 139 |
| 18 | ✅ Machine Learning Explained for Beginners 🤖📚
📌 Definition:
Machine Learning (ML) is a type of artificial intelligence that allows systems to learn from data and make decisions or predictions without being explicitly programmed for every task.
1️⃣ How It Works:
ML systems are trained on historical data to identify patterns. Once trained, they apply those patterns to new, unseen data.
Example: Feed a model emails labeled "spam" or "not spam," and it learns how to filter spam automatically.
2️⃣ Types of Machine Learning:
a) Supervised Learning
• Learns from labeled data (inputs + expected outputs)
• Examples: Email classification, price prediction
b) Unsupervised Learning
• Learns from unlabeled data
• Examples: Customer segmentation, topic modeling
c) Reinforcement Learning
• Learns by interacting with the environment and receiving rewards
• Examples: Game AI, robotics
3️⃣ Common Use Cases:
• Recommender systems (Netflix, Amazon)
• Face recognition
• Voice assistants (Alexa, Siri)
• Credit card fraud detection
• Predicting customer churn
4️⃣ Why It Matters:
ML powers smart systems and automates complex decisions. It's used across industries for improving speed, accuracy, and personalization.
5️⃣ Key Terms You’ll Hear Often:
• Model: The trained algorithm
• Dataset: Data used to train or test
• Features: Input variables
• Labels: Target outputs
• Training: Feeding data to the model
• Prediction: The model's output
💡 Start with simple projects like spam detection or house price prediction using Python and scikit-learn.
💬 Tap ❤️ for more!
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1.26 ₽ · /balance_help | 2 752 |
| 19 | If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍 | 2 703 |
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