AI & Machine Learning & Deep Learning

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AI & Machine Learning & Deep Learning

Here you can Learn and Download 1. Artificial Intelligence 2. Machine Learning 3. Deep Learning 4. NLP 5. Statistics 6. Data Visualization 7. Data Analysis 8. Time Series Analysis Learn Step by Step Machine Learning: https://t.me/LearnAIMLStepbyStep

@AIMLDeepThaught is a dedicated channel for machine and learning with regular updates in Courses & Learning

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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AI Engineering https://www.instagram.com/neural_nexus_ai_?igsh=bTdhNzNuMHI4YWFz

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Practical Statistics for Data Scientists

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Practical Statistics for Data Scientists
https://www.instagram.com/neural_nexus_ai_?igsh=bTdhNzNuMHI4YWFz

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Context Window

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Deep Learning for the Life Sciences

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Generative AI with LangChain

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Generative AI with LangChain

If you are interested in learning about the latest trends in AI, please follow Instagram ID.
https://www.instagram.com/neural_nexus_ai_?igsh=bTdhNzNuMHI4YWFz

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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AI Engineering

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Time Series

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AI & Machine Learning & Deep Learning

May 24, 2026, 05:10 AM

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Deep Learning for the Life Sciences

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

📈 Prophet for Time Series Forecasting (Meta)
Prophet is an additive, modular time-series forecasting framework developed by Meta, optimized for business-oriented temporal data with multiple seasonal components and known calendar effects.
🔹 When Prophet Is a Good Choice
✔️ Time series with strong, recurring seasonality
✔️ Daily / weekly / monthly data with calendar effects
✔️ Forecasts requiring interpretability & explainability
✔️ Business metrics (sales, demand, traffic, revenue)
🟢 Strengths
•  Automatic handling of multiple seasonalities
•  Built-in holiday and event modeling
•  Robust to missing data, outliers, and irregular sampling
•  Limited hyperparameter tuning required
•  Clear decomposition of forecast components
🔴 Limitations
•  Limited performance on short time series
•  Not suitable for high-frequency (sub-hourly) data
•  Weak in presence of sudden regime shifts / structural breaks
•  Less expressive than deep learning models (LSTM, TFT, N-BEATS)
•  Assumes additive structure by default
🧠 Practical Insight
Prophet is best used as a strong baseline model for structured business time series before moving to more complex statistical or deep learning approaches.
📌 “Fast to deploy, easy to interpret, but not designed for highly complex temporal dynamics.”

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

Machine Learning Algorithms

🔹 Supervised Learning
• Linear Regression – Predicts continuous values (house prices)
• Logistic Regression – Probability-based classification (spam/not spam)
• Decision Tree – Explainable, but prone to overfitting
• Random Forest – Robust ensemble, handles complex data
• SVM – Max-margin classifier, strong for high-dimensional data

🔹 Unsupervised Learning
• K-Means – Customer segmentation (requires K)
• KNN – Neighbor-based classification, compute-heavy

🔹 Dimensionality Reduction
• PCA – Reduces features, improves efficiency & noise handling

🔹 Other Important ML Algorithms (From the Cheat Sheet):
Naive Bayes → Text classification, sentiment analysis
Gradient Boosting / XGBoost → High-performance structured data models DBSCAN → Density-based clustering for arbitrary shapes
Hierarchical Clustering → Understanding data structure
Neural Networks (MLP) → Complex non-linear patterns
CNN → Image and vision tasks
RNN / LSTM → Sequence & time-series modeling
Transformers (BERT, GPT) → NLP, chatbots, translation
Autoencoders → Anomaly detection, compression

Real-World Uses
House prices & spam → Regression
Medical & credit scoring → Trees/Forests
Vision & text → SVM / CNN / Transformers
Segmentation → K-Means
Compression → PCA

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

💡 9 AI Skills to Master in 2026

It’s the infrastructure behind how smart businesses run today.

The gap between users and experts is closing fast.
But the gap between curiosity and capability is getting wider.

The difference comes down to skill, not just tools.

These are the nine that matter most in 2026.
Each one compounds the rest and turns AI from novelty into leverage.

1️⃣ Prompt Engineering to ask better questions and get sharper answers
🔢 AI Workflow Automation to connect apps and remove repetitive work
🔢 AI Agents to build systems that act without human input
🔢 Retrieval-Augmented Generation (RAG) to give models access to your own data
🔢 Fine-Tuning and Custom GPTs to train models for your goals and tone
🔢 Multimodal AI to mix text, image, and audio in one workflow
🔢 AI Video Generation to turn ideas into content without editing tools
🔢 AI Tool Stacking to link platforms into a single automated system
🔢 LLM Evaluation and Management to measure accuracy, cost, and performance

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

🚀 An End-to-End AI-Powered Stock Price Prediction & Investment Analysis App! 📈💡

🔹 LSTM Neural Networks

🔹 15+ Technical Indicators (TA Library)

🔹 Comprehensive Investment Scoring (8-Factor Model)

🔹 Future Price Forecasting

🔹 Advanced Metrics via Yahoo Finance API

🔹 Interactive Visualizations + Excel Export

💼 What This App Can Do

✅ Download real-time stock data using yFinance

✅ Automatically compute technical indicators (RSI, MACD, EMA, Bollinger Bands, ADX, OBV, ATR, Stoch, etc.)

✅ Train an LSTM model on historical price data

✅ Predict the next 5–30 days of stock prices

✅ Score a stock from 0–100 using:

Financial Performance

Valuation

Growth Potential

Risk

Momentum

Liquidity

Management Quality

Industry Strength

Each factor is normalized and weighted for an accurate, balanced investment score — ending with a rating like Strong Buy / Buy / Hold / Sell / Strong Sell.

📊 Tech Stack

Python

Streamlit

TensorFlow (LSTM)

Pandas / NumPy

TA Library

Yahoo Finance API

Matplotlib

🎯 Why I Built This

I wanted to create a tool that blends machine learning, technical analysis, and fundamental scoring into one dashboard — something retail traders, analysts, or finance students can use for learning, decision-making, and experimentation.

This project helped me strengthen skills in:

✔️ Time-Series Forecasting

✔️ Feature Engineering

✔️ Deep Learning Model Design

✔️ Data Visualization

✔️ Financial Data Analysis

✔️ UI/UX in Streamlit

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

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AI & Machine Learning & Deep Learning

May 9, 2026, 08:03 PM

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AI & Machine Learning & Deep Learning

May 2, 2026, 10:03 AM

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AI & Machine Learning & Deep Learning

May 2, 2026, 10:03 AM

https://t.me/GiftCallAlertsBot

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AI & Machine Learning & Deep Learning

Apr 22, 2026, 04:56 PM

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AI & Machine Learning & Deep Learning

Apr 22, 2026, 04:56 PM

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🌐 Welcome to Neural Nexus — Where Future AI Innovators Are Born!

Step into the world of Artificial Intelligence with Neural Nexus, Delhi’s premier AI institute — founded and run by a Kaggle Grandmaster.

Here, learning goes beyond theory — you’ll build real-world projects, master Machine Learning, Deep Learning, Generative AI, NLP, and Prompt Engineering, and gain the skills top companies are hiring for.

🚀 Why Neural Nexus?

• Learn directly from industry-recognized Kaggle Grandmasters
• Hands-on AI Labs & Live Industry Projects
• Career-focused mentorship & placement support
• Small batches for personalized learning

📍 Location: A-50, Block A, in front of Rais Print House, just
50 meters from Okhla Vihar Metro Station (Gate No. 2) —New Delhi’s
new hub for AI excellence.

✨ Join Neural Nexus — Master the Code of Tomorrow.

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AI & Machine Learning & Deep Learning

Apr 22, 2026, 04:56 PM

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AI & Machine Learning & Deep Learning

Apr 22, 2026, 04:56 PM

🚀 LLMs Are Overkill for 80% of Business Tasks — Enter SLMs

Most companies are burning cash on GPT-4 for workloads that don’t need billion-parameter reasoning.
Small Language Models (SLMs) are lean, specialized, and built for efficiency — delivering the same results at a fraction of the cost.


⚙️ Architecture Shift
LLMs:
General-purpose, monolithic transformers. Every query goes through full-scale compute — like using a Ferrari for grocery runs.
SLMs:
Compact, optimized models with task-specific embeddings, quantized weights (4–8 bit), and parallel inference pipelines.
They’re designed for edge deployment, faster throughput, and domain specialization.


💰 Cost Reality Check
Model Input ($/M tokens) Output ($/M tokens)
GPT-4 30.00 60.00
GPT-4.1-nano 0.10 0.40
Llama 3.2 (1B) 0.03 – 0.05 0.03 – 0.05
Custom Fine-Tuned SLM ≤ 0.02 ≤ 0.03
That’s up to 1000× cheaper per request — with lower latency and minimal hardware demands.


🏆 Where SLMs Win
✅ Customer support (handling 90%+ repetitive queries)
✅ Document classification and routing
✅ Sentiment or intent detection
✅ Code completion for niche languages
✅ On-device / IoT applications


🧠 Where LLMs Still Rule
🌐 Creative writing, multi-domain reasoning, and research tasks.


📊 Real-World Example: Invoice Processing
Before: GPT-4
After: Fine-tuned 1.3B-parameter SLM
• ⚡️ Latency: 2.0 s → 0.3 s
• 💸 Cost: -90%
• 🎯 Accuracy: +3% (from domain-specific fine-tuning)


🧩 Quick Start Guide
1. Identify repetitive or deterministic workflows
2. Measure current LLM token + latency costs
3. Test open-source SLMs (Phi-3, TinyLlama, Llama 3.2)
4. Fine-tune with your domain data (LoRA / QLoRA)
5. Deploy locally, on-prem, or at the edge


💡 The future of AI isn’t “bigger.” It’s smarter.
Specialized SLMs are the next step — smaller, faster, cheaper, and purpose-built for the real world.

AI & Machine Learning & Deep Learning

Apr 22, 2026, 04:56 PM

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5 Situations Where You Should Avoid Linear Regression (Save 50% of Modeling Time)

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AI & Machine Learning & Deep Learning

Apr 22, 2026, 04:56 PM

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AI & Machine Learning & Deep Learning

Apr 4, 2026, 08:06 PM

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Outlier Detection in Python-Manning

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AI & Machine Learning & Deep Learning

Apr 4, 2026, 08:06 PM

🚫 5 Situations Where You Should Avoid Linear Regression (Save 50% of Modeling Time)

🟢 1. Non-Linear Relationships
Linear regression can only model straight-line relationships. If your target depends on curves, thresholds, or complex shapes, results will be misleading.
👉 Better Options: Polynomial Regression | Gradient Boosting | Neural Networks

🟢 2. Strong Feature Interactions
Linear regression doesn’t capture feature interactions unless you manually engineer them. Real-world data often hides intricate dependencies.
👉 Better Options: Random Forest | Neural Networks | SVM (with kernels)

🟢 3. Categorical-Heavy or Sparse Data
One-hot encoding for categoricals makes feature space explode and loses signal. Linear regression struggles in such setups.
👉 Better Options: CatBoost | LightGBM (native categorical handling)

🟢 4. Complex Decision Boundaries
If your problem involves non-linear thresholds or region-specific behavior, a straight line just won’t cut it.
👉 Better Options: SVM with RBF | Gradient Boosting | Neural Networks

🟢 5. Heteroscedasticity (Non-Constant Variance)
Linear regression assumes constant error variance. If errors grow/shrink with predictors, your model and confidence intervals will break.
👉 Better Options: Generalized Least Squares | Tree-Based Models | Gradient Boosting

⚡️ Moral: Linear regression is powerful, but only when its assumptions hold. Recognize when to drop it early — and save yourself hours of wasted modeling.

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