Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Network:Machine Learning with Python India5 049 Education2 404...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 819 subscribers, ranking 2 404 in the Education category and 5 049 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 819 subscribers.

According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.60%. Within the first 24 hours after publication, content typically collects 2.50% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 767 views. Within the first day, a publication typically gains 1 695 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 06 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 819

Subscribers

+924 hours

+587 days

+7730 days

1 767

Post views

~ 1 69524 hours

~ 2 25748 hours

2.60%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

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https://t.me/MachineLearning9

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Hugging Face has literally gathered all the key "secrets". 🤔 It's important to understand the evaluation of large language models. 📊 While you're working with language models: > training or retraining your models, 🔄 > selecting a model for a task, 🎯 > or trying to understand the current state of the field, 🌍 the question almost inevitably arises: how to understand that a model is good? ❓ The answer is quality evaluation. It's everywhere: > leaderboards with model ratings, 🏆 > benchmarks that supposedly measure reasoning, 🧠 > knowledge, coding or mathematics, 💻 > articles with claimed new best results. 📈 But what is evaluation actually? 🤷 And what does it really show? 🔍 This guide helps to understand everything. 📚 What is model evaluation all about 🤖 Basic concepts of large language models for understanding evaluation 🏗️ Evaluation through ready-made benchmarks 📏 Creating your own evaluation system 🔧 The main problem of evaluation ⚠️ Evaluation of free text 📝 Statistical correctness of evaluation 📉 Cost and efficiency of evaluation 💰

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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Your 1:3 RR keeps failing for 7 days? 📊 ElitePIP “Entry Filters”: 3 checks before you click. Get it: Join Filters #ad 📢 InsideAd

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this channel for sale 500$ https://t.me/DataScienceN contact me @HusseinSheikho

67 817

Overfitting and Generalization in Machine Learning My ML model had 100% accuracy. And was completely useless. That's not a paradox; that's overfitting. The model didn't learn. It memorized. Here's the mathematical core most tutorials skip: E[loss] = Bias² + Variance + σ² → Bias² = too simple → Underfitting → Variance = too complex → Overfitting → σ² = irreducible → always there What this actually means in practice: → A degree-9 polynomial on 6 data points hits R² = 1.0 and oscillates wildly between them → A linear model on sine-wave data has near-zero variance — but massive bias → The optimal model isn't the simplest. Not the most complex. It's the one minimizing Bias² + Variance And the generalization gap? Formally defined as: gen_gap(f) = R(f) − R_emp(f) When this value is ≫ 0, your model is learning noise, not signal. The fix isn't "collect more data and hope." The fix is regularization, which I derive fully in my paper: L1, L2, Dropout, and Early Stopping, all from first principles. Which regularization strategy do you use most and why?

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Most AI engineers never fully understood the maths behind what they build! 🤯🧮 This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨ Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗 What it covers: - Vectors, linear algebra, calculus, and optimization 📐📉 - Classical machine learning and deep learning 🤖 - Transformer architectures and LLMs 🦄 - Efficient architectures, quantization, and distillation ⚡️ - CUDA, GPU programming, and SIMD 🚀 - AI inference and deployment 🌐 Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗 Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗

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🧮 $40/day × 30 days = $1,200/month. That's what my students average. From their phone. In 10 minutes a day. No degree needed. No investment knowledge required. Just Copy & Paste my moves. I'm Tania, and this is real. 👉 Join for Free, Click here #ad 📢 InsideAd

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Your 1:3 RR keeps failing for 7 days? 📊 ElitePIP “Entry Filters”: 3 checks before you click. Get it: Join Filters #ad 📢 InsideAd

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🔖 A huge repository of resources on Data Science 📈 Awesome DataScience — a structured list of open-source data, datasets, libraries, and tutorials for solving real-world problems. 🛠️ It's useful for both beginners and those already familiar with the field — you'll find something new here. 🌱 ⛓️ Link to GitHub: https://github.com/academic/awesome-datascience 🔗 tags: #DataScientist 🤖 #AI 🧠 #TechCommunity 🌐 #GrowthMindset 📈 #OpenSource 🏆 ▶️ https://t.me/CodeProgrammer 👨‍💻

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💡 Level Up Your IT Career in 2026 – For FREE Areas covered: #Python #AI #Cisco #PMP #Fortinet #AWS #Azure #Excel #CompTIA #ITIL #Cloud + more 🔗 Download each free resource here: • Free Courses (Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS) 👉https://bit.ly/4ejSFbz • IT Certs E-book 👉 https://bit.ly/42y8owh • IT Exams Skill Test 👉 https://bit.ly/42kp7Dv • Free AI Materials & Support Tools 👉 https://bit.ly/3QEfWek • Free Cloud Study Guide 👉https://bit.ly/4u8Zb9r 📲 Need exam help? Contact admin: wa.link/40f942 💬 Join our study group (free tips & support): https://chat.whatsapp.com/K3n7OYEXgT1CHGylN6fM5a

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Here are the 25 ML feature engineering techniques

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Leaked 7‑minute pre‑trade checklist: I stole this after watching 12 losing trades in a row… and it flipped my next 3 sessions. 1) mark the 2 liquidity levels first, 2) wait for the 15m sweep, 3) confirm 1 candle shift, 4) set 1R auto‑rule, 5) log the screenshot before entry. Want the template? Join Checklist #ad 📢 InsideAd

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Register for the FREE Python Demo Session! 📅 Date: 30 April 2026 ⏰ Time: 7:30 PM 🔗 Zoom Link: https://us06web.zoom.us/meeting/register/HSOTmzzpTkGIGm9C9oGbaA Everyone is welcome!

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In the last 14 days, 63% of retail trades were stopped out… but not because the market “hates” you - it’s the plan that’s missing. 𝗘𝗟𝗜𝗧𝗘𝗣𝗜𝗣 𝗘𝗠𝗣𝗜𝗥𝗘 helps you trade calmer with daily setups, clear levels, and 1-on-1 support when you feel stuck. Less panic. More structure. Request access here: Join #ad 📢 InsideAd

67 817

I watched 3 traders blow accounts in 7 days… for the same 1 mistake. Here’s the fix. Leaked Checklist (ELITEPIP EMPIRE): - Spot the 2 news traps that fake out “easy” entries - Use the 3-step setup filter to skip low-quality signals - Lock risk with a 1-minute position plan - Read market bias in under 5 minutes Join & level up | Get access #ad 📢 InsideAd