Found this - AI Builders, pay attention.
A curated marketplace just launched where AI builders list their systems and get paid - setup fee + monthly recurring. No sales, no client chasing. They handle everything, you just build.
100% free to join. No fees, no subscription, no hidden costs. They only take 20% when you earn - on setup fee and recurring. That's it.
Accepted builders are earning from day one. Spots are limited by design.
Takes 5 minutes to apply. You'll need a 90-second video of your system in action.
→ brainlancer.com
Daily updates from the CEO: https://www.linkedin.com/in/soner-catakli/
Follow, like & share in "your network" - these guys are building something seriously worth watching.
PS: First systems go live tomorrow. Builders who join early get the best positioning... investor-backed marketing means they bring the clients to you.
A curated marketplace just launched where AI builders list their systems and get paid - setup fee + monthly recurring. No sales, no client chasing. They handle everything, you just build.
100% free to join. No fees, no subscription, no hidden costs. They only take 20% when you earn - on setup fee and recurring. That's it.
Accepted builders are earning from day one. Spots are limited by design.
Takes 5 minutes to apply. You'll need a 90-second video of your system in action.
→ brainlancer.com
Daily updates from the CEO: https://www.linkedin.com/in/soner-catakli/
Follow, like & share in "your network" - these guys are building something seriously worth watching.
PS: First systems go live tomorrow. Builders who join early get the best positioning... investor-backed marketing means they bring the clients to you.
❤4
On GitHub, a repository has been curated comprising over 500 valuable services designed for daily tasks. 📂🛠️
The collection includes projects compatible with various operating systems, smartphones, web browsers, and torrent clients, alongside tools for productivity, software development, design, and content management. 🖥️📱🎨
https://github.com/Furthir/awesome-useful-projects?tab=readme-ov-file#creative 🔗
The collection includes projects compatible with various operating systems, smartphones, web browsers, and torrent clients, alongside tools for productivity, software development, design, and content management. 🖥️📱🎨
https://github.com/Furthir/awesome-useful-projects?tab=readme-ov-file#creative 🔗
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🚀 Thrilled to announce a major milestone in our collective upskilling journey! 🌟
I am incredibly excited to share a curated ecosystem of high-impact resources focused on Machine Learning and Artificial Intelligence. By consolidating a comprehensive library of PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
I am incredibly excited to share a curated ecosystem of high-impact resources focused on Machine Learning and Artificial Intelligence. By consolidating a comprehensive library of PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
2❤18👍10👾1
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://t.iss.one/CodeProgrammer✅
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
https://t.iss.one/CodeProgrammer
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ROC Plot: Clearly explained 🔥
💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).
🤔 Specificity and Sensitivity
The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.
Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.
🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.
😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).
A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.
📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.
Interested in AI Engineering?
https://t.iss.one/CodeProgrammer✅
💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).
🤔 Specificity and Sensitivity
The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.
Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.
🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.
😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).
A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.
📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.
Interested in AI Engineering?
https://t.iss.one/CodeProgrammer
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🔥 Precision-Recall plot: Clearly explained
🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.
🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.
The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.
🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.
💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.
🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.
📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.
https://t.iss.one/CodeProgrammer
🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.
🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.
The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.
🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.
💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.
🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.
📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.
https://t.iss.one/CodeProgrammer
❤7
30 Days with Python — this is a step-by-step guide to learning the Python programming language over 30 days.
Completing this task may take more than 100 days, so proceed at your own pace.
Repo: https://github.com/Asabeneh/30-Days-Of-Python
https://t.iss.one/CodeProgrammer🌟
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Completing this task may take more than 100 days, so proceed at your own pace.
Repo: https://github.com/Asabeneh/30-Days-Of-Python
https://t.iss.one/CodeProgrammer
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Top Machine Learning Algorithms You Should Actually Understand 🤖
Most individuals merely memorize algorithms. In contrast, professional engineers comprehend the appropriate application contexts and the underlying reasons for algorithmic failure.
This is not a simple list; it is an explanation of how Machine Learning (ML) functions in practical environments. 🛠
1️⃣ ➤ Linear Regression 📈
This serves as the foundational starting point.
The process involves fitting a straight line to data to address a fundamental question: how does the input affect the output?
↳ Example: Predicting house prices based on size.
This method performs effectively when relationships are linear but fails when patterns become non-linear.
2️⃣ ➤ Logistic Regression 📊
Despite its nomenclature, this algorithm is utilized for classification tasks.
It predicts probabilities rather than continuous values.
↳ Example: Distinguishing between spam and non-spam emails.
A thorough understanding of this method equips one with knowledge of decision boundaries.
3️⃣ ➤ Decision Trees 🌳
Conceptualize this as a flowchart.
Data is split based on specific conditions until a final decision is reached.
↳ Example: Loan approval systems.
While easy to interpret, this approach is prone to overfitting.
4️⃣ ➤ Random Forest 🌲
This involves not a single tree, but hundreds of trees voting collectively.
This ensemble approach significantly reduces overfitting.
↳ Example: Fraud detection systems.
It serves as a very robust baseline in real-world systems.
5️⃣ ➤ K Nearest Neighbors (KNN) 🔍
There is no explicit training phase.
The system simply compares new data points with the nearest existing data points.
↳ Example: Recommendation systems.
While simple, it becomes computationally slow at scale.
6️⃣ ➤ K Means Clustering 🎯
This is a form of unsupervised learning.
It groups similar data points into distinct clusters.
↳ Example: Customer segmentation.
This method is effective only if the clusters are well-separated.
7️⃣ ➤ Support Vector Machine (SVM) ⚖️
This algorithm identifies the optimal boundary between different classes.
It functions by maximizing the margin between classes.
↳ Example: Text classification.
While powerful, it lacks scalability for very large datasets.
8️⃣ ➤ Naive Bayes 📧
This method is based on probability theory.
It operates under the assumption that features are independent.
↳ Example: Email filtering.
It remains surprisingly effective for straightforward problems.
9️⃣ ➤ XGBoost 🏆
This algorithm is a consistent winner in competitions for a specific reason.
It sequentially improves weak models to create a strong predictor.
↳ Example: Structured data problems.
If uncertainty exists regarding which model to utilize, this is an excellent starting point.
🔟 ➤ Neural Networks 🧠
This constitutes the foundation of deep learning.
It is capable of handling highly complex patterns.
↳ Example: Image, text, and speech processing.
It requires substantial data, computational resources, and fine-tuning.
How They Fit Together 🧩
Simple Data → Linear / Logistic
Structured Data → Random Forest / XGBoost
Similarity Based → KNN
Unlabeled Data → K Means
High Dimension → SVM
Complex Patterns → Neural Networks
Real Insight 💡
Most real-world systems do not employ every available algorithm.
They rely on:
→ Strong baselines
→ High-quality data
→ Proper evaluation
They do not depend on overly complex models.
TL;DR 📝
Start simple.
Understand deeply.
Then scale complexity.
This is the methodology employed by professional Machine Learning engineers.
Most individuals merely memorize algorithms. In contrast, professional engineers comprehend the appropriate application contexts and the underlying reasons for algorithmic failure.
This is not a simple list; it is an explanation of how Machine Learning (ML) functions in practical environments. 🛠
1️⃣ ➤ Linear Regression 📈
This serves as the foundational starting point.
The process involves fitting a straight line to data to address a fundamental question: how does the input affect the output?
↳ Example: Predicting house prices based on size.
This method performs effectively when relationships are linear but fails when patterns become non-linear.
2️⃣ ➤ Logistic Regression 📊
Despite its nomenclature, this algorithm is utilized for classification tasks.
It predicts probabilities rather than continuous values.
↳ Example: Distinguishing between spam and non-spam emails.
A thorough understanding of this method equips one with knowledge of decision boundaries.
3️⃣ ➤ Decision Trees 🌳
Conceptualize this as a flowchart.
Data is split based on specific conditions until a final decision is reached.
↳ Example: Loan approval systems.
While easy to interpret, this approach is prone to overfitting.
4️⃣ ➤ Random Forest 🌲
This involves not a single tree, but hundreds of trees voting collectively.
This ensemble approach significantly reduces overfitting.
↳ Example: Fraud detection systems.
It serves as a very robust baseline in real-world systems.
5️⃣ ➤ K Nearest Neighbors (KNN) 🔍
There is no explicit training phase.
The system simply compares new data points with the nearest existing data points.
↳ Example: Recommendation systems.
While simple, it becomes computationally slow at scale.
6️⃣ ➤ K Means Clustering 🎯
This is a form of unsupervised learning.
It groups similar data points into distinct clusters.
↳ Example: Customer segmentation.
This method is effective only if the clusters are well-separated.
7️⃣ ➤ Support Vector Machine (SVM) ⚖️
This algorithm identifies the optimal boundary between different classes.
It functions by maximizing the margin between classes.
↳ Example: Text classification.
While powerful, it lacks scalability for very large datasets.
8️⃣ ➤ Naive Bayes 📧
This method is based on probability theory.
It operates under the assumption that features are independent.
↳ Example: Email filtering.
It remains surprisingly effective for straightforward problems.
9️⃣ ➤ XGBoost 🏆
This algorithm is a consistent winner in competitions for a specific reason.
It sequentially improves weak models to create a strong predictor.
↳ Example: Structured data problems.
If uncertainty exists regarding which model to utilize, this is an excellent starting point.
🔟 ➤ Neural Networks 🧠
This constitutes the foundation of deep learning.
It is capable of handling highly complex patterns.
↳ Example: Image, text, and speech processing.
It requires substantial data, computational resources, and fine-tuning.
How They Fit Together 🧩
Simple Data → Linear / Logistic
Structured Data → Random Forest / XGBoost
Similarity Based → KNN
Unlabeled Data → K Means
High Dimension → SVM
Complex Patterns → Neural Networks
Real Insight 💡
Most real-world systems do not employ every available algorithm.
They rely on:
→ Strong baselines
→ High-quality data
→ Proper evaluation
They do not depend on overly complex models.
TL;DR 📝
Start simple.
Understand deeply.
Then scale complexity.
This is the methodology employed by professional Machine Learning engineers.
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Thrilled to announce a major milestone in our professional development journey! 🚀 We are excited to unveil a strategic, curated ecosystem of 800+ high-impact Computer Science learning modules from industry titans like MIT, Harvard, and other top-tier global institutions. 🎓✨
This centralized repository represents a powerful synergy of knowledge, meticulously organized by key verticals including algorithms, ML, networks, and robotics, ensuring seamless alignment with your career growth objectives. 📈💡
Say goodbye to fragmented roadmaps and hello to a ready-made, optimized pathway for Computer Science excellence—empowering you to leverage these resources without the need for manual assembly or redundant effort. ⚙️🌟
Unlock your full potential and scale your expertise today:
⛓️ Strategic Resource Hub:
https://github.com/Developer-Y/cs-video-courses
#ContinuousLearning #GrowthMindset #TechExcellence #CareerStrategy #Innovation
This centralized repository represents a powerful synergy of knowledge, meticulously organized by key verticals including algorithms, ML, networks, and robotics, ensuring seamless alignment with your career growth objectives. 📈💡
Say goodbye to fragmented roadmaps and hello to a ready-made, optimized pathway for Computer Science excellence—empowering you to leverage these resources without the need for manual assembly or redundant effort. ⚙️🌟
Unlock your full potential and scale your expertise today:
⛓️ Strategic Resource Hub:
https://github.com/Developer-Y/cs-video-courses
#ContinuousLearning #GrowthMindset #TechExcellence #CareerStrategy #Innovation
❤5🔥4👍2
cnn-vgg19-model-tranform-learning.pdf
7 MB
Excited to share latest Deep Learning project: Faulty Solar Panel Detection using CNN + VGG19! 🚀
☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.
💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).
📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered
🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)
📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589
🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).
🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib
https://t.iss.one/CodeProgrammer🔰
☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.
💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).
📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered
🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)
📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589
🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).
🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib
https://t.iss.one/CodeProgrammer
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🔥 Google Colab has added the option of retraining 500+ open-source neural networks
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
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This FREE AI engineering roadmap
Will teach you more in 2026 than a 4-year college degree...
Here's the exact 6-step blueprint 👇
1️⃣STEP 1: Python Programming Foundations
Harvard CS50's Python Programming Course : https://lnkd.in/ePCvXwXP
→ Build unshakeable coding fundamentals
→ 6-8 weeks to Python mastery
2️⃣ STEP 2: Machine Learning Foundations
Stanford CS229: Machine Learning : https://lnkd.in/eEsdZbVc
→ Learn from the legends at Stanford
→ Master ML algorithms and math foundations
→ 10-12 weeks of pure gold
3️⃣ STEP 3: Deep Learning Mastery
Fast.ai Practical Deep Learning : https://course.fast.ai/
→ Jeremy Howard's legendary course
→ Build real AI applications from day 1
→ 8-10 weeks of hands-on projects
4️⃣ STEP 4: Natural Language Processing
Stanford CS224N/Ling284 : https://lnkd.in/ebQZ5_T3
→ Master transformers and language models
→ The foundation of ChatGPT and GPT-4
→ 10-12 weeks of cutting-edge NLP
5️⃣ STEP 5: Generative AI Introduction
Microsoft Generative AI for Beginners
: https://lnkd.in/ewsH8gMT
→ 21 Lessons teaching everything you need to know to start building Generative AI applications
→ 6-8 weeks of creative AI
6️⃣ STEP 6: Large Language Models
LLM University by Cohere : https://cohere.com/llmu
→ Fine-tune and deploy production LLMs
→ Build and deploy LLM models
→ 6-8 weeks of enterprise-level skills
https://t.iss.one/CodeProgrammer✅
Will teach you more in 2026 than a 4-year college degree...
Here's the exact 6-step blueprint 👇
1️⃣STEP 1: Python Programming Foundations
Harvard CS50's Python Programming Course : https://lnkd.in/ePCvXwXP
→ Build unshakeable coding fundamentals
→ 6-8 weeks to Python mastery
2️⃣ STEP 2: Machine Learning Foundations
Stanford CS229: Machine Learning : https://lnkd.in/eEsdZbVc
→ Learn from the legends at Stanford
→ Master ML algorithms and math foundations
→ 10-12 weeks of pure gold
3️⃣ STEP 3: Deep Learning Mastery
Fast.ai Practical Deep Learning : https://course.fast.ai/
→ Jeremy Howard's legendary course
→ Build real AI applications from day 1
→ 8-10 weeks of hands-on projects
4️⃣ STEP 4: Natural Language Processing
Stanford CS224N/Ling284 : https://lnkd.in/ebQZ5_T3
→ Master transformers and language models
→ The foundation of ChatGPT and GPT-4
→ 10-12 weeks of cutting-edge NLP
5️⃣ STEP 5: Generative AI Introduction
Microsoft Generative AI for Beginners
: https://lnkd.in/ewsH8gMT
→ 21 Lessons teaching everything you need to know to start building Generative AI applications
→ 6-8 weeks of creative AI
6️⃣ STEP 6: Large Language Models
LLM University by Cohere : https://cohere.com/llmu
→ Fine-tune and deploy production LLMs
→ Build and deploy LLM models
→ 6-8 weeks of enterprise-level skills
https://t.iss.one/CodeProgrammer
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Today, the public mint for Lobsters on TON goes live on Getgems 🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
❤2
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
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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
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