Sagar Vishwakarma

Myself Sagar | Full stack developer by day & night 🌃💻. I Always on the lookout for the next challenge to crack. | Let's build something amazing together! 💡

[Sagar Vishwakarma @dev_sagar_7]

Learned RAG basics and how LLMs use real data

📄 Document loaders
🧠 Embeddings
🧲 Vector stores
🔍 Retrieval with context

Building smarter LLM apps 🤖

GitHub 🔗 https://t.co/r7IgwUJ7nD

#RAG #LLM #GenAI #AI #MachineLearning #DeepLearning #Python

[Sagar Vishwakarma @dev_sagar_7]

LLM basics finally clear

🤖 What is an LLM
✍️ Prompting
🌡️ Temperature
🔢 Tokens
🔌 HuggingFace and OpenRouter

Learning GenAI step by step 🧠

GitHub 🔗 https://t.co/r7IgwUJ7nD

#LLM #GenAI #AI #MachineLearning #DeepLearning #Python

[Sagar Vishwakarma @dev_sagar_7]

Learning NLP from the ground up :

Tokenization
Embeddings

Built a small sentence similarity project and understood how text turns into meaningful vectors ✨

Code here 🔗 https://t.co/pDP3jEZpgM

#NLP #AI #MachineLearning #DeepLearning #Python #Developers

[Sagar Vishwakarma @dev_sagar_7]

Attention feels powerful and now I understand why they outperform RNNs

Learning HuggingFace basics made text tasks feel simple

Feeling confident with Transformer intuition
#Transformers #Attention #DeepLearning #AI

GitHub:https://t.co/pDP3jEZpgM

[Sagar Vishwakarma @dev_sagar_7]

GitHub : https://t.co/pDP3jEZpgM

[Sagar Vishwakarma @dev_sagar_7]

Learning CNNs boosted my confidence in computer vision tasks

From convolution filters to feature maps and pooling the entire pipeline finally makes sense
Running inference with MobileNet and ResNet was the real moment where everything clicked.

#DeepLearning #CNN #AI #ML

[Sagar Vishwakarma @dev_sagar_7]

Learning CNNs boosted my confidence in computer vision tasks

From convolution filters to feature maps and pooling the entire pipeline finally makes sense
Running inference with MobileNet and ResNet was the real moment where everything clicked.

#DeepLearning #CNN #AI #ML

[Sagar Vishwakarma @dev_sagar_7]

GitHub project: https://t.co/pDP3jEZpgM

[Sagar Vishwakarma @dev_sagar_7]

Built a simple feed-forward neural network today and trained it on a dummy dataset. Learned how tensors, model layers, loss functions and the full training loop work together in PyTorch. A solid step toward mastering deep learning.

#DeepLearning #PyTorch #AI #ML

[Sagar Vishwakarma @dev_sagar_7]

Built a simple feed-forward neural network today and trained it on a dummy dataset. Learned how tensors, model layers, loss functions and the full training loop work together in PyTorch. A solid step toward mastering deep learning.

#DeepLearning #PyTorch #AI #ML

[Sagar Vishwakarma @dev_sagar_7]

Built a small Payment Fraud Detection model today 🔍💳
Cleaned data, handled imbalance, trained models and tested fraud predictions.

Code here: https://t.co/TRNVsPrgUE

#MachineLearning #AI #FraudDetection

[Sagar Vishwakarma @dev_sagar_7]

GitHub: https://t.co/I8w6jRAzP9

#MachineLearning #AI #DiabetesPrediction

[Sagar Vishwakarma @dev_sagar_7]

Just finished my Diabetes Prediction ML project.

Cleaned data, handled zero values, scaled features and trained a Logistic Regression model. Evaluated using accuracy, precision, recall, F1 and confusion matrix.
Reached around 74% accuracy a solid baseline.
#MachineLearning #AI

[Sagar Vishwakarma @dev_sagar_7]

Just finished my Diabetes Prediction ML project.

Cleaned data, handled zero values, scaled features and trained a Logistic Regression model. Evaluated using accuracy, precision, recall, F1 and confusion matrix.
Reached around 74% accuracy a solid baseline.
#MachineLearning #AI

[Sagar Vishwakarma @dev_sagar_7]

Learnt how to evaluate ML models using accuracy, F1 and MSE.

Also understood how precision, recall and F1 are connected to the confusion matrix.

Helpful to see the exact mistakes a model makes.

#MachineLearning #AI #ModelEvaluation #DataScience

GitHub: https://t.co/mdmtmH4mwz

[Sagar Vishwakarma @dev_sagar_7]

Completed Clustering and K-Means today.

Learned how clusters form, how centroids move, inertia, elbow method and how to visualize cluster groups with simple plots.

Also built a mini K-Means project.

#MachineLearning #AI #Clustering #KMeans

GitHub: https://t.co/mdmtmH4mwz

[Sagar Vishwakarma @dev_sagar_7]

Finished learning Decision Trees and Logistic Regression and built a Titanic Survival Prediction model using Scikit learn.

Touched 80 percent accuracy and learned how to evaluate a classifier.

Project code here : https://t.co/I8w6jRAzP9

#MachineLearning #AI #Python #MLProject

[Sagar Vishwakarma @dev_sagar_7]

Built a small house price prediction project using python and linear regression.

Cleaned the data trained the model and tested it on unseen rows.

Good first step in applied ML.

Check the project : https://t.co/cxVQ4FmMON

#python #ai #ml #datascience

[Sagar Vishwakarma @dev_sagar_7]

Supervised Learning finally clicked today
Covered:

regression classification,
linear regression
logistic regression
KNN
model training prediction evaluation
loss functions

Feeling more confident after running everything with real examples
On to the next day
#AI #Python

[Sagar Vishwakarma @dev_sagar_7]

Today I completed one of the most important parts of AIML which is data preprocessing.

I worked on handling missing values scaling encoding outlier detection train test split and basic pipeline building.

It feels great to understand how much clean data improves the model.

[Sagar Vishwakarma @dev_sagar_7]

Working on my AIML fundamentals and broke everything into clean day wise sections

Day 1 Python Fundamentals
Day 2 NumPy and Pandas
Day 3 Math for ML
Day 4 Data Visualization

All notes are on my GitHub if you want to check them out.
Link : https://t.co/Pvr23ujpBs

[Sagar Vishwakarma @dev_sagar_7]

Wrapped up my Data Visualization study for AIML.
From preprocessing to interactive dashboards, and from Matplotlib to Plotly — learned how a clean chart can explain what pages of text can’t.
Good visuals = clear thinking.

#AIML #LearnInPublic #GenAi

[Sagar Vishwakarma @dev_sagar_7]

Completed my Calculus module for AI and ML.
Feeling strong with derivatives, gradients and optimization.
Ready for the next step in my AI learning journey.

#AIML #LearnInPublic