Skills

Technical and analytical skills used to design, build, and deploy machine learning and deep learning methods.

💻 Programming

  • Python
  • JavaScript
  • SQL
  • Java

🌐 Web & Backend

  • Django
  • Flask
  • REST APIs
  • Dash

📊 Machine Learning Libraries & Visualization

  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • Plotly

🔧 Machine Learning Models

  • Linear Regression
  • Logistic Regression
  • Random Forest Regressor/Classifier
  • Ridge Regression
  • Lasso Regression
  • Decision Tree
  • Random Forest
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • K-Means Clustering
  • Naive Bayes
  • Gradient Boosting (GBM)
  • XGBoost
  • Bagging Method
  • Stacking
  • Blending
  • Gradient Descent
  • Principal Component Analysis (PCA)

🤖 Generative AI & LLM Engineering

  • Large Language Models (LLMs)
  • Hugging Face Transformers
  • Ollama
  • Vector Databases
  • Prompt Engineering
  • Fine-tuning LLMs
  • Retrieval-Augmented Generation (RAG)
  • Tokenization & Embeddings
  • OpenAI API
  • LangChain

🧠 Deep Learning Libraries & Frameworks

  • TensorFlow
  • PyTorch
  • Keras

🌐 Deep Learning Models

  • Artificial Neural Network (ANN)
  • Deep Neural Networks (DNN)
  • Convolutional Neural Network (CNN)
  • Recurrent Neural Networks (RNN)
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Units (GRU)

☁️ Big Data Frameworks & Processing

  • Hadoop
  • Spark
  • PySpark
  • Hive

🐳 Containerization & Orchestration / DevOps

  • Docker
  • Kubernetes

☁️ Cloud & Storage

  • AWS S3
  • Amazon EC2

🛠️ Tools

  • Power BI
  • Tableau
  • Excel
  • Google Colab
  • Jupyter Notebook
  • Visual Studio
  • GitHub