nlp-processing

Trains intelligent agents to learn optimal behaviors through interaction with environments using reinforcement learning techniques.

Processes and analyzes images using deep learning models for classification, detection, and visual understanding tasks.

Builds, trains, and evaluates machine learning models for classification, regression, and clustering using scikit-learn's powerful algorithms and techniques.

Generates interactive data visualizations and performs exploratory data analysis using Matplotlib, Seaborn, Plotly, and other visualization tools.

Performs time series analysis using ARIMA, SARIMA, Prophet, detecting trends, seasonality, and anomalies for precise temporal predictions.

Performs rigorous statistical analysis using Python's SciPy, enabling hypothesis testing, A/B testing, and data validation across various statistical methods.

Enables efficient Python programming for data science, covering fundamentals, data manipulation, and advanced library usage with NumPy and Pandas.

Develops neural network models using PyTorch and TensorFlow for advanced machine learning tasks like image classification, NLP, and pattern recognition.

Optimizes machine learning models through techniques like quantization, pruning, hyperparameter tuning, and AutoML for improved performance and efficiency.

Builds scalable data pipelines and infrastructure using Apache Spark, Airflow, and big data processing techniques for efficient ETL workflows.