Data Science & ML
Python, statistics, machine learning, deep learning, NLP, and model deployment.
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Topics in this track
Python for Data Science
BeginnerNumPy arrays, Pandas DataFrames, Matplotlib & Seaborn plots, Jupyter notebooks.
Statistics & Probability
BeginnerDescriptive stats, probability distributions, hypothesis testing, p-values, confidence intervals.
SQL for Data Analysis
BeginnerQueries, joins, aggregations, window functions, CTEs, and performance tuning.
Machine Learning Basics
IntermediateSupervised & unsupervised learning, regression, classification, clustering, model evaluation metrics.
Deep Learning
IntermediateNeural networks, CNNs, RNNs, transformers, backpropagation, PyTorch and TensorFlow.
Natural Language Processing
IntermediateText preprocessing, embeddings, attention mechanism, BERT, GPT, and fine-tuning LLMs.
Data Engineering & Pipelines
IntermediateETL pipelines, Apache Spark, Airflow, data warehouses (BigQuery, Redshift), dbt.
MLOps & Model Deployment
AdvancedModel versioning (MLflow), serving (FastAPI, TorchServe), monitoring, A/B testing, drift detection.