Hugging Face Transformers in 2026: NLP, Fine-Tuning and Interview Questions
Hugging Face Transformers tutorial covering the v5 API, fine-tuning with LoRA, NLP pipelines, and the most common interview questions asked in data science roles in 2026.
Data Science & ML
DATA
Comprehensive Data Science and Machine Learning curriculum with Python as the main language. From data manipulation with Pandas and NumPy to implementing Deep Learning models with TensorFlow/Keras, through classic ML with Scikit-Learn. Also includes MLOps skills to deploy and maintain models in production with Docker, FastAPI and cloud platforms.
What you'll learn
Modern Python with object-oriented programming and best practices
Data manipulation with Pandas, NumPy and SQL (BigQuery)
Visualization with Matplotlib, Seaborn and Plotly
Descriptive and inferential statistics with Statsmodel
Machine Learning with Scikit-Learn and XGBoost (regression, classification, clustering)
Deep Learning with TensorFlow and Keras (CNN, RNN, Transformers)
NLP and GenAI with Hugging Face, LangChain and LLMs (GPT, Gemini)
MLOps with MLflow, Docker, FastAPI and Streamlit
Development environments: Jupyter, Google Colab
Cloud deployment with Google Compute, Cloud Storage and GPU
Key topics to master
The most important concepts to understand this technology and ace your interviews
1
Python: types, data structures, OOP, decorators, generators, context managers
2
NumPy: arrays, broadcasting, indexing, vectorized operations, linear algebra
3
Pandas: DataFrames, Series, indexing, groupby, merge, pivot, time series
4
SQL: SELECT, JOIN, GROUP BY, window functions, CTEs, query optimization
5
Visualization: Matplotlib (figures, axes, subplots), Seaborn (statistical plots), Plotly (interactive)
6
Statistics: distributions, hypothesis testing, confidence intervals, regression
7
Feature Engineering: encoding, scaling, feature selection, feature creation
8
Supervised ML: linear/logistic regression, trees, Random Forest, XGBoost, metrics
9
Unsupervised ML: K-Means, hierarchical clustering, PCA, t-SNE
10
ML Pipeline: train/test split, cross-validation, hyperparameter tuning, overfitting
11
Deep Learning: perceptrons, backpropagation, activation functions, optimizers, loss functions
12
CNN: convolutions, pooling, architectures (ResNet, VGG), transfer learning
13
RNN/LSTM: sequences, vanishing gradient, attention mechanism, Transformers
14
NLP: tokenization, embeddings, word2vec, BERT, LLM fine-tuning
15
MLOps: versioning (MLflow), containerization (Docker), API (FastAPI), monitoring
16
Cloud: Google Cloud (Compute, Storage, BigQuery), GPU training, Vertex AI
17
AI Ethics: bias, explainability (SHAP, LIME), fairness, GDPR
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