Data Science and Machine Learning
Artificial intelligence is changing the world — be a part of it. A course from leading data scientists: from math to model deployment.
Why Data Science and ML?
Hottest Field in Tech
AI/ML is transforming every industry — demand for data scientists has grown 5x in 3 years.
Math to Production
Full-cycle course: from linear algebra and statistics to deploying neural networks.
Endless Applications
Healthcare, finance, e-commerce, self-driving cars — AI is everywhere.
Salary Skyrockets
Data Scientists earn from $3,000 with experience. Senior roles reach $10,000+.
Who is this course for
For beginners with basic programming skills who want to become a Data Scientist or ML Engineer.
Technologies You'll Master
Python
Primary language for data science and ML.
Pandas
Data manipulation and analysis library.
NumPy
Numerical computing for large arrays.
Scikit-learn
Classic ML algorithms made simple.
PyTorch
Deep learning framework for neural networks.
Jupyter
Interactive notebooks for data exploration.
Matplotlib
Data visualization and plotting library.
XGBoost
Gradient boosting for high-performance ML.
Flask
Deploy ML models as web services.
Docker
Package and deploy ML applications.
BERT
NLP model for text understanding.
OpenCV
Computer vision library for image processing.
Course Program
Mathematics Foundation
- Linear algebra, statistics (distributions, hypothesis testing)..
Python for DS
- Pandas (grouping, merge, pivot), NumPy, visualization (Matplotlib, Seaborn, Plotly).
- EDA.
- Assignment: analyze a movie dataset..
Classic Machine Learning
- Linear regression, logistic regression, decision trees, random forest, gradient boosting (XGBoost, LightGBM).
- Metrics (MAE, RMSE, AUC-ROC).
- Cross-validation, hyperparameter tuning (GridSearch, Optuna)..
Neural Networks
- PyTorch (tensors, gradients), fully connected networks, CNN, RNN (LSTM).
- Transfer learning..
NLP & Computer Vision
- Tokenization, TF-IDF, Word2Vec, BERT.
- OpenCV..
Feature Engineering & Deployment
- Feature engineering, handling missing values.
- Model deployment (Flask/FastAPI, Docker, Streamlit).
- Final.
Bonuses
- Access to Kaggle competitions with mentors
- Review of top solutions
- Portfolio recommendations
Format
Video lectures, Jupyter notebooks, weekly Q&A sessions.
Result
Full Data Science cycle — from data to production.