Data Science & Machine Learning
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Welcome to the Data Science & Machine Learning course — your complete path to mastering data analysis and building intelligent systems.
Course Format
Self-paced · 4 modules · Estimated 60 hours · Hands-on projects with real datasets included.
What You’ll Learn
- Data Analysis — Data wrangling, visualization, statistics, and exploratory analysis
- Machine Learning — Supervised and unsupervised learning, model evaluation, and feature engineering
- Deep Learning — Neural networks, CNNs, RNNs, transformers, and transfer learning
- ML in Production — Model deployment, MLOps, monitoring, and ethical AI
Course Modules
| Module | Topics Covered |
|---|---|
| 1. Data Analysis | Pandas, NumPy, Matplotlib, Seaborn, statistical analysis, hypothesis testing |
| 2. Machine Learning | Regression, classification, clustering, decision trees, SVM, ensemble methods |
| 3. Deep Learning | TensorFlow, PyTorch, CNNs, RNNs, LSTMs, attention mechanisms, transformers |
| 4. ML in Production | Model serving, Docker, MLflow, CI/CD for ML, fairness and bias |
Who This Course Is For
- Aspiring data scientists and ML engineers
- Software developers transitioning into AI/ML
- Analysts wanting to add machine learning to their toolkit
- Researchers and academics working with data
Prerequisites
Basic programming and math recommended
Familiarity with Python programming and basic statistics (mean, median, probability) is helpful. We’ll cover the math as we go.
Ready to Start?
Begin your journey with Module 1 and start analyzing data like a pro.
Start with Module 1