Master Python, data science, machine learning, GenAI, agents, MLOps, governance, and deployment through applied projects
Sub Category
- Data Science
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Objectives
- Explain the complete data science lifecycle and frame business problems as practical AI and analytics projects.
- Use Python fundamentals, functions, modules, data structures, files, exceptions, and reusable programming practices.
- Work efficiently with Jupyter notebooks, Git, version control, reproducibility, and collaborative development workflows.
- Manipulate numerical data using NumPy arrays, vectorized operations, indexing, slicing, and broadcasting.
- Load, clean, transform, join, aggregate, and analyze datasets using pandas.
- Create effective charts, dashboards, and data stories using modern visualization principles.
- Apply descriptive statistics, probability, statistical inference, hypothesis testing, and A/B testing.
- Collect data from public datasets, APIs, databases, and web-based sources.
- Write SQL queries involving filtering, grouping, joins, subqueries, and analytical operations.
- Design databases, relational models, data warehouses, and end-to-end data pipelines.
- Build supervised machine learning models for regression and classification problems.
- Evaluate models using cross-validation, confusion matrices, precision, recall, ROC-AUC, residual analysis, and other metrics.
- Apply decision trees, random forests, gradient boosting, k-nearest neighbors, and Naive Bayes.
- Perform feature engineering, categorical encoding, scaling, feature selection, and leakage prevention.
- Apply clustering, PCA, t-SNE, UMAP, recommender systems, time-series forecasting, and anomaly detection.
- Interpret model behavior using feature importance, SHAP concepts, partial dependence, error analysis, and model cards.
- Build foundational neural networks and understand activation functions, backpropagation, optimizers, and regularization.
- Develop introductory computer vision, natural language processing, and transformer-based workflows.
- Use generative AI, prompt engineering, retrieval-augmented generation, vector databases, and hallucination-reduction techniques.
- Design AI agents with tool use, function calling, planning, routing, memory, and multi-step workflows.
- Package, deploy, serve, monitor, version, and maintain models using MLOps practices.
- Apply cloud, container, orchestration, security, privacy, responsible AI, and governance principles.
- Develop an enterprise AI strategy, prioritize use cases, assess data readiness, and plan solution architecture.
- Complete and present a portfolio-ready capstone project supported by documentation, evaluation, deployment, and governance reviews.
Pre Requisites
- No previous artificial intelligence, machine learning, or data science experience is required.
- The program begins with foundational concepts and gradually advances toward enterprise-level implementation.
- Basic computer, file-management, web-browsing, and problem-solving skills are recommended.
- A computer capable of running Python, Jupyter notebooks, and standard data science libraries is required.
- A reliable internet connection is useful for installing tools, accessing datasets, and completing research.
- No advanced mathematics, statistics, or programming background is required before beginning.
- Learners should be prepared to practice coding regularly and complete weekly labs, reviews, and projects.
- Familiarity with spreadsheets or basic business data can be helpful but is not mandatory.
- Access to a code editor, GitHub account, and notebook environment will support portfolio development.
- Cloud access may be useful for selected deployment exercises, but many concepts can be practiced locally.
- Students should be willing to troubleshoot errors, document their work, review feedback, and improve projects iteratively.
- Consistency, curiosity, and a commitment to completing the year-long learning journey are the most important prerequisites.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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