All Courses

Dive deep into machine learning algorithms and techniques with this comprehensive course designed for Python developers. Master supervised learning methods including linear regression, logistic regression, decision trees, and ensemble methods. Explore unsupervised learning techniques such as clustering, dimensionality reduction, and anomaly detection algorithms. Learn essential skills for model evaluation, cross-validation, and hyperparameter tuning to optimize your ML models. Build complete machine learning workflows using scikit-learn, the industry-standard library for classical ML in Python. Upon completion, you will be able to build, evaluate, and improve classical ML models for real-world applications. Prerequisites include Python basics and familiarity with data manipulation using NumPy and Pandas libraries.

Master Large Language Models and prompt engineering to build cutting-edge AI applications in this practical course. Understand how LLMs work, including Transformer architecture, attention mechanisms, and models like GPT and LLaMA. Learn prompt engineering techniques including zero-shot, few-shot, and chain-of-thought prompting strategies. Gain hands-on experience using the OpenAI API and Hugging Face Transformers library for AI development. Build AI-powered applications with LangChain, learning to create chains, agents, and memory-enabled systems. By the end, you will be skilled at leveraging LLMs for real-world applications across various domains. Basic Python knowledge and familiarity with AI concepts are recommended to get the most from this course.

Become a job-ready data scientist with this comprehensive bootcamp covering the complete ML workflow from data to deployment. Master Python programming and data foundations combined with essential machine learning algorithms and techniques. Dive into deep learning using TensorFlow and PyTorch to build neural networks for complex problem solving. Complete end-to-end machine learning pipeline projects that demonstrate your skills to potential employers. Learn model deployment and MLOps basics to take your models from development to production environments. Graduate fully capable of building and deploying ML models on real-world data with a strong portfolio. Basic Python knowledge is recommended, and mathematical curiosity will help you excel in this program.

Master responsible AI development with this essential bootcamp for practitioners concerned with trustworthy AI systems. Learn comprehensive AI ethics, data privacy regulations, and bias mitigation strategies for fair AI systems. Understand AI security threats and defense mechanisms against adversarial attacks targeting machine learning models. Explore Explainable AI (XAI) techniques to make your models transparent and interpretable for stakeholders. Navigate regulatory compliance frameworks including GDPR, CCPA, and the EU AI Act for enterprise deployments. Graduate able to design and audit AI systems that meet ethical, legal, and security requirements. This bootcamp is valuable for AI practitioners, compliance officers, and product managers working with AI.