Large Language Models & Prompt Engineering

Master Python programming and data analysis skills essential for AI and data science careers in this hands-on course. Learn Python syntax, control flow, functions, and object-oriented programming from the ground up with practical examples. Explore powerful data manipulation libraries including NumPy for numerical computing and Pandas for data wrangling. Create stunning visualizations using Matplotlib and Seaborn to effectively communicate insights from your data analysis. Work with real-world datasets to practice cleaning, transforming, and analyzing data like a professional data scientist. By course completion, you will be able to clean, analyze, and visualize data using Python with confidence. This course requires no prior programming experience, just basic computer skills and a willingness to learn.

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.