Course for Advanced Data Science with Python

• Advanced AI Concepts: An overview of artificial intelligence introducing advanced topics such as deep learning and neural networks.
• Time Series Analysis: Exploration of methodologies like ARIMA and SARIMA for time series analysis.
• Random Forests: In-depth study of decision tree algorithms and random forests.
• Reinforcement Learning: Fundamentals of reinforcement learning, including Q-learning, deep Q-networks, and policy gradients.
• Deep Learning: Introduction to neural networks, convolutional neural networks, and other advanced models, with practical implementation exercises.
• Natural Language Processing (NLP): Text data preprocessing, word embeddings, and hands-on work with NLP libraries in Python.
• Model Evaluation: Techniques for evaluating advanced machine learning models (precision, recall, cross-validation).

Upon completion, students will be able to implement, optimize, and critically evaluate complex machine and deep learning models, applying them to address specific real-world challenges in AI and data science.

• Python
• Spyder
• Jupyter Notebook

All tools are free, and instructions for downloading and installing them will be provided at the start of the program.