Practical Scrum for Software Development Teams
05/11/2024Cyber Security Awareness for Modern Workplaces
05/11/2024Python for Data Science and AI
£4,500.00
Overview:
This 5-day advanced course builds on basic Python skills to equip participants with specialized techniques in data science, machine learning, and AI. Through hands-on projects and real-world applications, participants will dive deep into data exploration, advanced machine learning, deep learning architectures, and natural language processing (NLP). Updated modules on MLOps, transfer learning, and responsible AI practices provide practical, up-to-date knowledge for implementing scalable and ethical data science and AI solutions.
Program Objectives:
At the end of this program, participants will be able to:
- Perform advanced data analysis and visualization techniques using libraries such as Pandas, Matplotlib, and Seaborn.
- Build and evaluate machine learning models using advanced algorithms, including boosting and transfer learning.
- Implement deep learning models for complex tasks, leveraging popular frameworks like TensorFlow, Keras, and PyTorch.
- Apply NLP techniques to real-world text data, including chatbot development with generative AI.
- Understand and utilize MLOps basics for deploying and managing machine learning models.
- Address ethical considerations in AI, focusing on bias, transparency, and responsible AI usage.
Target Audience:
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- Data Analysts and Data Scientists looking to deepen their skills
- Business Analysts interested in applying machine learning and AI
- Software Engineers with Python knowledge aiming to transition into data science
- Professionals with Python experience seeking advanced data-driven skills in machine learning and AI
Program Outline:
Day 1: Advanced Data Manipulation and Visualization
- Advanced Pandas Techniques: Grouping, merging, and applying functions across dataframes.
- Data Visualization: Creating complex visualizations with Matplotlib and Seaborn.
- Exploratory Data Analysis (EDA): Identifying patterns, correlations, and data quality issues.
- Data Preprocessing: Scaling, encoding, and feature engineering for machine learning.
- Hands-on Exercise: Performing EDA and creating interactive visualizations on a dataset.
Day 2: Machine Learning and Model Evaluation
- Review of Supervised and Unsupervised Learning: Brief overview of key concepts.
- Advanced Machine Learning Algorithms: Gradient Boosting, Random Forests, XGBoost, and KNN.
- Model Evaluation and Optimization: Cross-validation, hyperparameter tuning, and metrics (ROC-AUC, F1 Score).
- Feature Engineering and Selection: Techniques to improve model performance.
- Hands-on Project: Building and optimizing a classification or regression model.
Day 3: Deep Learning with TensorFlow and PyTorch
- Overview of Neural Networks: Understanding ANN, CNN, RNN, and LSTM.
- Transfer Learning with Pretrained Models: Using transfer learning for image and text applications.
- Deep Learning Frameworks: Hands-on with TensorFlow, Keras, and PyTorch.
- Hands-on Project: Fine-tuning a pretrained model on a specific domain task.
Day 4: Natural Language Processing and Generative AI
- Foundations of NLP: Text processing, tokenization, and embeddings (Word2Vec, BERT).
- NLP Applications: Sentiment analysis, topic modeling, and document classification.
- Introduction to Generative AI: Overview of ChatGPT and other language models.
- Building a Chatbot: Using the ChatGPT API to create a domain-specific chatbot.
- Hands-on Exercise: Developing an NLP project for sentiment analysis or topic modeling.
Day 5: MLOps, Model Deployment, and Responsible AI
- MLOps Fundamentals: Introduction to model deployment, monitoring, and CI/CD for ML.
- Model Deployment Techniques: Deploying models with Flask and FastAPI.
- Responsible AI Practices: Ethical AI, bias mitigation, and interpretability techniques.
- Real-World Case Studies: Industry examples of successful and ethical AI implementations.
- Capstone Project: Deploy a machine learning or NLP model and present the project, discussing challenges and solutions.