Professional Certificate in AI and Machine Learning for Professionals

Day 1: Foundations of AI and Machine Learning

Overview of AI and ML Concepts

• History, current trends, and applications across industries.
• Key terminology, methodologies, and problem-solving approaches.
• Introduction to Machine Learning Types:
• Supervised, unsupervised, and reinforcement learning.
• Comparison and use cases.

Key Terminology

• Features, labels, training data, and test data.
• Concepts of overfitting and underfitting.

Tools and Frameworks

• Overview of popular tools: Python, TensorFlow, PyTorch, and Google AutoML.

Day 2: Data Analysis and Preprocessing

Data Preparation Techniques

• Cleaning, transforming, and normalising datasets.
• Visualisation techniques for understanding data patterns.
• Tools and libraries for visualisations (e.g., Python, NumPy, Pandas).

Supervised Learning Algorithms

• Regression techniques (linear and polynomial regression).

• Gradient Descent: Revolutionary technique in modern AI.
• Classification techniques (Nearest Neighbours, SVM).
• Decision Trees and Random Forests.

Practical Session

• Use Python and IBM SPSS for predictive modelling.
• Work with structured datasets (e.g., sales data, employee performance, financial data).
• Analyse feature importance and optimise the model.

Day 3: Deep Learning and Unsupervised Learning

Unsupervised Learning Algorithms

• Clustering (K-means, Hierarchical).
• Content-based filtering.
• Practical applications of unsupervised learning techniques.

Deep Learning Fundamentals

• Neural networks, activation functions, and backpropagation.
• Architectures: Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).
• Applications of transfer learning.

Practical Session

• Apply K-Means clustering to real-life datasets.
• Train and fine-tune a deep learning model using Google AutoML Vision for object detection.
• Explore unsupervised learning using Google AutoML Tables.

Day 4: Natural Language Processing (NLP) and Automation

NLP Fundamentals

• Tokenization, embeddings, and language models.
• Applications in sentiment analysis and Named Entity Recognition (NER).
• Large Language Models (LLMs).

Practical Session and Tasks

Custom GPT Configuration:

• LLM development using GPT.
• Overview of OpenAI GPT fine-tuning.
• Steps to create a custom GPT for specialized business use cases.
• Example use case: Chatbot for customer inquiries.

Workflow Automation:

• Using Zapier with OpenAI for automated processes.
• Building workflows (e.g., email summaries, task automation).

Day 5: Deployment, Ethical Implications, and Future of AI

Deploying AI Models

• Basics of deploying ML models in cloud platforms.
• Introduction to explainability and interpretability in AI.
• Interpretability and explainability of ML models.

Ethical Implications

• Fairness, bias, and ethical considerations in AI.
• Privacy and security concerns.
• Regulatory frameworks and compliance.

Capstone Project

• Design a solution using learned techniques and develop an AI roadmap for your company (e.g., predictive modelling or NLP workflows).