Focus:
This foundational section equips learners with essential skills in data management, statistical analysis, and data visualization. The aim is to build a strong base for working with data, ensuring learners can collect, clean, and manage data, as well as present it in clear, insightful visualizations. Understanding basic statistics and predictive analysis will lay the groundwork for more advanced machine learning concepts.

Key Outcomes:

• Efficient Data Handling: Learn how to manage and clean large datasets using tools like SQL and Python.

• Clear and Effective Data Visualization: Gain expertise in using BI tools (Tableau, Power BI) and Python libraries (Matplotlib, Seaborn) to create interactive and meaningful visualizations.

• Statistical Analysis for Data Science: Understand key statistical concepts like hypothesis testing, regression, and probability distributions.

• Prepared for Predictive Analytics: Apply foundational statistical methods to lay the groundwork for building predictive models.

Focus:
In this section, learners dive into machine learning algorithms to solve real-world problems through predictive models. The focus is on building and optimising models using supervised and unsupervised learning techniques. Learners will gain practical experience by implementing algorithms and applying them to tasks like classification, regression, and clustering, with a strong emphasis on model evaluation and improvement.

Key Outcomes:

• Practical Machine Learning Skills: Master algorithms like regression, decision trees, random forests, and k-NN for real-world applications.

• Model Evaluation and Tuning: Learn how to evaluate machine learning models using metrics such as accuracy, precision, recall, and AUC-ROC, and fine-tune models for optimal performance.

• Predictive Modelling Expertise: Build and deploy models to predict outcomes such as sales forecasting, fraud detection, and customer churn.

• Feature Engineering Mastery: Develop the skills to preprocess and transform data for improved model performance.

Focus:
This section dives deep into deep learning and advanced AI algorithms for tackling complex problems in fields like computer vision, natural language processing (NLP), and reinforcement learning. Learners will understand the intricacies of neural networks, explore Large Language Models (LLMs) like GPT and BERT, and learn to implement and deploy AI models in real-world scenarios.

Key Outcomes:

• Deep Learning Expertise: Learn how to design and implement deep learning models using neural networks for image and text processing tasks.

• Advanced AI Applications: Gain hands-on experience with CNNs for image recognition and RNNs for sequence data, applying them to complex AI challenges.

• Real-World AI Implementation: Deploy deep learning models using tools like TensorFlow, Keras, and PyTorch to create scalable AI solutions.

• Understanding Large Language Models (LLMs): Master the concepts behind LLMs like GPT and BERT, and explore their real-world applications in text generation, chatbots, and language translation.