Learn Introduction to AI, Data Science & Machine Learning with Python in Just 5 Days

• Explore how the role of the Data Scientist is evolving in the age of Generative AI, Foundation Models, and AI-assisted analytics
• Understand the technical, analytical, business, and communication skills required of modern Data Scientists
• Examine how Data Scientists, Data Engineers, Machine Learning Engineers, and AI Engineers differ in their roles of delivering value from data
• Follow the complete Data Science lifecycle, from business problem definition and data acquisition through model development, deployment, and governance
• Learn how to translate business questions into Data Science, Machine Learning, and AI opportunities
• Explore the concepts behind Foundation Models, Large Language Models (LLMs), Generative Pre-trained Transformers (GPTs), embeddings, and Retrieval-Augmented Generation (RAG)
• Discover how organizations combine traditional Machine Learning with Generative AI and AI agents to solve real-world business problems
• Understand why Responsible AI, explainability, fairness, privacy, and governance are becoming core competencies for modern Data Scientists
• Examine how an AI Assistant might be used in writing Python code and suggesting analysis approaches

• Build practical Python skills used by today’s Data Scientists, Machine Learning Engineers, and AI practitioners
• Use Python’s pandas library to explore, transform, combine, and prepare data for Machine Learning and AI applications
• Apply Exploratory Data Analysis (EDA) techniques to uncover patterns, trends, anomalies, and business insights
• Assess data quality and address common challenges such as missing values, duplicates, outliers, normalization, and feature scaling
• Explore how EDA can help identify bias, fairness concerns, and model readiness before building Machine Learning models
• Create effective visualizations using pandas, Matplotlib, and Seaborn to support data exploration, communication, and decision-making
• Examine how an AI Assistant might be used as a Personal Tutor or for Code Documentation

• Explore how Data Scientists work with unstructured data such as text, images, audio, and video to support business decision-making
• Learn the fundamentals of Natural Language Processing (NLP), including text preprocessing, tokenization, feature engineering, vectorization, and text classification
• Compare traditional NLP techniques such as TF-IDF and term-document matrices with modern approaches based on embeddings, transformers, and Large Language Models (LLMs)
• Generate and analyze embeddings using modern AI models, and discover how semantic similarity powers search, recommendation systems, Retrieval-Augmented Generation (RAG), and AI assistants
• Use Python libraries, OpenAI APIs, and Generative AI tools to prepare, analyze, and extract insights from unstructured data for Machine Learning and AI applications

• Learn how to translate business forecasting problems into regression-based Machine Learning models and predictive analytics solutions
• Build and train linear regression models in Python to predict continuous outcomes such as revenue, sales, costs, pricing, and resource consumption
• Explore simple and multiple linear regression techniques, including how model coefficients can be used to understand relationships between variables
• Evaluate model quality using statistical measures, residual analysis, and visualization techniques to assess predictive performance
• Understand the assumptions behind linear regression and learn how data quality, correlation, and feature selection can influence model accuracy
• Discover why interpretable models remain important in modern AI systems, helping organizations explain predictions, support decision-making, and meet governance requirements
• Use a Generative AI Assistant to help explain the Regression findings to a non-technical audience

• Learn how classification models predict categorical outcomes and support decision-making across business, healthcare, finance, security, and AI applications
• Explore the fundamentals of supervised learning, including training and test datasets, target variables, and predictor features used to build classification models
• Build and apply decision tree classifiers that use recursive partitioning to categorize data and generate transparent, rule-based predictions
• Evaluate classification model performance using confusion matrices, accuracy and error rates while understanding the impact of class imbalance on results
• Compare common classification algorithms, including Decision Trees, Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, and modern AI classification models
• Examine the growing importance of explainable AI, learning how decision trees provide transparent reasoning that can be understood, validated, and audited by business and regulatory stakeholders

• Explore alternative classification techniques beyond decision trees, including Logistic Regression, Neural Networks, and Naive Bayes
• Learn how Logistic Regression predicts binary outcomes by estimating probabilities, making it a widely used and highly interpretable classification method in business, healthcare, finance, and risk analytics
• Considering how Activation Functions are integral to Logistic Regression Classifiers
• Delve into the architecture of Neural Networks and investigate the explosive growth of Deep Learning and Transformer approaches in AI
• Exploring the probability foundations of Naive Bayes classifiers
• Reviewing additional approaches to assess model performance: Precision, Recall, F1, ROC curves, and AUC metrics
• Use an AI assistant to visualize a neural network

• Understand clustering as an unsupervised learning technique that identifies natural groupings within data without predefined labels, making it useful for discovering hidden patterns and relationships
• Explore the concept of similarity and learn how clustering groups observations that are alike while separating observations that are significantly different based on their characteristics
• Examine various distance measures, including Euclidean and correlation-based distances, and understand how the choice of similarity metric can significantly influence clustering outcomes
• Apply K-Means clustering to partition data into meaningful clusters through centroid initialization, cluster assignment, and iterative centroid updates until stable groupings are achieved
• Learn the importance of feature scaling and data preparation to ensure variables are comparable and clustering algorithms produce accurate and meaningful results
• Explore Hierarchical Clustering methods and understand how clustering supports business analytics, customer segmentation, recommendation systems, anomaly detection, and modern AI applications involving embeddings and similarity search
• Use an AI Assistant to help describe the characteristics of clusters from the centroid values

• Understand association rules as an unsupervised learning technique used to identify relationships between items that frequently occur together in transaction data, helping organizations uncover hidden patterns without predefined target variables
• Learn how association analysis supports business decision-making through market basket analysis, product placement, cross-selling, recommendation systems, fraud detection, website optimization, healthcare analytics, and manufacturing quality control.
• Explore how organizations can find meaningful patterns in anonymous transaction data, using purchase histories and item combinations to generate actionable insights even when customer identities are unavailable
• Understand how transaction datasets are transformed into sparse matrices and one-hot encoded representations to efficiently analyze large numbers of products and transactions within Python
• Apply the Apriori algorithm to discover frequent itemsets by using minimum support thresholds that reduce computational complexity while identifying the most relevant item combinations
• Evaluate association rules using key metrics such as support, confidence, and lift, and learn how these measures help determine the strength, reliability, and usefulness of discovered relationships for recommendation systems and business analytics
• Use an AI Assistant to help customise Python code

You'll learn how to use Python, Data Science, Machine Learning, and Generative AI to analyze data, build predictive models, identify patterns, create visualizations, and solve real-world business problems. The course covers the complete Data Science lifecycle along with modern AI technologies such as LLMs, embeddings, RAG, and AI agents.

Generative AI is changing how Data Scientists write code, explore datasets, analyze results, create visualizations, document projects, and communicate findings. This course shows how to incorporate Generative AI and AI assistants into the Data Science workflow while still applying human judgment, validation, and responsible AI practices.

Large Language Models (LLMs) can assist with coding, data analysis, natural language processing, documentation, research, and interpreting complex information. The course introduces LLMs, Foundation Models, GPTs, embeddings, and Retrieval-Augmented Generation (RAG) and explains how these technologies fit into modern AI and Data Science workflows.

AI agents are designed to perform tasks by combining capabilities such as reasoning, planning, tool use, and interaction with information or systems. The course introduces AI agents and agentic AI concepts and explores how they are becoming part of the modern Data Science and AI ecosystem.

AI assistants can help Data Scientists generate and explain Python code, troubleshoot problems, analyze datasets, create visualizations, document workflows, and interpret machine learning results. This course demonstrates practical ways to use AI-assisted Data Science while validating outputs and maintaining accuracy.

Natural Language Processing (NLP) enables computers to process and analyze human language. The course covers text preprocessing, tokenization, vectorization, TF-IDF, embeddings, text classification, transformers, and LLM-based approaches.