Introduction to AI, Data Science & Machine Learning with Python LT1264

Module 1The Role of a Data Scientist: Combining Technical and Non-Technical Skills

• What is the required skillset of a Data Scientist?
• Combining the technical and non-technical roles of a Data Scientist
• The difference between a Data Scientist and a Data Engineer
• Exploring the entire lifecycle of Data Science efforts within the organization
• Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
• Exploring diverse and wide-ranging data sources that you can use to answer business questions
• Explore the concepts behind Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs)
• Module 2: Data Manipulation and Visualization using Python's Pandas and Matplotlib Libraries
• Introducing the features of Python that are relevant to Data Scientists and Data Engineers
• Viewing Data Sets using Python’s Pandas library
• Importing, exporting, and working with all forms of data, from Relational Databases to Google Images
• Using Python Selecting, Filtering, Combining, Grouping, and Applying Functions from Python's Pandas library
• Dealing with Duplicates, Missing Values, Rescaling, Standardizing, and Normalizing Data
• Visualizing data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries

Module 3Preprocessing and Analyzing Unstructured Data with Natural Language Processing

• Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models
• Exploring the most popular approaches to Natural Language Processing (NLP), such as stemming and "stop" words
• Preparing a term-document matrix (TDM) of unstructured documents for analysis
• Review the architectures of Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs)
• Look at how Data Scientists can integrate Large Language Models (LLMs) in their work

Module 4Linear Regression and Feature Engineering for Business Problem Solving

• Expressing a business problem, such as customer revenue prediction, as a linear regression task
• Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)
• Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE
• Exploring the Feature Engineering possibilities to improve the Linear Regression model

Module 5Classification Models and Evaluation for Predictive Analysis

• Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn
• Exploring how AI/ML Classification models are built using Training, Test, and Validation
• Evaluating the strength of a Decision Tree Classifier

Module 6Alternative Approaches to Classification and Model Evaluation

• Examining alternative approaches to classification
• 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 approaches in AI
• Exploring the probability foundations of Naive Bayes classifiers
• Reviewing different approaches to measuring the performance of AI/ML Classification Models
• Reviewing ROC curves, AUC measures, Precision, Recall, and Confusion Matrices

Module 7Clustering Techniques for Customer and Product Segmentation

• Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
• Exploring what the concept of similarity means to humans and how you can implement it programmatically through distance measures on descriptive variables
• Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm
• Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
• Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)

Module 8Association Rules and Recommender Systems for Business Applications

• Building models of customer behaviors or business events from logged data using Association Rules
• Evaluating the strength of these models through probability measures of support, confidence, and lift
• Employing feature engineering approaches to improve the models
• Building a recommender for your customers that is unique to your product/service offering

Module 9Network Analysis for Organizational Insights

• Analyzing your organization, its people, and its environment as a network of inter-relationships
• Visualizing these relationships to uncover previously unseen business insights
• Exploring ego-centric and socio-centric methods of analyzing connections critical to your organization

Module 10Big Data Analytics, Communication, and Ethics

• Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
• Exploring the communications and ethics aspects of being a Data Scientist
• Discuss the ethical implications of recent developments in AI
• Surveying the paths of continual learning for a Data Scientist