Intro to AI/ML and Data Science with Python training course

• Preprocessing unstructured data (web ads, emails, blogs).
• Common NLP techniques: stemming and stop words.
• Creating term-document matrices for analysis.
• Integrating Large Language Models (LLMs).

Module 4: Linear Regression and Feature Engineering

• Solving business problems with linear regression (e.g., revenue prediction).
• Identifying predictors for target variables.
• Evaluating regression models using RMSE.
• Using feature engineering to improve models.

Module 5: Classification Models and Evaluation

• Building and using AI/ML classifiers (e.g., Customer Churn).
• Training, testing, and validating classification models.
• Evaluating decision tree classifier performance.

Module 6: Alternative Classification Approaches

• Exploring alternative classification methods.
• Understanding the role of activation functions in Logistic Regression.
• Using Neural Networks and Deep Learning (e.g., self-driving cars).
• Probability foundations of Naive Bayes classifiers.
• Evaluating classification models (ROC, AUC, Precision, Recall, etc.).

Module 7: Clustering for Customer and Product Segmentation

• Segmenting customers/products with clustering algorithms.
• Implementing similarity measures in Python.
• Top-down clustering with K-Means.
• Bottom-up clustering with hierarchical algorithms.
• Clustering unstructured data (e.g., Tweets, Emails).

Module 8: Association Rules and Recommender Systems

• Modelling customer behaviour with Association Rules.
• Evaluating models using support, confidence, and lift.
• Feature engineering to enhance models.
• Building custom recommender systems.

Module 9: Network Analysis for Insights

• Analysing organisational relationships through network analysis.
• Visualising connections to uncover business insights.
• Ego-centric vs. socio-centric analysis.

Module 10: Big Data Analytics, Communication, and Ethics

• Cloud-based Big Data analytics (Microsoft, Amazon, Google).
• Communicating and handling ethics in Data Science.
• Discussing AI ethics and future implications.
• Exploring continuous learning paths for Data Scientists.