Executive Development Programme in Python for Machine Learning: Build & Deploy Models

This course is designed for executives and managers with a basic understanding of machine learning who wish to deepen their technical acumen and strategic decision-making skills. Participants will gain hands-on experience in building and deploying machine learning models using Python. The curriculum covers essential Python libraries, model selection, and deployment strategies tailored for enterprise settings.

Upon completion, attendees will be able to confidently lead data-driven initiatives, understand the technical implications of model deployment, and make informed decisions that leverage machine learning to drive business outcomes.

1. 1. Introduction to Python for Machine Learning: Learners will be introduced to Python programming basics and its libraries essential for machine learning. They will gain foundational skills in writing clean, efficient Python code and using popular libraries like NumPy and Pandas.
2. 2. Data Preprocessing and Manipulation: This module covers techniques for data cleaning, transformation, and manipulation. Learners will learn how to prepare data for machine learning models using Pandas and other tools, ensuring data quality and integrity.
3. 3. Exploratory Data Analysis: Learners will delve into techniques for exploring and visualizing data to uncover patterns and insights. They will gain skills in using libraries such as Matplotlib and Seaborn for effective data analysis.
4. 4. Supervised Learning Basics: This module introduces learners to supervised learning algorithms including linear regression, logistic regression, and decision trees. They will learn to implement these models and evaluate their performance using metrics like accuracy and error rates.
5. 5. Unsupervised Learning Techniques: Learners will explore unsupervised learning methods like clustering and dimensionality reduction. They will use techniques such as k-means clustering and principal component analysis to analyze and visualize complex datasets.
6. 6. Model Evaluation and Selection: This module covers various methods for evaluating machine learning models and selecting the best model for a given task. Learners will gain skills in using cross-validation, ROC curves, and other evaluation techniques.
7. 7. Advanced Supervised Learning Models: Learners will study advanced supervised learning models including support vector machines, random forests, and gradient boosting. They will learn to tune these models and handle large datasets effectively.
8. 8. Deep Learning Fundamentals: This module introduces learners to deep learning concepts and neural networks. They will gain hands-on experience with building and training simple neural networks using frameworks like TensorFlow or PyTorch.
9. 9. Model Deployment and Integration: Learners will learn how to deploy machine learning models in real-world applications. They will gain skills in integrating models with web applications and cloud services using Flask, Docker, and AWS.
10. 10. Case Studies and Capstone Project: In this final module, learners will work on a capstone project applying all the skills learned throughout the programme. They will tackle real-world problems, from data preprocessing to model deployment, and present their findings.