Projects
Assignments
Overview
Dive into the world of Artificial Intelligence (AI) and Machine Learning (ML) to solve business problems effectively. This course provides a structured approach to developing data-driven solutions through AI and ML methodologies.
Objectives
By the end of this course, leaner will be able to:
- Develop AI solutions for business problems.
- Prepare and preprocess data for machine learning.
- Train, evaluate, and fine-tune machine learning models.
- Build linear regression, forecasting, classification, and clustering models.
- Construct models using decision trees, random forests, support-vector machines, and neural networks.
- Operationalize and maintain machine learning models in production.
Prerequisites
- Familiarity with foundational data science concepts and the machine learning process.
- Understanding of statistical concepts and summary statistics.
- Proficiency in Python programming, including the use of libraries like NumPy and pandas.
Course Outline
Module 1: Solving Business Problems Using AI and MLCourses2024-02-20T18:17:12+05:30
- Identify solutions for business problems.
- Formulate machine learning problems.
- Select appropriate ML approaches.
Module 2: Preparing DataCourses2024-02-20T18:00:16+05:30
- Data collection and transformation.
- Feature engineering.
- Working with unstructured data.
Module 3: Training, Evaluating, and Tuning ML ModelsCourses2024-02-20T18:01:17+05:30
- Model training and evaluation.
- Hyperparameter tuning.
Module 4: Building Regression and Forecasting ModelsCourses2024-02-20T18:06:25+05:30
- Linear regression models.
- Time series forecasting models.
Module 5: Operationalizing and Maintaining ML ModelsCourses2024-02-20T18:07:41+05:30
- Model deployment and automation.
- Integration into ML systems.
- Security and maintenance of ML pipelines.
