Ementorhub

Skills you'll gain

Statistical Machine Learning

Unsupervised Learning

Generative AI

Performance Metric

Business Logic

Reinforcement Learning

Decision Tree Learning

Machine Learning Algorithms

Applied Machine Learning

Artificial Neural Networks

Data Modeling

Artificial Intelligence and Machine Learning (AI/ML)

Predictive Modeling

Large Language Modeling

Deep Learning

Supervised Learning

Dimensionality Reduction

Feature Engineering

There are 5 modules in this course

By the end of this course, you will be able to: 1. Implement, evaluate, and explain supervised, unsupervised, and reinforcement learning algorithms. 2. Apply feature selection and engineering techniques to improve model performance. 3. Describe deep learning models for complex AI tasks. 4. Assess the suitability of various AI & ML techniques for specific business problems. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.

Unsupervised learning

Reinforcement learning and other approaches

Deep learning and neural networks

The concepts in practice