Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

AI with Python: Apply & Implement ML Models

EDUCBA via Coursera

Go to class Write review

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

Build practical Artificial Intelligence and Machine Learning skills with Python in this hands-on course designed for intermediate learners who want to move from foundational concepts to implementing advanced AI models. You will begin by exploring the fundamentals of AI, Python for machine learning, bias-variance tradeoff, model evolution, and the role of Scikit-learn in developing intelligent solutions. As you progress, you will learn how to prepare, preprocess, and visualize datasets, apply dimensionality reduction techniques, select appropriate machine learning models, and evaluate classifier performance using statistical analysis, accuracy metrics, and label encoding. The course then advances to deep learning, where you will implement multilayer perceptrons, clustering, ensemble methods, and binary classification models using TensorFlow, Keras, and PyTorch within Jupyter Notebook environments. What makes this course distinctive is its step-by-step learning approach that combines essential AI theory with practical coding demonstrations, allowing you to immediately apply concepts to real-world datasets. You will also strengthen your ability to document AI workflows with Markdown and communicate insights through Pyplot visualizations. By the end of the course, you will be able to analyze datasets, build, evaluate, test, and refine machine learning and deep learning models while confidently presenting your AI projects.

Syllabus

Foundations of AI with Python

This module builds a strong foundation in Artificial Intelligence by introducing Python’s role in AI, exploring the basics of machine learning, and emphasizing the importance of data processing. Learners will also examine the concepts of bias, variance, and model evolution while gaining hands-on exposure to Scikit-learn, a widely used machine learning library. By the end of this module, learners will be equipped with essential skills to begin building AI solutions confidently.

Data Handling and Machine Learning Models

This module focuses on data handling, preprocessing, and visualization to ensure clean and structured datasets. Learners will practice applying dimensionality reduction techniques, model selection strategies, and classifier methods such as KNN. Additionally, the module highlights evaluation metrics, statistical analysis, and encoding methods to improve classification performance. By completing this module, learners will gain practical skills to prepare data effectively and build accurate machine learning models.

Deep Learning and Practical AI Applications

This module introduces learners to advanced AI techniques, including multilayer perceptrons, clustering, and ensemble methods. It also provides hands-on exposure to popular frameworks like TensorFlow, PyTorch, and Keras within Jupyter Notebook environments. The module concludes with practical applications in binary classification, documentation using Markdown, and visualization with Pyplot, empowering learners to implement deep learning models and present AI projects effectively.