AI with Python: Apply & Implement ML Models

This course is part of Artificial Intelligence with Python: Foundations to Projects Specialization

Instructor: EDUCBA

Access provided by Somaiya Vidyavihar University

3 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

9 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Analyze datasets and apply key ML algorithms in Python.
Evaluate classifiers and perform dimensionality reduction.
Build deep learning models with TensorFlow, Keras, and PyTorch.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

11 assignments

Taught in English

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This course is part of the Artificial Intelligence with Python: Foundations to Projects Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

By the end of this course, learners will be able to analyze datasets, apply machine learning algorithms, evaluate classifiers, and implement deep learning models using Python and its popular frameworks. The course begins with the foundations of AI, covering essential concepts such as Python for AI, bias-variance tradeoff, and model evolution. Learners will then explore data handling, visualization, dimensionality reduction, and classifier evaluation to strengthen practical ML skills. Finally, the course dives into advanced AI with multilayer perceptrons, clustering, ensemble methods, and hands-on practice with TensorFlow, Keras, and PyTorch.

What makes this course unique is its step-by-step structure combining theory with practical coding demonstrations using Jupyter Notebook, ensuring learners can directly apply concepts to real-world problems. Through integrated lessons on documentation and visualization, participants will also learn how to clearly present AI projects. Designed for intermediate-level learners, this course bridges the gap between basic knowledge and advanced AI applications, empowering you to confidently build, test, and refine machine learning and deep learning models.

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.

What's included

8 videos3 assignments

8 videos Total 75 minutes

Introduction to Course 8 minutes
Python for AI 6 minutes
What is Machin Learning 11 minutes
Data Processing Effort 9 minutes
What is Meaning of Bias 10 minutes
Bias vs Variance Tradeoff 8 minutes
Model Evolution 11 minutes
Scikit Learn 13 minutes

3 assignments Total 50 minutes

Graded - Foundations of AI with Python 30 minutes
Introduction to AI and Python 10 minutes
Bias, Variance, and Model Evolution 10 minutes

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.

What's included

12 videos4 assignments

12 videos Total 111 minutes

Loading the Data 11 minutes
Checking the Visualization 14 minutes
Predict 9 minutes
Data Values 8 minutes
Applying Dimensionality Reduction 10 minutes
Model Selection 10 minutes
Neighbors Classifier 10 minutes
Accuracy of Classifier 9 minutes
ML Classification Hindson 7 minutes
Statistical Analysis of the Dataset 6 minutes
Import Label Encoder 9 minutes
Accuracy Score 7 minutes

4 assignments Total 60 minutes

Graded - Data Handling and Machine Learning Models 30 minutes
Data Preparation and Visualization 10 minutes
Feature Engineering and Model Building 10 minutes
Evaluating Classifiers and Datasets 10 minutes

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.

What's included

9 videos4 assignments

9 videos Total 79 minutes

Multilayer Perceptron 7 minutes
Multilayer Perceptron Continued 8 minutes
Number of Clusters 10 minutes
Multiple Method 10 minutes
Keras-Pytorch and Tensorflow 10 minutes
Working on Jupyter Notebook 11 minutes
Binary Classification 12 minutes
Use Markdown Headings 6 minutes
Pyplot 6 minutes

4 assignments Total 60 minutes

Graded - Deep Learning and Practical AI Applications 30 minutes
Neural Networks with Perceptrons 10 minutes
Ensemble Methods and Frameworks 10 minutes
Classification, Documentation, and Visualization 10 minutes

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Instructor

Instructor ratings

(8 ratings)

EDUCBA

1,027 Courses 227,853 learners

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EDUCBA

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Reviewed on Feb 1, 2026

Excellent learning experience. The step-by-step approach makes it easy to grasp AI concepts without feeling overwhelmed.

Reviewed on Jan 14, 2026

This course provides a clear and practical understanding of AI and machine learning using Python. The concepts are explained in a simple way, making it easy to apply them in real-world projects.

Reviewed on Jan 17, 2026

The course content is well-structured and easy to follow. Python examples made AI concepts simple and practical

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