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.
AI with Python: Apply & Implement ML Models
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AI with Python: Apply & Implement ML Models
This course is part of Artificial Intelligence with Python: Foundations to Projects Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
7 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
- Data Manipulation
- Supervised Learning
- Scikit Learn (Machine Learning Library)
- Artificial Intelligence
- Applied Machine Learning
- Machine Learning
- Deep Learning
- Data Cleansing
- Matplotlib
- Predictive Modeling
- Python Programming
- Exploratory Data Analysis
- Jupyter
- PyTorch (Machine Learning Library)
- Statistical Analysis
- Tensorflow
- Feature Engineering
- Dimensionality Reduction
- Keras (Neural Network Library)
- Unsupervised Learning
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
September 2025
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
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There are 3 modules in this course
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 assignments1 plugin
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
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
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