The AI Driven Machine Learning with Python Specialization provides a complete, hands-on pathway to mastering machine learning. Learners will gain expertise in data preprocessing, visualization, model building, and deployment using Python, TensorFlow, and scikit-learn. Through practical case studies—ranging from healthcare analytics to AI-based image detection—participants will bridge theory and real-world application. By the end, learners will be able to design, train, evaluate, and deploy AI-powered solutions across industries.
AI Driven Machine Learning with Python Specialization
AI Driven Machine Learning with Python Specialization
Build Intelligent Models with Python. Master machine learning theory and projects using Python, TensorFlow, and real-world datasets.
Instructor: EDUCBA
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Apply supervised and unsupervised machine learning algorithms using Python and scikit-learn.
Build, train, and deploy deep learning models using TensorFlow and real-world datasets.
Integrate machine learning workflows into data-driven projects with performance optimization and visualization.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
October 2025
38 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA
Specialization - 4 course series
What you'll learn
Build and optimize ML models using scikit-learn.
Preprocess and visualize data with NumPy, Pandas, and Matplotlib.
Apply regression, classification, and clustering techniques.
Skills you'll gain
Unsupervised Learning, Performance Tuning, Data Processing, Pandas (Python Package), Matplotlib, Feature Engineering, Data Manipulation, Data Science, Statistical Modeling, Dimensionality Reduction, Applied Machine Learning, Statistical Machine Learning, Machine Learning, Regression Analysis, Python Programming, Predictive Modeling, Machine Learning Algorithms, Data Visualization, Scikit Learn (Machine Learning Library), and NumPy
What you'll learn
Build and train CNNs for image-based mask detection.
Integrate AI models into interactive front-end apps.
Deploy deep learning solutions using AWS cloud.
Skills you'll gain
Deep Learning, Application Deployment, Data Processing, Image Analysis, Cloud Applications, Computer Vision, Application Development, Amazon Web Services, Artificial Intelligence and Machine Learning (AI/ML), Front-End Web Development, and Tensorflow
What you'll learn
Apply logistic regression to healthcare datasets.
Preprocess and transform medical data in Python.
Evaluate prediction models using ROC curves.
Skills you'll gain
Jupyter, NumPy, Health Informatics, Data Processing, Scikit Learn (Machine Learning Library), Predictive Analytics, Applied Machine Learning, Supervised Learning, Analytics, Software Installation, Machine Learning, Data Transformation, Predictive Modeling, and Pandas (Python Package)
What you'll learn
Build and evaluate regression, clustering, and classification models.
Prepare, train, and interpret data for predictive modeling.
Apply ML techniques to solve real-world business problems.
Skills you'll gain
Data Visualization, Time Series Analysis and Forecasting, Supervised Learning, Predictive Modeling, Unsupervised Learning, Data Manipulation, Machine Learning Algorithms, Statistical Modeling, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning, Feature Engineering, Python Programming, Classification And Regression Tree (CART), Credit Risk, Predictive Analytics, and Applied Machine Learning
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
The AI Driven Machine Learning with Python Specialization can typically be completed in approximately 10 to 11 weeks, with an estimated commitment of 3 to 4 hours per week. This flexible, self-paced timeline allows learners to gradually build a strong foundation in machine learning theory and implementation. Through a combination of guided lessons, coding exercises, and real-world projects, participants can efficiently progress from core concepts to advanced applications—balancing both theoretical understanding and practical, hands-on experience.
A basic understanding of Python programming and familiarity with concepts such as variables, loops, and functions is recommended. Prior exposure to fundamental statistics, linear algebra, or data analysis will be helpful but not mandatory, as key concepts are introduced and reinforced through practical examples.
Yes, it is recommended to take the courses in the suggested sequence. The Specialization is designed to progress logically—from foundational machine learning principles and Python-based model building to deep learning applications and real-world case studies—ensuring that each course builds upon the skills developed in the previous one.
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Financial aid available,