Apply Artificial Intelligence Using Python for Beginners
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Apply Artificial Intelligence Using Python for Beginners
This course is part of Apply AI Foundations with Python and AWS Specialization
Instructor: EDUCBA
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What you'll learn
Explain core Artificial Intelligence concepts and how Python supports AI workflows.
Apply NumPy for numerical computing and data manipulation.
Analyze and visualize data using Matplotlib and Seaborn to identify patterns and insights.
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January 2026
9 assignments
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There are 3 modules in this course
By the end of this course, learners will be able to explain core Artificial Intelligence concepts, apply Python libraries for numerical computing, and analyze data using professional visualization techniques with confidence.
This beginner-friendly course is designed to help learners build a strong foundation in Artificial Intelligence using Python, even with no prior AI experience. Starting from environment setup with Anaconda and Jupyter Notebook, the course gradually introduces essential tools such as NumPy for numerical operations, Matplotlib for data plotting, and Seaborn for advanced statistical visualization. Learners will benefit by gaining practical, hands-on experience with industry-relevant Python libraries that are widely used in AI, data science, and machine learning workflows. Each concept is explained step by step, making complex topics easy to understand and apply. What makes this course unique is its clear learning progression, visual-first approach to data understanding, and focus on practical skills over theory overload. By the end of the course, learners will not only understand how AI workflows begin but will also be able to visually explore data, identify patterns, and build confidence for advanced AI and machine learning studies. This course is ideal for students, professionals, and aspiring data scientists looking to start their AI journey with Python.
This module introduces learners to the fundamentals of Artificial Intelligence using Python, guiding them through course orientation, environment setup, and core numerical computing concepts required for AI and data analysis.
What's included
6 videos3 assignments
6 videosβ’Total 51 minutes
- Introduction to Courseβ’7 minutes
- Download Anaconda Navigatorβ’9 minutes
- Set up and Installationβ’8 minutes
- NumPy in Jupiter Notebookβ’9 minutes
- Array Functionβ’7 minutes
- NumPy indexing and Selectionβ’10 minutes
3 assignmentsβ’Total 50 minutes
- Graded-Getting Started with Python for AIβ’30 minutes
- Course Kickoff & Environment Setupβ’10 minutes
- NumPy Fundamentals in Practiceβ’10 minutes
This module focuses on transforming data into meaningful visual insights by introducing core Python visualization libraries and teaching learners how to create and customize plots for data analysis and interpretation.
What's included
6 videos3 assignments
6 videosβ’Total 58 minutes
- Filter Functionβ’6 minutes
- Python Libraries for Visualizationβ’10 minutes
- Python Libraries for Visualization Continuedβ’10 minutes
- Matplotlib Library and its Usersβ’11 minutes
- Matplotlib Library and its Users Continuedβ’9 minutes
- Plotting of Dataβ’13 minutes
3 assignmentsβ’Total 50 minutes
- Graded-Data Visualization Essentialsβ’30 minutes
- Exploring Visualization Librariesβ’10 minutes
- Mastering Matplotlib for Data Plottingβ’10 minutes
This module advances learnersβ data visualization skills by leveraging Seaborn to create statistical and relational plots that support exploratory data analysis and AI-driven insights.
What's included
5 videos3 assignments
5 videosβ’Total 44 minutes
- Seaborn Package for Visualizationβ’9 minutes
- Seaborn Package for Visualization Continuedβ’8 minutes
- Scatter Plotsβ’7 minutes
- Scatter Plots Continuedβ’9 minutes
- Seaborn Libraries and its Implicationβ’11 minutes
3 assignmentsβ’Total 50 minutes
- Graded-Advanced Visualization with Seabornβ’30 minutes
- Seaborn Basics and Applicationsβ’10 minutes
- Scatter Plots & Seaborn Insightsβ’10 minutes
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Reviewed on Jun 4, 2026
I liked how the instructor explained AI fundamentals with practical coding examples rather than relying only on theory.
Reviewed on May 21, 2026
The exercises are manageable and useful for practicing the basics of AI implementation and data handling.
Reviewed on May 26, 2026
The coding exercises improved my understanding of AI implementation.
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