This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
Artificial Intelligence with Python: Foundations to Projects Specialization
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Artificial Intelligence with Python: Foundations to Projects Specialization
Hands-On Machine Learning With Python. Master data handling, ML algorithms, deep learning, and NLP by building real-world AI projects.
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
Set up Python AI environments and create analytical visuals with NumPy, Matplotlib, and Seaborn.
Build, evaluate, and explain supervised, unsupervised, and deep learning models in Python.
Design NLP pipelines with NLTK and present end-to-end AI solutions using reproducible notebooks.
Overview
Skills you'll gain
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
September 2025
31 practice exercises
Advance your subject-matter expertise
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- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA
Specialization - 3 course series
What you'll learn
Set up Python environments with Anaconda and Jupyter.
Manipulate and analyze data efficiently using NumPy.
Create clear, insightful visualizations with Matplotlib & Seaborn.
Skills you'll gain
Jupyter, Package and Software Management, NumPy, Matplotlib, Seaborn, Data Manipulation, Data Visualization Software, Box Plots, Scatter Plots, Histogram, Development Environment, and Heat Maps
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
Artificial Intelligence, Tensorflow, PyTorch (Machine Learning Library), Statistical Analysis, Scikit Learn (Machine Learning Library), Feature Engineering, Deep Learning, Python Programming, Machine Learning, Dimensionality Reduction, Data Manipulation, Data Cleansing, Predictive Modeling, Unsupervised Learning, Keras (Neural Network Library), Matplotlib, Supervised Learning, Jupyter, Exploratory Data Analysis, and Applied Machine Learning
What you'll learn
Apply predictive analytics and ML algorithms to real problems.
Analyze clustering, classification, and NLP pipelines in Python.
Construct AI solutions using logic, rules, and search strategies.
Skills you'll gain
Predictive Analytics, Supervised Learning, Applied Machine Learning, Unsupervised Learning, Computational Logic, Python Programming, Machine Learning Algorithms, Natural Language Processing, Random Forest Algorithm, Text Mining, Data Processing, Algorithms, Scikit Learn (Machine Learning Library), Unstructured Data, Data Science, Artificial Intelligence, and Predictive Modeling
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Frequently asked questions
This self-paced Specialization typically takes 9–10 weeks at 3–4 hours per week (≈ 27–40 hours). Weeks 1–3 cover Python setup, NumPy, and Matplotlib/Seaborn with a mini visualization project; Weeks 4–6 focus on supervised/unsupervised ML and MLPs (TensorFlow/Keras/PyTorch) with a model-comparison notebook; Weeks 7–9 apply predictive analytics and NLP (clustering, classification, NLTK) in an end-to-end project. Week 10 is a buffer to refine notebooks and polish portfolio-ready docs. Plan for two 90-minute sessions weekly; fast-track in 7–8 weeks if experienced, or extend to 12 weeks for a lighter pace.
Basic Python (variables, lists, loops, functions) and comfort with Jupyter/Anaconda are recommended. High-school math (algebra, functions, basic statistics) is sufficient; light familiarity with vectors/matrices and probability helps but isn’t required. No prior AI/ML experience is assumed.
Yes—complete them sequentially for the best experience. If you already use NumPy and can create plots in Matplotlib/Seaborn, you may start at Course 2; if you’ve trained and evaluated classifiers before, you can jump to Course 3.
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Financial aid available,