Apply AI Foundations with Python and AWS

EDUCBA via Coursera Specialization

Go to class Write review

Overview

Coursera Flash Sale

40% Off Coursera Plus for 3 Months!

Grab it

This Specialization provides a structured and practical introduction to artificial intelligence, covering core AI concepts, machine learning fundamentals, deep learning principles, and real-world AI applications. Learners gain hands-on experience using Python tools and AWS AI services to analyze data, build models, and evaluate intelligent systems. The program emphasizes ethical AI, industry-aligned workflows, and scalable cloud-based implementations to prepare learners for real-world and certification-oriented roles.

Syllabus

Course 1: Understand and Apply AI Fundamentals with AWS
Course 2: Apply Artificial Intelligence Using Python for Beginners
Course 3: Understand and Apply Artificial Intelligence Fundamentals
Course 4: Learn & Build Machine Learning Models with Python

Courses

0 reviews
6 hours 32 minutes
View details

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.
0 reviews
8 hours 39 minutes
View details

By the end of this course, learners will be able to explain core machine learning concepts, prepare and analyze data using Python libraries, visualize insights effectively, and build and evaluate basic machine learning models using industry-standard tools. This beginner-friendly course is designed to provide a clear, structured pathway into machine learning with Python, making it ideal for students, aspiring data scientists, and professionals transitioning into data-driven roles. Learners start with foundational machine learning principles and gradually progress through numerical computing with NumPy, data manipulation with Pandas, and data visualization using Matplotlib. Unlike theory-heavy courses, this program emphasizes practical understanding and hands-on workflows, helping learners connect concepts to real-world applications. The course also introduces essential preprocessing techniques, Scikit-learn pipelines, and linear regression modeling, ensuring learners understand not just how to build models, but why each step matters. What makes this course unique is its step-by-step learning progression, well-structured modules, and assessment-aligned objectives, enabling learners to build confidence as they move from data preparation to model evaluation. Upon completion, learners will have a strong foundation to pursue advanced machine learning topics or apply their skills in real projects.
0 reviews
13 hours 3 minutes
View details

By the end of this course, learners will be able to explain core artificial intelligence concepts, analyze machine learning approaches, apply deep learning principles, and evaluate AI solutions using AWS services. This comprehensive course is designed to build strong foundations in AI fundamentals and core concepts, progressing from classical AI techniques and machine learning to deep learning, generative models, and real-world AI applications. Learners will gain a clear understanding of how AI systems reason, learn, and make decisions, supported by practical insights into NLP, computer vision, reinforcement learning, and ethical AI practices. What makes this course unique is its balanced blend of theory, practice, and cloud-based implementation. It not only explains how AI works, but also how AI is applied at scale using AWS AI services such as SageMaker, Lex, Polly, Rekognition, and foundation models. Structured modules, lesson-wise objectives, practice quizzes, and graded assessments ensure progressive mastery and exam readiness. This course is ideal for beginners, aspiring AI practitioners, and professionals preparing for the AWS Certified AI Practitioner exam, enabling learners to confidently apply AI concepts to real-world business and industry use cases.
0 reviews
5 hours 58 minutes
View details

By the end of this course, learners will be able to explain core artificial intelligence concepts, analyze intelligent reasoning methods, apply machine learning techniques, and evaluate reinforcement learning approaches used in real-world AI systems. This course provides a comprehensive and structured introduction to artificial intelligence, guiding learners from foundational concepts to practical learning paradigms. It begins by establishing a clear understanding of what artificial intelligence is, how it has evolved, and why it matters, while addressing ethical and societal considerations that shape responsible AI development. Learners then explore the logical, probabilistic, and search-based reasoning techniques that enable intelligent decision-making. The course advances into machine learning, covering supervised and unsupervised learning, clustering, distance measures, dimensionality reduction, and association rule learning. It culminates with reinforcement learning, where learners examine how intelligent agents learn through interaction, rewards, and feedback using both model-based and model-free approaches. What makes this course unique is its end-to-end learning journey, combining conceptual clarity, theoretical foundations, and applied machine learning perspectives within a single cohesive structure. Upon completion, learners will gain practical AI literacy, critical thinking skills, and a strong foundation for advanced AI, data science, or machine learning studies.