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Master AI to Build Intelligent Systems and Drive Innovation
Instructor: Edureka
Included with
Recommended experience
Intermediate level
Learners must have prior knowledge of basic Python programming concepts and an understanding of statistical and inferential analysis.
Recommended experience
Intermediate level
Learners must have prior knowledge of basic Python programming concepts and an understanding of statistical and inferential analysis.
Analyze and apply fundamental Python functions and methods.
Utilize and apply various machine learning models effectively.
Design and optimize neural networks for AI applications.
Explain and implement image, video, and audio processing methods.
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February 2025
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Begin your journey with the Mastering AI specialization, designed for both aspiring and experienced professionals. This program equips you with essential skills in artificial intelligence, machine learning, and deep learning to develop cutting-edge solutions.
Explore key concepts such as neural networks, statistical foundations, predictive modeling, and AI-driven computer vision and speech recognition. Through hands-on projects and real-world case studies, gain the expertise to build intelligent models, optimize deep learning architectures, and apply AI to solve complex challenges.
The specialization comprises four comprehensive courses:
Python and Statistics Foundations: Build a strong foundation in Python programming, probability, and statistical analysis for AI applications.
Applied Machine Learning with Python: Learn to develop, train, and optimize machine learning models to extract insights and drive AI solutions.
Practical Deep Learning with Python: Master deep learning techniques, neural networks, and advanced model optimization for real-world AI applications.
AI Applications: Computer Vision and Speech Recognition: Explore AI-driven image processing and speech recognition technologies.
By the end of this program, you’ll be prepared to design and implement AI solutions, harness the power of deep learning, and advance your career in artificial intelligence. Join us to unlock the full potential of AI and drive innovation across industries!
Applied Learning Project
Learners will acquire proficiency in building complex machine learning and deep learning models to solve challenging problems while demonstrating a high level of problem-solving skills. Learners will learn to program using Python, clean and transform data using various data preprocessing methods, and apply statistical and inferential modeling.
Learners will also gain expertise in different machine learning techniques. Additionally, they will explore deep learning techniques such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Faster R-CNN, and other advanced architectures.
The curriculum encompasses knowledge of AI processing for video, audio, and speech recognition. Learners will progress from basic to advanced programming concepts for handling AI-related tasks. Their ability to apply acquired knowledge will be demonstrated through individual projects, serving as the culmination of their educational journey.
Write Python programs using core concepts like variables, data types, and control flow.
Apply NumPy and Pandas to manipulate and analyze data efficiently.
Create insightful data visualizations using Matplotlib, Seaborn, and Plotly for effective reporting.
Perform statistical analysis and probability tests to solve data-driven problems and validate hypotheses.
Explore machine learning algorithms, including supervised, unsupervised, and semi-supervised methods.
Apply decision trees, random forests, and K-means clustering for classification and clustering.
Develop machine learning models to gain insights and make predictions from real-world data.
Enhance model accuracy by applying model-boosting techniques and evaluating their effectiveness.
Understand the core components of deep learning models and their role in AI.
Apply CNN, R-CNN, and Faster R-CNN for object detection tasks.
Implement RNNs and LSTMs for sequential data processing.
Optimize and evaluate deep learning models for improved performance.
Analyze speech waveforms and apply audio signal processing techniques.
Develop and implement computer vision algorithms using OpenCV.
Perform morphological operations on images and videos for data manipulation.
Apply speech recognition techniques for digitizing and analyzing audio signals.
Edureka is an online education platform focused on delivering high-quality learning to working professionals. We have the highest course completion rate in the industry and we strive to create an online ecosystem for our global learners to equip themselves with industry-relevant skills in today’s cutting edge technologies.
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This specialization is designed to be finished within a span of 3 to 4 months. Dedicating a minimum of 5 to 6 hours per week to your studies.
Learners should have prior knowledge of basic Python programming concepts and a foundational understanding of statistical and inferential analysis.
Yes, it is recommended to take the courses in the suggested order to build foundational knowledge before progressing to advanced topics. This ensures a smoother learning experience and mastery of key concepts.
The Applied Data Analytics Specialization does not offer university credit. However, you will gain valuable skills and practical experience to advance your career in data analytics.
Upon completing the Specialization, you will be able to analyze and manipulate data using Python, SQL, and Power BI, create data visualizations, build predictive models, and develop interactive dashboards to derive actionable insights for real-world business challenges.
This course primarily uses Python for programming and implementation.
Learners can execute Python code directly from a web browser with no configuration required using Google Colab.
Absolutely! The materials and techniques are put into practice in actual tasks in computer vision, machine learning, deep learning, and audio processing.
No software installation is required, as Google Colab provides a cloud-based environment that eliminates the need for local setup. Learners only need a Google account and an active internet connection. However, they may choose to write code using Anaconda, Jupyter, Spyder, or any other preferred IDE. It is important to note that flexibility may be limited when working with Computer Vision and Speech Analysis due to variations in syntax across platforms.
To effectively engage in this course, learners should have prior programming experience and a foundational understanding of statistical and inferential analysis.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.