05-23-2025, 07:36 AM
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Certified Machine Learning Associate
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 13m | Size: 552 MB
Master Machine Learning with hands-on projects covering Supervised, Unsupervised, Deep Learning.[/center]
What you'll learn
Gain foundational understanding of AI and machine learning concepts, algorithms, and applications.
Develop practical skills in Python, data preprocessing, and implementing supervised and unsupervised learning models.
Build and train deep learning models including CNNs and RNNs using TensorFlow/Keras.
Complete a capstone project and deploy machine learning models for real-world use cases.
Requirements
Basic computer literacy and willingness to learn programming. No prior experience in AI or machine learning required; all key concepts and tools will be introduced step-by-step. A computer with internet access to install Python and libraries like Anaconda and TensorFlow.
Description
Are you ready to launch your career in one of the most in-demand tech domains? The Certified Machine Learning Associate course is designed for beginners and intermediate learners who want to build a solid foundation in machine learning through a practical, hands-on approach. In this course, you'll learn: The fundamentals of Supervised and Unsupervised Learning (Linear Regression, Classification, Clustering, PCA) Advanced techniques using Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) Essential algorithms like K-Means, Q-Learning, and Backpropagation Real-world problem solving through capstone projects, such as Smart Agriculture AI for disease detection We use Python along with popular libraries like scikit-learn, TensorFlow, and Keras to help you build, evaluate, and deploy machine learning models. By the end of this course, you'll have not only theoretical knowledge but also practical experience in solving real-world problems using AI. You'll also learn how to evaluate model performance using precision, recall, and confusion matrices. The course includes interactive quizzes, assignments, and real datasets to ensure deep understanding. By completing the final capstone project, you'll gain the confidence to apply ML in practical scenarios or research.Whether you're a student, aspiring data scientist, or software engineer, this course will help you become job-ready with portfolio-worthy projects and a certificate to validate your skills.
Who this course is for
Beginners and aspiring data scientists who want a comprehensive introduction to AI/ML. Developers and professionals looking to upskill in machine learning techniques and deep learning frameworks. Students and tech enthusiasts eager to build hands-on AI/ML projects and gain industry-relevant skills.
Certified Machine Learning Associate
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 13m | Size: 552 MB
Master Machine Learning with hands-on projects covering Supervised, Unsupervised, Deep Learning.[/center]
What you'll learn
Gain foundational understanding of AI and machine learning concepts, algorithms, and applications.
Develop practical skills in Python, data preprocessing, and implementing supervised and unsupervised learning models.
Build and train deep learning models including CNNs and RNNs using TensorFlow/Keras.
Complete a capstone project and deploy machine learning models for real-world use cases.
Requirements
Basic computer literacy and willingness to learn programming. No prior experience in AI or machine learning required; all key concepts and tools will be introduced step-by-step. A computer with internet access to install Python and libraries like Anaconda and TensorFlow.
Description
Are you ready to launch your career in one of the most in-demand tech domains? The Certified Machine Learning Associate course is designed for beginners and intermediate learners who want to build a solid foundation in machine learning through a practical, hands-on approach. In this course, you'll learn: The fundamentals of Supervised and Unsupervised Learning (Linear Regression, Classification, Clustering, PCA) Advanced techniques using Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) Essential algorithms like K-Means, Q-Learning, and Backpropagation Real-world problem solving through capstone projects, such as Smart Agriculture AI for disease detection We use Python along with popular libraries like scikit-learn, TensorFlow, and Keras to help you build, evaluate, and deploy machine learning models. By the end of this course, you'll have not only theoretical knowledge but also practical experience in solving real-world problems using AI. You'll also learn how to evaluate model performance using precision, recall, and confusion matrices. The course includes interactive quizzes, assignments, and real datasets to ensure deep understanding. By completing the final capstone project, you'll gain the confidence to apply ML in practical scenarios or research.Whether you're a student, aspiring data scientist, or software engineer, this course will help you become job-ready with portfolio-worthy projects and a certificate to validate your skills.
Who this course is for
Beginners and aspiring data scientists who want a comprehensive introduction to AI/ML. Developers and professionals looking to upskill in machine learning techniques and deep learning frameworks. Students and tech enthusiasts eager to build hands-on AI/ML projects and gain industry-relevant skills.
Code:
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