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AI with Python

Apply & Implement ML Models

EDUCBA

What makes this course unique is its step-by-step structure combining theory with practical coding demonstrations using Jupyter Notebook, ensuring learners can directly apply concepts to real-world problems. Through integrated lessons on documentation and visualization, participants will also learn how to clearly present AI projects. Designed for intermediate-level learners, this course bridges the gap between basic knowledge and advanced AI applications, empowering you to confidently build, test, and refine machine learning and deep learning models.

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What's inside

Syllabus

Foundations of AI with Python

This module builds a strong foundation in Artificial Intelligence by introducing Python’s role in AI, exploring the basics of machine learning, and emphasizing the importance of data processing. Learners will also examine the concepts of bias, variance, and model evolution while gaining hands-on exposure to Scikit-learn, a widely used machine learning library. By the end of this module, learners will be equipped with essential skills to begin building AI solutions confidently.

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Learners who complete AI with Python: Apply & Implement ML Models will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI with Python: Apply & Implement ML Models.

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Artificial Intelligence

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Storytelling with Data

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Deep Learning with TensorFlow and Keras

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Provides a practical approach to deep learning with a strong focus on TensorFlow and Keras. It's suitable for those looking to deepen their understanding through implementation. It covers various applications and useful reference for building and deploying models.

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Mastering Neural Network Computer Vision with...

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A recent publication focusing specifically on computer vision applications with TensorFlow and Keras. is great for those wanting to dive into a contemporary and popular area of deep learning using Keras. It's a valuable resource for students and professionals in computer vision.

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3D Data Science with Python

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Bridges volumetric point cloud analysis with Python, teaching LiDAR processing, spatial meshing, and 3D machine vision using modern scientific computing toolchains.

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Hands-On Computer Vision with TensorFlow 2

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Demonstrates practical visual recognition techniques, guiding readers through convolutional networks, transfer learning, object detection, and image segmentation with practical Keras code.

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Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.

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Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.

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Introduction to Python for Absolute Beginners

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Probabilistic Graphical Models

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Python Machine Learning

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A comprehensive guide to machine learning with Python, including significant coverage of Keras and TensorFlow. This German book is valuable for German-speaking learners to gain a solid understanding and practical skills in Keras within the broader ML ecosystem.

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