Deep Learning With TensorFlow and Keras [FREE] | TensorFlow Tutorial | Keras Tutorial | Simplilearn

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Deep Learning with Keras and TensorFlow Full Course 2026 is designed to help beginners and professionals understand how modern AI systems are built and trained. The course starts with deep learning basics and neural network concepts, then gradually introduces TensorFlow and Keras for hands-on model development. You’ll learn how to build, train, and evaluate deep learning models for real-world problems like image classification and prediction tasks. Key topics such as activation functions, loss functions, optimizers, and model tuning are explained in simple terms. By the end of the course, learners will be confident in applying deep learning techniques using industry-standard tools and frameworks. This course is ideal for anyone aiming to move into AI, machine learning, or advanced data science roles.

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➡️ About Professional Certificate in AI and Machine Learning

Excel in AI, ML, and Generative AI in this artificial intelligence course with hands-on Python, Deep Learning, and Agentic AI. Gain real-world experience, practical artificial intelligence training, and skills in AI automation, LLMs, and machine learning to future-proof your career.

Key Features

✅ Simplilearn Career Service helps you get noticed by top hiring companies
✅ Receive program completion certificate from Simplilearn, in partnership with Purdue University
✅ Practical exposure to ChatGPT, LLM-based AI solutions, and other AI applications.
✅ Live interactive sessions on the latest AI trends, such as ChatGPT, generative AI, prompt engineering, and more
✅ Core curriculum delivered in live classes by industry experts
✅ 15+ hands-on projects, including 3 capstones from various industry domains.
✅ Live online masterclasses delivered by industry and IBM experts
✅ Become eligible for Purdue University Alumni Association membership
✅ Exclusive hackathons and Ask Me Anything sessions by IBM

Skills Covered
✅ Generative AI
✅ Prompt Engineering
✅ ChatGPT
✅ Conversational AI
✅ Large Language Models
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Speech Recognition
✅ Machine Learning Algorithms
✅ Agentic AI

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