What learn
-
Build and train neural networks using TensorFlow and Keras
-
Implement CNNs for image classification and object detection
-
Apply RNNs and LSTMs for natural language processing tasks
-
Use transfer learning to leverage pre-trained models effectively
-
Deploy machine learning models to production with TF Serving
Meetings
Requirements
- <p>Solid understanding of Python programming</p><p>Basic knowledge of linear algebra and calculus is helpful</p><p>A computer with a modern GPU is recommended but not required (Google Colab can be used)</p>
Description
Dive deep into machine learning and deep learning with TensorFlow, the most widely used ML framework in the industry. This course takes you from understanding basic ML concepts to building and deploying sophisticated neural network models.
You will learn linear regression, classification, clustering, convolutional neural networks for image recognition, recurrent neural networks for sequence data, natural language processing, generative adversarial networks, and reinforcement learning fundamentals. Each topic includes theoretical explanations followed by hands-on TensorFlow implementations.
The course culminates with a capstone project where you build, train, and deploy a production-ready ML model using TensorFlow Serving and Docker. You will graduate with the skills to tackle real-world machine learning challenges.
Frequently Asked Question
About Instructor
About Instructor
Award-winning UI/UX designer and creative director with 10 years of experience working with top brands. Expert in Figma, Adobe Creative Suite, and design systems. Specializes in teaching user-centered design thinking and prototyping for digital products.
