Advanced AI and Machine Learning using TensorFlow
Advanced AI and Machine Learning with TensorFlow is a specialized course centered on deep learning and AI methodologies. Using TensorFlow as the main technology, participants learn how to create, train, and implement sophisticated machine learning models, such as neural networks, to tackle challenging issues like image recognition, natural language processing, and predictive analytics.
- 10+ Courses
- 30+ Projects
- 400 Hours
Advanced AI and Machine Learning using TensorFlow Training is suitable for the following target audiences:
Data Scientists: Perfect for data scientists who wish to study TensorFlow and use it in cutting-edge AI projects to improve their machine learning abilities.
Machine Learning Engineers: Engineers in charge of creating, refining, and training machine learning models are the target audience for Machine Learning Engineers.
AI Researchers: Ideal for experts working on AI projects who require hands-on experience putting advanced models into practice with TensorFlow.
Software Developers: Designed for developers using TensorFlow to incorporate AI and machine learning capabilities into software applications.
IT Professionals and Analysts: This course is excellent for IT professionals who want to gain practical expertise with TensorFlow before entering AI and machine learning careers.
Machine Learning Engineer: Developing and implementing ML models in technology, healthcare, and finance sectors.
AI Specialist: Developing AI tools to improve decision-making, streamline procedures, and automate jobs.
Data Scientist: Using cutting-edge machine learning methods to evaluate data and produce forecasts.
AI Research Scientist: This person works in academia or R&D departments and conducts AI research to create new models and algorithms.
AI Software Developer: Using TensorFlow, develop AI-powered apps and solutions to address practical issues.
Jobs in fields like technology, healthcare, finance, and manufacturing offer competitive pay and chances for career advancement, making these positions highly sought after in both the USA and Canada.
- Revisit fundamental principles of deep learning
- Architecture and workflow of TensorFlow 2.x
- Computational graphs, anxious execution, and tensors
- Constructing your initial neural network from start to finish
- Practical lab: Develop and train a basic MLP in TensorFlow.
- Activation functions, bulk normalization and initialization
- Regularization and dropout strategies
- Learning rate schedules and optimizers
- Handling of overfitting and underfitting
- Lab Work : Develop and optimize a deep, completely connected model.
- TensorFlow’s architecture and layers for CNNs
- Padding, feature extraction, and pooling
- Fine-tuning and transfer learning with the TF-Hub
- Hands On assignment : Image classification utilizing transfer learning and CNNs
- Fundamentals of RNNs, LSTM, and GRU
- Introduction to Transformers and Attention
- TensorFlow Text: Tokenization and embeddings
- Hands On Practice: Develop a sentiment analysis or text classification model.
- Autoencoders
- Variational Autoencoders (VAE)
- Introduction to GANs and training stability advice
- Lab work : Utilize the GAN architecture to construct an image generator.
- Agents, environments, and rewards are the primary concepts of RL.
- Policy gradients and Q-learning
- Implementation of Deep Q-Networks (DQN) with TensorFlow
- Practice: Develop a basic RL agent in an OpenAI Gym-style environment.
- Quantization and consolidation of the model
- TensorFlow Lite for mobile and peripheral applications
- Serving and APIs for inference in TensorFlow
- Practical: Export and deploy a trained model to a demonstration application.
- Students choose a genuine issue (generative, NLP, RL, or vision).
- Develop and execute an exhaustive model infrastructure.
- Please submit the project report, training data, and code.
- Feedback and presentation session
The course focuses on advanced deep learning and applied AI using TensorFlow, including CNNs, transformers, generative models and deployment techniques.
Yes. You should already understand core ML concepts like supervised learning, neural networks and Python programming.
Yes. The course is fully based on TensorFlow 2.x and its ecosystem, including Keras, TF Hub and TensorFlow Lite.
Yes. Each week includes coding labs, model building exercises and practical evaluation tasks.
Projects may include image classification, text analysis, GAN based generation and reinforcement learning models.
Yes. You will learn model tuning, optimization, deployment with TensorFlow Serving and lightweight deployment with TF Lite.
It helps, but not required. Cloud platforms like Google Colab can be used to train deep learning models.
Yes. Students complete a capstone project that covers data handling, model development, training, evaluation and deployment.
Yes. A certificate of completion is provided after finishing course tasks and the capstone.
Yes. It strengthens the skills needed for the certification. Certification preparation will need additional time after 8 weeks.
We currently offer online sessions with flexible weekday/weekend batches for 8 weeks. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
You can register via our website https://mediumblue-spoonbill-560747.hostingersite.com/, or reach out to our support teams via phone, email, or WhatsApp. We’ll help you with batch schedules and payment options.
Email info@mediumblue-spoonbill-560747.hostingersite.com Call Us +1-347-4082054
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Job opportunities in USA and Canada
Machine Learning Engineer: Developing and implementing ML models in technology, healthcare, and finance sectors.
AI Specialist: Developing AI tools to improve decision-making, streamline procedures, and automate jobs.
Data Scientist: Using cutting-edge machine learning methods to evaluate data and produce forecasts.
AI Research Scientist: This person works in academia or R&D departments and conducts AI research to create new models and algorithms.
AI Software Developer: Using TensorFlow, develop AI-powered apps and solutions to address practical issues.
Jobs in fields like technology, healthcare, finance, and manufacturing offer competitive pay and chances for career advancement, making these positions highly sought after in both the USA and Canada.
