Training: Machine Learning with TensorFlow
Level
AdvancedDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedMachine Learning with TensorFlow Training
The Machine Learning with TensorFlow training is an intensive course designed to introduce you to the most important tools and techniques used with one of the most popular machine learning platforms. Its practical workshop format (80% exercises, 20% theory) will quickly get you building, training, and optimizing ML models that can be applied in data analysis, image processing, and business task automation. This course is an excellent choice for anyone looking to advance their data analysis skills using TensorFlow.
Who is this training for?
Developers and data analysts who want to independently implement ML models-
Professionals with basic Python knowledge interested in applying TensorFlow in practice
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IT specialists implementing modern data analytics and process automation
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Students and enthusiasts looking to start a career in machine learning
What will you learn during this training?
- Build, train, and evaluate machine learning models in TensorFlow
- Prepare and augment data, and effectively visualize model training processes
- Apply transfer learning techniques and advanced model architectures (CNN, RNN)
- Gain practical skills in optimizing and deploying models across various use cases
- Learn data preparation, analysis, and visualization techniques for ML projects
- Build a solid foundation for further learning and advanced AI projects
Training Program
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Day 1: Introduction to Machine Learning and TensorFlow
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Module 1: ML Basics with TensorFlow
- Introduction to the TensorFlow ecosystem: installation, architecture, core functions
- Overview of machine learning and deep learning types (supervised, unsupervised, deep learning)
- Constructing artificial neural networks and introduction to optimization mechanics
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Module 2: Data Preparation and Analysis
- Data processing, cleaning, and exploration with TensorFlow and Pandas
- Data visualization techniques and preparing datasets for training
- Data augmentation techniques and managing training datasets for ML models
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Module 1: ML Basics with TensorFlow
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Day 2: Model Building, Training, and Evaluation
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Module 3: Building Machine Learning Models
- Building regression and classification models in TensorFlow/Keras
- Implementing neural layers, model optimization, and hyperparameter tuning
- Optimization techniques: hyperparameter tuning, dropout, batch normalization, early stopping
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Module 4: Validation and Result Interpretation
- Model evaluation techniques: test set splits, performance metrics with TensorBoard
- Visualizing training history and interpreting model behavior
- Debugging training processes and analyzing model outcomes
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Module 3: Building Machine Learning Models
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Day 3: Practical Projects and Applications
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Module 5: Project Work and Case Studies
- Solving real-world problems with TensorFlow (e.g., image analysis, text classification)
- Using pre-trained models to quickly build effective solutions
- Application examples: image analysis, natural language processing, time-series forecasting
- Team-based workshop: from data preparation to model deployment
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Module 6: Latest Trends and Competence Development
- Automated model training, transfer learning, integration with other frameworks
- Discussion of AI trends and career development opportunities in machine learning
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Module 5: Project Work and Case Studies
Contact us
we will organize training for you tailored to your needs
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