Machine Learning Training Courses courses in United States

Introduction to AutoML and Auto-Keras• What AutoML is and why it matters• Role of Auto-Keras in automated ML workflows• Comparison with traditional manual model building• Common AutoML use casesAuto-Keras Workflow and Capabilities• Automated model search and a...

• Data analysts and aspiring data scientists
• Machine learning beginners
• AI practitioners exploring AutoML tools
• Software developers working with ML models
• Technology professionals seeking faster ML development

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Auto-Keras Training, validating their expertise in understanding Auto-Keras concepts, automated model selection, use cases, and responsible AutoML adoption.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of supervised learning workflows• Interest in automated and low-code AI solutions

Introduction to AutoML• What AutoML is and why it is used• AutoML vs traditional machine learning workflows• Components of an AutoML pipeline• Common AutoML use casesAutoML Model Selection and Optimization• Automated algorithm selection concepts• Hyperparamete...

• Data analysts and business analysts
• Machine learning beginners and practitioners
• AI and analytics professionals
• Product managers working with ML solutions
• Technology professionals exploring low-code AI platforms

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in AutoML Training, validating their expertise in understanding AutoML concepts, workflows, use cases, limitations, and responsible adoption.

Prerequisites: • Basic understanding of machine learning or data analytics concepts• Familiarity with datasets and predictive problem statements• Awareness of Python or ML workflows is beneficial• No advanced programming or data science expertise required

Introduction to Caffe and Deep Learning Workflow• Overview of the Caffe framework and ecosystem• Strengths and limitations of Caffe• Caffe vs other deep learning frameworks• Typical use cases for CaffeCaffe Architecture and Model Definition• Caffe layers and n...

• Computer vision and deep learning practitioners
• AI and machine learning engineers
• Researchers working with image-based models
• Software developers exploring Caffe
• Technology professionals evaluating deep learning frameworks

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Caffe Fundamentals Training, validating their expertise in understanding Caffe architecture, deep learning workflows, computer vision applications, and responsible model development.

Prerequisites: • Basic understanding of machine learning or deep learning concepts• Familiarity with Python or C++ programming is beneficial• Awareness of neural networks and data-driven workflows• Interest in computer vision and deep learning frameworks

Introduction to CNTK and Deep Learning Workflow• Overview of Microsoft Cognitive Toolkit (CNTK)• CNTK architecture and core components• Computation graphs and symbolic networks• Strengths and limitations of CNTKDefining Neural Networks with CNTK• Layers, param...

• AI and deep learning practitioners
• Data scientists and machine learning engineers
• Software developers working with neural networks
• Researchers exploring deep learning frameworks
• Technology professionals evaluating CNTK

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Cognitive Toolkit (CNTK) Training, validating their expertise in understanding CNTK architecture, model training workflows, evaluation techniques, and responsible deep learning practices.

Prerequisites: • Basic understanding of machine learning or deep learning concepts• Familiarity with Python programming fundamentals• Awareness of neural networks and data-driven workflows• Interest in exploring Microsoft-based deep learning frameworks

Foundations of Convolutional Neural Networks• Evolution from traditional vision techniques to CNNs• Why CNNs are effective for image data• CNN vs fully connected neural networks• Common CNN use casesCore CNN Building Blocks• Convolution operations and kernels•...

• Data scientists and machine learning engineers
• Computer vision practitioners
• AI and deep learning professionals
• Software developers working with image data
• Graduate students and technology professionals

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Convolutional Neural Networks (CNN) Training, validating their expertise in designing, training, evaluating, and responsibly applying convolutional neural networks for computer vision applications.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of neural networks and deep learning basics• Interest in computer vision and image-based AI systems

Introduction to Deep Learning• What deep learning is and how it differs from machine learning• Evolution of neural networks and deep learning• Use cases where deep learning outperforms traditional ML• Challenges and limitations of deep learningNeural Network F...

• Aspiring deep learning practitioners
• Data scientists and machine learning engineers
• Software developers working with AI systems
• AI and analytics professionals
• Graduate students and technology professionals

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Deep Learning Essentials Training, validating their expertise in understanding deep learning concepts, architectures, applications, and responsible usage.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of basic mathematics and statistics concepts• Interest in neural networks and advanced AI systems

Introduction to Keras and Deep Learning Workflow• Overview of Keras and its role in deep learning• Relationship between Keras and TensorFlow• End-to-end deep learning workflow using Keras• When to use Keras for model developmentBuilding Neural Networks with Ke...

• Aspiring deep learning practitioners
• Data scientists and machine learning engineers
• Software developers working with AI models
• AI and analytics professionals
• Students and early-career technologists

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Deep Learning with Keras Training, validating their expertise in building, training, and applying deep learning models using Keras for practical AI applications.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of neural networks and deep learning terminology• Interest in rapid deep learning model development

Introduction to TensorFlow and Deep Learning Workflow• Overview of TensorFlow and its ecosystem• Deep learning workflow using TensorFlow• Tensors, computational graphs, and operations• Building simple neural networksNeural Network Implementation with TensorFlo...

• Aspiring deep learning engineers
• Data scientists and machine learning practitioners
• Software developers working with AI systems
• AI and analytics professionals
• Graduate students and early-career technologists

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Deep Learning with TensorFlow Training, validating their expertise in building, training, and applying deep learning models using TensorFlow for practical applications.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of neural networks and basic deep learning terminology• Interest in implementing deep learning models using TensorFlow

Foundations of Deep Neural Networks• Evolution from shallow models to deep neural networks• Structure of deep neural networks• Neurons, layers, and network depth• Common use cases for deep neural networksForward and Backpropagation• Forward propagation explain...

• Data scientists and machine learning engineers
• AI and deep learning practitioners
• Software developers working with neural networks
• AI researchers and applied analytics professionals
• Graduate students and technology professionals

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Deep Neural Network Training, validating their expertise in designing, training, evaluating, and responsibly applying deep neural networks in real-world AI systems.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of linear algebra and basic statistics concepts• Interest in advanced neural network–based AI systems

Introduction to DeepLearning4J and Java-Based Deep Learning• Overview of DeepLearning4J and its use cases• DL4J architecture and ecosystem components• Comparison with other deep learning frameworks• Advantages of Java-based deep learningNeural Network Fundamen...

• Java developers and software engineers
• Machine learning practitioners working in Java ecosystems
• Enterprise application developers
• Big data and analytics professionals
• Technology professionals exploring DL4J

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in DeepLearning4J (DL4J) Training, validating their expertise in building, training, and applying deep learning models using the DeepLearning4J framework.

Prerequisites: • Basic knowledge of Java programming• Familiarity with object-oriented programming concepts• Awareness of machine learning or deep learning fundamentals• Interest in enterprise-scale AI and Java-based ML frameworks

Introduction to Vision-Language Models• What Vision-Language Models are and how they work• Relationship between vision models, language models, and VLMs• Common VLM architectures and capabilities• Use cases across industriesFoundations of VLM Fine-Tuning• Pre-...

• AI and machine learning engineers
• Computer vision and NLP practitioners
• Data scientists working with multimodal data
• AI researchers and applied AI professionals
• Technology professionals exploring VLM customization

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Fine-Tuning VLM Training, validating their expertise in understanding, preparing, evaluating, and responsibly applying Vision-Language Model fine-tuning techniques.

Prerequisites: • Basic understanding of machine learning or deep learning concepts• Familiarity with computer vision or NLP fundamentals• Awareness of multimodal or foundation models• Experience with Python or AI workflows is beneficial

Introduction to Keras and Deep Learning Workflows• What Keras is and why it is used• Keras within the modern deep learning ecosystem• End-to-end deep learning workflow using Keras• When to choose Keras for model developmentBuilding Neural Networks with Keras•...

• Aspiring deep learning practitioners
• Data scientists and machine learning engineers
• Software developers working with AI models
• AI and analytics professionals
• Students and early-career technologists

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Certification: Participants who successfully complete the training will receive an AcadNXT Certification in Keras Essentials Training, validating their expertise in building, training, evaluating, and responsibly applying deep learning models using the Keras framework.

Prerequisites: • Basic understanding of machine learning concepts• Familiarity with Python programming fundamentals• Awareness of neural networks and deep learning terminology• Interest in rapid and structured deep learning model development