Course Highlights
- 7-month program
- 100+ hours live online sessions
- 480+ learning hours
- 20 capstone projects
- Model deployment in cloud
- 3-month Internship -developing proof of concepts
- Combo Certifications
- Expert trainers
- Placement Records 1
- Internship and Live project
- Eligible for GOVT. OF INDIA Incentives
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Skill Type
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Course Duration
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Domain
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GOI Incentive applicable
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Course Category
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Nasscom Assessment
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Placement Assistance
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Certificate Earned
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Content Alignment Type
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NOS Details
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Mode of Delivery
Course Details
What will you learn in Artificial Intelligence Engineer (AIE) course?
Demonstrate what machine learning, deep learning, and neural networks are, as well as machine learning methods like classification, regression, clustering, and dimensional reduction.
- To address complex issues, use deep learning, machine learning, computer vision, and natural language processing.
- Creates AI models using deep learning neural networks and machine learning technologies to gain commercial insights.
- Machine learning models are used to create APIs.
- Internal stakeholders and product managers are being educated on the usefulness of the AI models they are developing.
- Create supervised and unsupervised machine learning models using SciPy and ScikitLearn.
- Build machine learning algorithms and pipelines using Apache Spark.
- Deep learning models and neural networks are created using Keras, PyTorch, and TensorFlow.
- Demonstrate what machine learning, deep learning, and neural networks are, as well as machine learning methods like classification, regression, clustering, and dimensional reduction.
- To address complex issues, use deep learning, machine learning, computer vision, and natural language processing.
- Creates AI models using deep learning neural networks and machine learning technologies to gain commercial insights.
- Machine learning models are used to create APIs.
- Internal stakeholders and product managers are being educated on the usefulness of the AI models they are developing.
- Create supervised and unsupervised machine learning models using SciPy and ScikitLearn.
- Build machine learning algorithms and pipelines using Apache Spark.
- Deep learning models and neural networks are created using Keras, PyTorch, and TensorFlow.
Why you should take Artificial Intelligence Engineer (AIE) course?
AI Engineer is the most comprehensive course with Curriculum aligned with industry and accredited by IABAC®
Mentors are PhDs., from Elite Universities with decades of experience in AI Internship from AI company is bundled with the course 1 client or startup project with Industry experts support Proven Track record of Job Assistance and Placement Service
Who should take Artificial Intelligence Engineer (AIE) course?
- Graduates and undergraduates who want to work in the field of artificial intelligence.
- IT specialists that specialize in the deployment of AI and ML on the cloud.
- Applicants who wish to strengthen their Machine Learning and Big Data skills and gain a leg up in Big Data Science.
- Professionals in the analytics industry who desire to pursue AI as a profession.
- Senior analysts and team leaders
Curriculum
- Course 1: Data Science Foundation
- Course 2: Python Essentials for Data Science
- Course 3: R Language Essentials
- Course 4: Math for Data Science
- Course 5: Statistics for Data Science
- Course 6: Data Preparation with Numpy & Pandas
- Course 7: Visualization with Python
- Course 8: Machine Learning Associate
- Course 9: Advanced Machine Learning
- Course 10: SQL for Data Science
- Course 11: Deep Learning – CNN Basics
- Course 12: Tableau Associate
- Course 13: ML Model Deploy- Flask API
- Course 14: Big Data Essentials
- Course 15: Data Science Project Execution
- Course 16: Artificial Intelligence Foundation
- Course 17: Machine Learning
- Course 18: Tensorflow 2.x Platform
- Course 19: Core Learning Algorithms
- Course 20: Understanding Neural Networks
- Course 21: Implementing Deep Neural Networks
- Course 22: Deep Computer Vision - CNN
- Course 23: Recurrent Neural Network (RNN)
- Course 24: Natural Language Processing
- Course 25: Reinforcement Learning Concepts
- Course 26: Implementing Reinforcement Learning
- Course 27: Generative Adversarial Network
Tools you will learn in Artificial Intelligence Engineer (AIE) course
- The artificial intelligence engineer course curriculum covers some of the most widely used artificial intelligence tools, including Python, Scikit Learn, TensorFlow, Theano, Keras, PyTorch, and CNTK.
- Candidates will be provided an in-depth understanding of the most popular and highly graded tools and software platforms extensively utilized for AI research and design.