Upcoming Batches for Artificial Intelligence Certification Course
Course Description
Shelloak's Advanced Artificial Intelligence Course helps you master essentials of text processing and classifying texts along with important concepts such as Tokenization, Stemming, Lemmatization, POS tagging and many more. You will learn to perform image pre-processing, image classification, transfer learning, object detection, computer vision and also be able implement popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. This course is curated by the industry experts after an extensive research to meet the latest industry requirements and demands. Unleash the power of Artificial Intelligence and accelerate your career— join the global revolution now!
Prerequisite
Prior knowledge of Python and Machine Learning will be helpful but not at all mandatory. To refresh your skills in Python and ML, we will provide self-paced videos absolutely free as prerequisites in your LMS.
Who should take this course?
The Artificial Intelligence course is suitable for anyone who wants to stay up-to-date with the latest advances in AI and wants to build the skills needed to develop and deploy intelligent systems.
This course will be ideal for the following professionals.
- Freshers
- Python Developers
- Researchers
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- NLP Engineers
- Software Testers
- Software Developers
If you are one of the above, then do not hesitate to talk to our assistant team and enroll in our AI Course training today.
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This Course is well researched amalgamation of Natural Language Processing and Deep Learning, specifically designed for professionals and beginners to meet the industry standards. This course gives you an in-depth understanding of Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing using Python’s NLTK package, CNN, RCNN, RNN, LSTM, RBM, and their implementation using TensorFlow 2.0 package. You will learn to build real-time projects on NLP and Deep Learning, to make you industry-ready and help you to kickstart your career in this domain.
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This Artificial Intelligence Course provides in-depth knowledge of concepts such as Natural Language Processing, Text Classification, Text Processing, Image Processing, Object Detection, Deep Learning, TensorFlow, OpenCV, and many more.
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Artificial Intelligence (AI) is a broad field with many subfields, and the skills required for an AI engineer can vary depending on the specific area of expertise. However, there are some basic skills that most AI engineers should possess:
- Strong Programming Skills: AI engineers need to have a strong foundation in programming languages such as Python, C++, Java, or R. This includes knowledge of data structures, algorithms, and object-oriented programming.
- Machine Learning: AI engineers must have a solid understanding of the concepts and algorithms of machine learning. This includes knowledge of supervised and unsupervised learning, deep learning, and natural language processing (NLP).
- Data Structures and Algorithms: A deep understanding of data structures and algorithms is essential for designing and implementing efficient algorithms for large data sets. This also includes knowledge of big data technologies and distributed computing.
- Statistics and Probability: Knowledge of statistics and probability is essential for understanding and designing machine learning algorithms. AI engineers need to know concepts like hypothesis testing, regression analysis, and Bayesian networks.
- Problem Solving: AI engineers must have strong problem-solving skills to design and implement complex AI systems. They must be able to identify problems, break them down into smaller components, and develop solutions.
- Creativity: AI engineers must be creative thinkers to develop novel solutions to complex problems. They should be able to think outside the box and come up with innovative ideas.
- Ethics and Accountability: AI engineers must understand the ethical implications of their work and the impact it has on society. They must ensure their AI systems are transparent, explainable, and accountable.
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The system requirements for this AI Course
- A system with an Intel i3 processor or above
- A minimum of 4GB RAM (8GB or above is recommended for faster processing)
- 50 GB HDD Storage
- Operating system: 32-bit or 64-bit
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You will execute your Assignments/Case Studies using Python Jupyter Notebook/Google Colab. Detailed step-by-step installation guides are available on the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.
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We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
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To become an AI engineer, you can follow these steps:
Learn programming: Start with languages like Python, Java, or C++, and gain proficiency in data structures and algorithms.
Understand mathematics and statistics: Study linear algebra, calculus, probability, and statistics to grasp the foundations of AI.
Master machine learning: Learn about various ML algorithms, techniques, and frameworks such as TensorFlow or PyTorch.
Gain practical experience: Work on real-world projects, participate in Kaggle competitions and build a portfolio to showcase your skills.
Specialize in AI subfields: Explore areas like natural language processing, computer vision, or reinforcement learning.
Continuous learning: Stay updated with the latest advancements and research in AI through online courses, tutorials, and academic papers.