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Edureka's advanced AI training course is designed by industry experts to help you prepare AI Engineer, Data Scientist, NLP Engineer, etc. The objective of this Artificial Intelligence training course is to help learners improve their Computer Vision, Text Processing skills, etc. AI certification course will help you understand various concepts like OS Module, Setting the NLTK Environment, POS Tagging, etc.
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| May 03 rd |
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Edureka’s Artificial Intelligence 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.
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.
The demand for AI engineers is increasing rapidly and is expected to continue growing in the future, driven by the increasing adoption of AI technologies in various industries and the growing importance of AI skills in the job market. There are many reasons why someone might want to take up an online artificial intelligence course. Here are a few:
The field of artificial intelligence (AI) is rapidly growing and is expected to continue growing in the coming years. AI is being used in many industries such as healthcare, finance, education, and more. With the increasing adoption of AI, there is a high demand for professionals with the necessary skills and knowledge to work in this field. Here are some ways AI career growth is happening:
Overall, the field of AI provides many opportunities for career growth and development, and there is a high demand for skilled professionals in this field. Enroll in this Artificial Intelligence Certification training today.
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.
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.
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:
Edureka’s Artificial Intelligence Course enables you to move ahead in your career by helping you get skilled with the fundamentals of AI. The AI course focuses on providing hands-on experience to make you ready for any AI related opportunity.
Learn the fundamentals of Natural Language Processing (NLP), sentiment analysis, language translation, text summarization, deep learning, convolutional neural networks, recurrent neural networks, and autoencoders. Additionally, you will be working with the OpenCV library, object detection, image segmentation, and image classification along with various real-life projects.
The system requirements for this AI Course
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.
The cost of an AI course is INR 19,995, but you can get it at a discounted price of INR 17,995 and Save INR 2000. This offer is limited. You can also avail of No-Cost EMI, which starts at 5,999 / month.
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.
The salary of an AI engineer in India can vary depending on factors such as experience, skillset, location, industry, and the organization's size. On average, AI engineers in India can expect to earn a salary ranging from INR 5 lakh to INR 20 lakh per year. However, it's important to note that these figures are approximate and can vary significantly based on the factors mentioned earlier. Highly skilled and experienced AI engineers working in top tech companies or specialized fields may command higher salaries, potentially exceeding INR 20 lakh per year. Additionally, AI engineers with advanced degrees or certifications and those with expertise in specific subfields of AI, such as natural language processing or computer vision, may have better salary prospects. It's essential to refer to up-to-date salary surveys and resources for the most accurate and recent information on AI engineer salaries in India.
As part of AI Courseto become an AI engineer, you may work on various projects focusing on different aspects of AI. Some common project areas include:
Machine Learning: Projects involving training models for classification, regression, or recommendation systems. Examples could include building a spam email classifier or predicting house prices.
Natural Language Processing (NLP): Projects related to language understanding and generation, such as sentiment analysis, chatbots, or language translation systems.
Computer Vision: Projects that deal with image or video analysis, such as object detection, facial recognition, or autonomous driving systems.
Reinforcement Learning: Projects centred around training agents to make decisions in dynamic environments, like teaching a robot to navigate a maze or playing complex games.
Data Analysis: Projects involving exploratory data analysis, data preprocessing, and feature engineering, often using statistical techniques and visualizations.
These projects aim to provide hands-on experience in applying AI techniques to solve real-world problems and help you develop practical skills and understanding in the field of AI engineering.
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