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Artificial Intelligence Certification Course in Hyderabad
Artificial Intelligence Certification Course in Hyderabad
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Live Online Classes starting on 28th Dec 2024
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Why enroll for Artificial Intelligence Certification Course in Hyderabad?
Artificial Intelligence Course in Hyderabad Benefits
Today, the amount of data that is generated, by both humans and machines, far outpaces humans' ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning, allows organizations to improve core business processes and is the future of all complex decision making. The best way to land you a good job with a handsome salary in this domain is to get AI Certification.
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Why Artificial Intelligence Certification Course from edureka in Hyderabad
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Artificial Intelligence Certification Course
Artificial Intelligence Skills Covered
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Artificial Intelligence Course in Hyderabad Syllabus
Curriculum Designed by Experts
The Artificial Intelligence course in Hyderabad offers a thoughtfully designed curriculum that covers a wide range of AI topics. The curriculum is structured to provide participants with a strong foundation in AI principles and practical skills. Participants will engage in hands-on projects, coding exercises, and discussions throughout the course to reinforce their learning. They will receive guidance from experienced instructors who are experts in the field of AI. By the end of the course, participants will have a strong foundation in AI principles, practical skills in developing AI systems, and the ability to apply AI techniques across diverse domains. Join our Artificial Intelligence course in Hyderabad and embark on a transformative journey into the world of AI.
Introduction to Text Mining and NLP
8 Topics
Topics
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
![skill]( "Hands On Experience")
- Install NLTK Packages using NLTK Downloader
- Accessing your operating system using the OS Module in Python
- How to read json format, understand key-value pairs, and for that matter, understand uses of pkl files
![skill]( "skill you will learn")
- Reading & Writing .txt Files from/to your Local
- Reading & Writing .docx Files from/to your Local
- Working with the NLTK Corpora
Extracting, Cleaning and Preprocessing Text
9 Topics
Topics
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
![skill]( "Hands On Experience")
- Regex, Word, Blankline, Sentence Tokenizers
- Bigrams, Trigrams & Ngrams
- Stopword Removal
- UTF encoding, dealing with URLs, hashtags
- POS Tagging
- Named Entity Recognition (NER)
![skill]( "skill you will learn")
- Tokenization
- Stopword Removal
- UTF encoding
- POS Tagging
- Named Entity Recognition (NER)
Analyzing Sentence Structure
5 Topics
Topics
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
![skill]( "Hands On Experience")
- Parsing Syntax Trees
- Chunking
- Chinking
- Automate Text Paraphrasing using CFG’s
![skill]( "skill you will learn")
- Chunking
- Chinking
- Automate Text Paraphrasing
Text Classification-I
5 Topics
Topics
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
![skill]( "Hands On Experience")
- Demonstrate Bag of Words Approach
- Working with CountVectorizer()
- Using TF & IDF
![skill]( "skill you will learn")
- Bag of Words
- CountVectorizer()
- TF-IDF
Introduction to Deep Learning
11 Topics
Topics
- What is Deep Learning?
- Curse of Dimensionality
- Machine Learning vs. Deep Learning
- Use cases of Deep Learning
- Human Brain vs. Neural Network
- What is Perceptron?
- Learning Rate
- Epoch
- Batch Size
- Activation Function
- Single Layer Perceptron
![skill]( "Hands On Experience")
- Single Layer Perceptron
![skill]( "skill you will learn")
- Curse of Dimensionality
- Single Layer Perceptron
Getting Started with TensorFlow 2.0
14 Topics
Topics
- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Activation Function
- Layer Types
- Model Compilation
- Model Optimizer
- Model Loss Function
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
![skill]( "Hands On Experience")
- Classifying handwritten digits using TensorFlow 2.0
![skill]( "skill you will learn")
- Installing and Working with TensorFlow 2.0
Convolution Neural Network
12 Topics
Topics
- Image Classification Example
- What is Convolution
- Convolutional Layer Network
- Convolutional Layer
- Filtering
- ReLU Layer
- Pooling
- Data Flattening
- Fully Connected Layer
- Predicting a cat or a dog
- Saving and Loading a Model
- Face Detection using OpenCV
![skill]( "Hands On Experience")
- Saving and Loading a Model
- Face Detection using OpenCV
![skill]( "skill you will learn")
- Image Classification using CNN
- Face Detection using OpenCV
Regional CNN
20 Topics
Topics
- Regional-CNN
- Selective Search Algorithm
- Bounding Box Regression
- SVM in RCNN
- Pre-trained Model
- Model Accuracy
- Model Inference Time
- Model Size Comparison
- Transfer Learning
- Object Detection – Evaluation
- mAP
- IoU
- RCNN – Speed Bottleneck
- Fast R-CNN
- RoI Pooling
- Fast R-CNN – Speed Bottleneck
- Faster R-CNN
- Feature Pyramid Network (FPN)
- Regional Proposal Network (RPN)
- Mask R-CNN
![skill]( "Hands On Experience")
- Transfer Learning
- Object Detection
![skill]( "skill you will learn")
- Transfer Learning
- Object Detection
- Mask R-CNN
Boltzmann Machine & Autoencoder
9 Topics
Topics
- What is Boltzmann Machine (BM)?
- Identify the issues with BM
- Why did RBM come into the picture?
- Step-by-step implementation of RBM
- Distribution of Boltzmann Machine
- Understanding Autoencoders
- Architecture of Autoencoders
- Brief on types of Autoencoders
- Applications of Autoencoders
![skill]( "Hands On Experience")
- Implement RBM
- Simple encoder
![skill]( "skill you will learn")
- RBM
- Autoencoders
Generative Adversarial Network(GAN)
7 Topics
Topics
- Which Face is Fake?
- Understanding GAN
- What is Generative Adversarial Network?
- How does GAN work?
- Step by step Generative Adversarial Network implementation
- Types of GAN
- Recent Advances: GAN
![skill]( "Hands On Experience")
- Implement Generative Adversarial Network
![skill]( "skill you will learn")
- Generative Adversarial Network
Emotion and Gender Detection (Self-paced)
5 Topics
Topics
- Where do we use Emotion and Gender Detection?
- How does it work?
- Emotion Detection architecture
- Face/Emotion detection using Haar Cascade
- Implementation on Colab
![skill]( "Hands On Experience")
- Implement Emotion and Gender Detection
![skill]( "skill you will learn")
- Emotion and Gender Detection
Introduction to RNN and GRU (Self-paced)
14 Topics
Topics
- Issues with Feed Forward Network
- Recurrent Neural Network (RNN)
- Architecture of RNN
- Calculation in RNN
- Backpropagation and Loss calculation
- Applications of RNN
- Vanishing Gradient
- Exploding Gradient
- What is GRU?
- Components of GRU
- Update gate
- Reset gate
- Current memory content
- Final memory at current time step
![skill]( "Hands On Experience")
- Implement COVID RNN GRU
![skill]( "skill you will learn")
- RNN
- GRU
LSTM (Self-paced)
18 Topics
Topics
- What is LSTM?
- Structure of LSTM
- Forget Gate
- Input Gate
- Output Gate
- LSTM architecture
- Types of Sequence-Based Model
- Sequence Prediction
- Sequence Classification
- Sequence Generation
- Types of LSTM
- Vanilla LSTM
- Stacked LSTM
- CNN LSTM
- Bidirectional LSTM
- How to increase the efficiency of the model?
- Backpropagation through time
- Workflow of BPTT
![skill]( "Hands On Experience")
- Intent Detection using LSTM
![skill]( "skill you will learn")
- LSTM
- Sequence Prediction
- Sequence Generation
Auto Image Captioning Using CNN LSTM (Self-paced)
Topics
- Auto Image Captioning
- COCO dataset
- Pre-trained model
- Inception V3 model
- The architecture of Inception V3
- Modify the last layer of a pre-trained model
- Freeze model
- CNN for image processing
- LSTM or text processing
![skill]( "Hands On Experience")
- Auto Image Captioning
![skill]( "skill you will learn")
- Auto Image Captioning
- CNN for image processing
- LSTM or text processing
Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)
4 Topics
Topics
- Why is OpenCV used?
- What is OpenCV
- Applications
- Demo: Build a Criminal Identification and Detection App
![skill]( "Hands On Experience")
- Build a Criminal Identification and Recognition app on Streamlit.
![skill]( "skill you will learn")
- OpenCV
- Project Implementation with OpenCV
TensorFlow for Deployment (Self-paced)
13 Topics
Topics
- Use Case: Amazon’s Virtual Try-Out Room.
- Why Deploy models?
- Model Deployment: Intuit AI models
- Model Deployment: Instagram’s Image Classification Models
- What is Model Deployment
- Types of Model Deployment Techniques
- TensorFlow Serving
- Browser-based Models
- What is TensorFlow Serving?
- What are Servables?
- Demo: Deploy the Model in Practice using TensorFlow Serving
- Introduction to Browser based Models
- Demo: Deploy a Deep Learning Model in your Browser.
![skill]( "Hands On Experience")
- Learn and build a program that Detects Faces using your webcam using OpenCV.
- Learn Hyper parameter tuning techniques in Keras on a Fashion Dataset.
- Build and deploy a model using TensorFlow Serving.
- Build a neural network model for Handwritten digits use activation function, batch size, Optimizer and learning rate for betterment of you model.
- Build a Object detection model and detection is done by providing a video the model accurately identifies the objects that are depicted in the video.
![skill]( "skill you will learn")
- Deploying model with Tensorflow
Text Classification-II (Self-paced)
17 Topics
Topics
- Converting text to features and labels
- Multinomial Naive Bayes Classifier
- Leveraging Confusion Matrix
![skill]( "Hands On Experience")
- Converting text to features and labels
- Demonstrate text classification using Multinomial NB Classifier
- Leveraging Confusion Matri
![skill]( "skill you will learn")
- Converting text to features and labels
- Text classification
- Confusion Matrix
In Class Project (Self-paced)
1 Topics
Topics
- Sentiment Classification on Movie Rating Dataset
![skill]( "Hands On Experience")
- Implement all the text processing techniques starting with tokenization
- Express your end to end work on Text Mining
- Implement Machine Learning along with Text Processing
![skill]( "skill you will learn")
- Sentiment Analysis
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Artificial Certification Course in Hyderabad Description
Artificial Intelligence (AI) course in Hyderabad delves into advanced technology. This comprehensive course is designed to provide participants with a complete understanding of AI concepts, algorithms, and applications. Through theoretical knowledge and practical implementation, participants will learn how to design and develop AI systems using machine learning techniques. Explore topics such as neural networks, natural language processing, computer vision, and data analytics. With hands-on projects, real-world case studies, and expert guidance, this course will equip you with the skills to excel in AI. Join Edureka’s Artificial Intelligence course in Hyderabad and unlock the potential of intelligent systems.
What is the Artificial Intelligence Course in Hyderabad?
Edureka’s Artificial Intelligence Course in Hyderabad 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.
Who should take up this Artificial Intelligence Course in Hyderabad?
The Artificial Intelligence course in Hyderabad 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 Certification training today.
What is the duration of this AI Course in Hyderabad?
The AI course offered by Edureka in Hyderabad is 21 hours long and designed to cover essential concepts and practical applications effectively.
What are the prerequisites for this Artificial Intelligence Course in Hyderabad?
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.
What will I learn from this Artificial Intelligence Course in Hyderabad?
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.
Artificial Intelligence Course in Hyderabad Projects
Airline Sentiment Analysis
In this project, you will be provided with a dataset having tweets about 6 US Airlines along with their sentiments: positive, negative and neutral. You have to perform Sentiment ....Face Mask Detection
The goal of the project is to create a Deep Learning model to detect in real time whether a person is wearing a face mask or not.Artificial Intelligence Certification in Hyderabad
To unlock Edureka’s Artificial Intelligence course completion certificate in Hyderabad, you must ensure the following:
Completely participate in this Artificial Intelligence course in Hyderabad.
Evaluation and completion of the quizzes and projects listed.
An Artificial Intelligence Certification in Hyderabad enhances career prospects by providing in-demand AI skills, practical knowledge, and industry-recognized credentials, enabling professionals to secure roles in AI-driven industries and advance their careers in a competitive market.
The AI Certification Course in Hyderabad offers industry-relevant skills, hands-on projects, expert-led guidance, and job placement support, providing a strong foundation for thriving in the rapidly growing AI job market.
With this AI certification in Hyderabad, you can pursue roles such as AI Engineer, Machine Learning Specialist, Data Scientist, Business Intelligence Developer, NLP Engineer, Robotics Process Automation (RPA) Developer, and AI Consultant.
Artificial Intelligence (AI) is a complex and rapidly evolving field, learning its capabilities and functionality requires appropriate direction and a well-structured training path. Beginners interested in a career in Artificial Intelligence using Python can sign up for our training and earn certificates to demonstrate their expertise in this domain.
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Artificial Intelligence Course in Hyderabad FAQs
Is coding necessary for AI?
Yes, coding is necessary for AI. Key languages include Python for its libraries like TensorFlow and PyTorch, R for data analysis, Java for scalability, C++ for performance, and JavaScript for web-based AI applications.
Which top companies in Hyderabad are actively hiring AI engineers?
Top companies in Hyderabad actively hiring AI engineers include:
Tata Consultancy Services (TCS)
HCL Technologies
Accenture
Cognizant
Tech Mahindra
Wipro
Amazon
IBM
Genpact
EY (Ernst & Young)
These companies frequently seek AI talent for roles in machine learning, natural language processing, and AI development projects.
What are some beginner projects in AI?
Beginner AI projects include creating a chatbot, building a recommendation system, developing a spam email classifier, implementing sentiment analysis on social media data, designing a basic image recognition tool, and building a virtual assistant. These projects help in learning algorithms, data processing, and model training.
What is the cost of the Artificial Intelligence Course in Hyderabad?
The cost of the Artificial Intelligence Course in Hyderabad is INR 17995.
What is the salary for AI Engineer in Hyderabad?
The salary of an AI Engineer in Hyderabad can vary depending on factors such as experience, skills, company size, and industry. On average, an AI Engineer in Hyderabad can expect to earn between INR 6 lakhs to 18 lakhs per annum. However, salaries can go higher for professionals with more experience and specialized skills.
How can I become an AI Engineer?
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.
Be future ready, start learning
Have more questions?
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