Artificial Intelligence Certification Course

CertAdda’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!

Original price was: $499.00.Current price is: $449.00.

Instructor-led Advanced AI Course live online Training Schedule

 

Date

Duration

Timings

Jun 08th SAT & SUN (5 WEEKS) Weekend Batch SOLD OUT Timings – 011:00 AM to 02:00 PM (EDT)
Aug 31st SAT & SUN (5 WEEKS) Weekend Batch ⚡Filling Fast Timings – 11:00 AM to 02:00 PM (EDT)

 

Introduction to Text Mining and NLP

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

 Hands-on/Demo

  • 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

 Skills

  • 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

Topics

  • Tokenization
  • Frequency Distribution
  • Different Types of Tokenizers
  • Bigrams, Trigrams & Ngrams
  • Stemming
  • Lemmatization
  • Stopwords
  • POS Tagging
  • Named Entity Recognition

 Hands-on/Demo

  • Regex, Word, Blankline, Sentence Tokenizers
  • Bigrams, Trigrams & Ngrams
  • Stopword Removal
  • UTF encoding, dealing with URLs, hashtags
  • POS Tagging
  • Named Entity Recognition (NER)

 Skills

  • Tokenization
  • Stopword Removal
  • UTF encoding
  • POS Tagging
  • Named Entity Recognition (NER)

Analyzing Sentence Structure

Topics

  • Syntax Trees
  • Chunking
  • Chinking
  • Context Free Grammars (CFG)
  • Automating Text Paraphrasing

 Hands-on/Demo

  • Parsing Syntax Trees
  • Chunking
  • Chinking
  • Automate Text Paraphrasing using CFG’s

 Skills

  • Chunking
  • Chinking
  • Automate Text Paraphrasing

Text Classification-I

Topics

  • Machine Learning: Brush Up
  • Bag of Words
  • Count Vectorizer
  • Term Frequency (TF)
  • Inverse Document Frequency (IDF)

 Hands-on/Demo

  • Demonstrate Bag of Words Approach
  • Working with CountVectorizer()
  • Using TF & IDF

 Skills

  • Bag of Words
  • CountVectorizer()
  • TF-IDF

Introduction to Deep Learning

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

 Hands-on/Demo

  • Single Layer Perceptron

 Skills

  • Curse of Dimensionality
  • Single Layer Perceptron

Getting Started with TensorFlow 2.0

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

 Hands-on/Demo

  • Classifying handwritten digits using TensorFlow 2.0

 Skills

  • Installing and Working with TensorFlow 2.0

Convolution Neural Network

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

 Hands-on/Demo

  • Saving and Loading a Model
  • Face Detection using OpenCV

 Skills

  • Image Classification using CNN
  • Face Detection using OpenCV

Regional CNN

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

 Hands-on/Demo

  • Transfer Learning
  • Object Detection

 Skils

  • Transfer Learning
  • Object Detection
  • Mask R-CNN

Boltzmann Machine & Autoencoder

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

 Hands-on/Demo

  • Implement RBM
  • Simple encoder

 Skills

  • RBM
  • Autoencoders

Generative Adversarial Network(GAN)

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

 Hands-on/Demo

  • Implement Generative Adversarial Network

 Skills

  • Generative Adversarial Network

Emotion and Gender Detection (Self-paced)

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

 Hands-on/Demo

  • Implement Emotion and Gender Detection

 Skills

  • Emotion and Gender Detection

Introduction to RNN and GRU (Self-paced)

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

 Hands-on/Demo

  • Implement COVID RNN GRU

 Skills

  • RNN
  • GRU

LSTM (Self-paced)

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

 Hands-on/Demo

  • Intent Detection using LSTM

 Skills

  • 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

 Hands-on/Demo

  • Auto Image Captioning

 Skills

  • Auto Image Captioning
  • CNN for image processing
  • LSTM or text processing

Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)

Topics

  • Why is OpenCV used?
  • What is OpenCV
  • Applications
  • Demo: Build a Criminal Identification and Detection App

 Hands-on/Demo

  • Build a Criminal Identification and Recognition app on Streamlit.

 Skills

  • OpenCV
  • Project Implementation with OpenCV

TensorFlow for Deployment (Self-paced)

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.

 Hands-on/Demo

  • 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.

 Skills

  • Deploying model with Tensorflow

Text Classification-II (Self-paced)

Topics

  • Converting text to features and labels
  • Multinomial Naive Bayes Classifier
  • Leveraging Confusion Matrix

 Hands-on/Demo

  • Converting text to features and labels
  • Demonstrate text classification using Multinomial NB Classifier
  • Leveraging Confusion Matri

 Skills

  • Converting text to features and labels
  • Text classification
  • Confusion Matrix

In Class Project (Self-paced)

Topics

  • Sentiment Classification on Movie Rating Dataset

 Hands-on/Demo

  • 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

 Skills

  • Sentiment Analysis

What is the Artificial Intelligence Course?

CertAdda’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.

Why take up the Online Artificial Intelligence Course?

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:

  • Career advancement: Artificial intelligence is one of the fastest-growing fields in technology today, and there is a high demand for skilled professionals in this area. By taking an AI course, you can increase your skills and knowledge and make yourself more valuable to employers.
  • Stay up-to-date: The field of artificial intelligence is rapidly evolving, with new technologies and techniques being developed all the time. By taking an AI course, you can stay up-to-date with the latest trends and developments in the field.
  • Learn from experts: You will be taught by experts in the field, giving you access to their knowledge and experience. This can be invaluable in helping you understand complex topics and gain practical skills.

How will Artificial Intelligence help your career?

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:

  • Increased job opportunities: There is a growing demand for AI professionals in both technical and non-technical roles. Technical roles include AI engineers, data scientists, machine learning engineers, and software developers, while non-technical roles include AI project managers, AI consultants, and AI analysts.
  • Advancements in technology: As AI technology advances, there are new opportunities for AI professionals to develop new applications and solutions that can solve complex problems.
  • Emerging subfields: There are emerging subfields within AI, such as explainable AI, AI ethics, and AI security, which provide new opportunities for AI professionals to specialize and grow their careers.
  • Continuous learning and development: AI is a constantly evolving field, and AI professionals must continuously learn and develop new skills to stay up-to-date with the latest advancements.
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 training today.

What are the essential concepts covered in this Artificial Intelligence Course?

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.

Who should take up this Artificial Intelligence 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 Certification training today.

What are the basic skills of a Artificial Intelligence Engineer?

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.

What are the prerequisites for this Artificial Intelligence Course?

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 is the main focus of CertAdda’s Artificial Intelligence Course?

CertAdda’s Artificial Intelligence Course enables you to move ahead in your career by helping you get skilled with the fundamentals of AI. The course focuses on providing hands-on experience to make you ready for any AI related opportunity.

What will I learn from this Artificial Intelligence Course?

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.

What are the system requirements for this Artificial Intelligence 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

How will I execute the practicals in this Artificial Intelligence 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.

What if I miss a class?

You will never miss a lecture at CertAdda You can choose either of the two options:

 View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

Will I get placement assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the “create your resume” option.

Can I attend a demo session before enrollment?

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.

Who are the instructors?

All the instructors at certadda are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by certadda for providing an awesome learning experience to the participants.

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