The Artificial Intelligence Certification Course is designed to help learners understand and implement core AI concepts including machine learning, neural networks, natural language processing, and deep learning. Ideal for aspiring data scientists, software engineers, analysts, and AI enthusiasts, this program empowers you with practical skills to build intelligent systems and real-world AI applications. With hands-on projects and industry-aligned curriculum, you’ll gain in-depth expertise in modern AI technologies and frameworks.

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Artificial Intelligence Certification Course

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Artificial Intelligence Certification Course?

Comprehensive Learning: Gain a solid foundation in AI, machine learning, deep learning, and NLP.
Real-World Application: Build AI-powered solutions for image recognition, chatbots, recommendation engines, and more.
Career Growth: Stand out with AI certification that demonstrates advanced technical capability.
Hands-On Experience: Work on live projects, model training, and deployment.
Expert Instruction: Learn from industry professionals and AI practitioners.

Course Description

This course is a comprehensive guide to AI theory and practical implementation, covering algorithms, tools, and real-world projects.

1. Software Developers wanting to specialize in AI. 2. Data Scientists and Analysts expanding into deep learning. 3. Students or Professionals looking to break into the AI field. 4. Tech Enthusiasts interested in intelligent systems and automation.

1. Learn from foundations to advanced AI applications. 2. Master tools like TensorFlow, Python, Keras, and OpenCV. 3. Prepare for AI roles with certification, projects, and interview prep.

What you'll learn

Fundamentals of AI: History, evolution, and future trends.

Machine Learning Algorithms: Supervised, unsupervised, and reinforcement learning.

Deep Learning and Neural Networks: CNNs, RNNs, and transfer learning.

Natural Language Processing (NLP): Text analysis, sentiment detection, and chatbots.

Computer Vision: Image classification, object detection, and facial recognition.

Model Deployment: Using Flask, Streamlit, and cloud services.

Ethics in AI: Bias, fairness, and responsible AI practices.

Requirements

Basic knowledge of Python programming.
Understanding of mathematics and statistics is helpful, but not mandatory.

Curriculum Designed by Experts

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

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

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

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

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

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

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

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

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

Single Layer Perceptron

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

Classifying handwritten digits using TensorFlow 2.0

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

Saving and Loading a Model
Face Detection using OpenCV

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

Transfer Learning
Object Detection

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

Implement RBM
Simple encoder

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

Implement Generative Adversarial Network

Where do we use Emotion and Gender Detection?
How does it work?
Emotion Detection architecture
Face/Emotion detection using Haar Cascade
Implementation on Colab

Implement Emotion and Gender Detection

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

Implement COVID RNN GRU

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

Intent Detection using LSTM

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

Auto Image Captioning

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

Build a Criminal Identification and Recognition app on Streamlit.

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.

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.

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

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

Sentiment Classification on Movie Rating Dataset

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

Free Career Councelling

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FAQ

Yes. It starts from the basics and gradually progresses to advanced AI topics.

Typically 8–12 weeks, including time for projects and assessments.

Yes. You get lifetime access to course materials and updates.

What Our Learners Say

D Daniel Morris, AI & Machine Learning Engineer

(4.0)

The Artificial Intelligence Certification Course provided a robust and well-roun...

E Emily Carter, Business Intelligence Analyst

This course helped me understand what AI really is and how it works behind the s...

L Liam Brooks, Data Science Consultant

(4.3)

The course covers all major AI concepts and frameworks, and the learning path wa...

S Sophia Turner, Junior AI Developer

Amazing course! It gave me the tools to understand and build AI models from scra...

Artificial Intelligence Certification Course

Read Review

Instructor led live online Classes

Artificial Intelligence Certification Course