Artificial Intelligence Certification Course

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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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

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