Curriculum

Externship Program - Artificial Intelligence

Module-1: Introduction to Artificial Intelligence
  • What is Artificial Intelligence 
  • History of Artificial Intelligence 
  • Use Cases of Artificial Intelligence 
  • Role of Machine Learning Engineer 
  • Machine Learning Tools & Packages

Module-2: Python for Data Science
  • Python Basics
  • Python Packages
  • Working with NUMPY
  • Working with Pandas 
  • Introduction to Data Visualization
  • Introduction to Matplotlib and Seaborn
  • Basic Plotting with Matplotlib and Seaborn

Module-3: Data Wrangling Techniques
  • Introduction to Data Preprocessing
  • Importing the Dataset 
  • Handling Missing data 
  • Working with Categorical Data 
  • Splitting the data into Train and Test set 
  • Feature Scaling

Module-4: Introduction to Neural Networks
  • The Neuron 
  • The Activation Function 
  • How do Neural Networks work? 
  • How do Neural Networks learn? 
  • Gradient Descent 
  • Stochastic Gradient Descent 
  • Backpropagation

Module-5: Tensorflow & Keras
  • Introduction to Tensorflow & Keras Framework 
  • Introduction to the Sequential Mode
  • Activation functions 
  • Layers 
  • Training 
  • Loss function 
  • Building ANN Using Tensor flow
  • Evaluating Improving and Tuning ANN

Module-6: Convolutional Neural Networks
  • Introduction to Convolutional Neural Networks 
  • What are convolutional neural networks? 
       Step 1 - Convolution Operation  
       Step 2 - Pooling 
       Step 3 - Flattening 
       Step 4 - Full Connection Classification of images using CNN 
  • Evaluating, Improving, and Tuning the CNN 
  • Video Analysis using OpenCV
  • Object detection using YOLO

Module-7: Transfer Learning
  • Introduction to Transfer Learning Models
  • How does Transfer Learning work?
  • When should we use Transfer Learning?
  • Approaches to transfer Learning
  1. Inception V3
  2. Xception
  3. Resnet-50
  4. VGG-19

Module-8: Recurrent Neural Networks
  • Introduction to Recurrent Neural Networks 
  • The idea behind Recurrent Neural Networks 
  • The Vanishing Gradient Problem 
  • LSTMs 
  • LSTM Variations Predicting Google stock prices using RNN 
  • Evaluating, Improving, and Tuning the RNN

Module-9: Natural Language Processing
  • Introduction to Natural Language Processing 
  • Introduction to NLTK 
  • Bag of Words model 
  • Natural Language Processing in Python 
  • Sentiment analysis using Natural Language Processing

Module-10: Build and Deploy an AI Application
  • Introduction to different modes of Deployments
  • Working with Flask framework 
  • Building an application with Flask Framework 
  • Integrating Deep learning model with Web Application