Curriculum
Summer Internship 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
- Inception V3
- Xception
- Resnet-50
- 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