Advanced Computer Vision- 50 Hours

20%

Course Description

Data Science, Machine Learning, Deep Learning and Artificial Intelligence

Objectives of the Program

•  To enable participants understand how Data Science is used in every aspect of our daily lives and businesses
•  To enable the participants to learn basics of Python.
•  To enable the participants to learn Data Science/Machine learning/Deep Learning using Python.
•  To empower the participants with sufficient knowledge to use latest technologies of Data Science (Hands-On).
•  To make the participants understand overview of how Exploratory Data Analysis works.
•  To provide the participants Hands-on experience in Data science concepts.
•  To provide the participants Hands-on experience in Machine Learning concepts.
•  To empower the participants with sufficient knowledge to use latest technologies of Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
•  Overview of the Program Content
•  Introduction to Data Science, Machine Learning, Deep Learning and Artificial
•  Intelligence
•  Basics of Python and Hands-on on Python
•  Introduction to Data Science using Python
•  Hands on session on Data Science with Python [Database Connectivity with PYTHON: Performing Database Transactions (Inserting, Deleting, and Updating The   Database)]
•  Introduction to Machine Learning and its types
•  Examples Overview of Machine Learning packages in Python
•  Introduction to Deep Learning and its types
•  Project in Data Science/Machine Learning /Artificial Intelligence

Module 1: Introduction to Neural network for Deep Learning

•  Introduction to Tensorflow/Keras/Pytorch
•  Biological Neuron/ Artificial Neuron (Perceptron)
•  Perceptron/MLP/ Vanilla Neural Network/ANN
•  Types of ANN: Sequential ANN and Functional ANN
•  Weight Initialization and Biases
•  Activation Functions
•  Backward Propogation
•  Gradient Descent
•  Learning rate
•  Optimizers
•  Regularization
•  Loss Function and Cost Function
•  Hyperparameters
   

Module 2: Computer vision

•  Introduction to computer Vision and Image Processing
•  Introduction to CNN
• Introduction to CNN Architectures and Transfer Learning
• Image Classification using CNN and Transfer Learnin

Module 3: Advanced Computer Vision

• Object Detection
•  Image Segmentation
•  Semantic Segmentation

Module 4: Unsupervised Deep Learning

o Introduction to GAN and its types
o Introduction to Autoencoder

Tools, Languages and Libraries Used:

• 1. Sql
• 2. Python
• 3. Anaconda
• 4. Jupyter Notebook
• 5. Google Colab
• 6. Tableau
• 7. Numpy
• 8. Pandas
• 9. Matplotlib
• 10. Seaborn
• 11. Sklearn
• 12. Scipy
• 13. Tensorflow
• 14. Keras

Mr. Arpit Yadav
Senior Data Scientist in AIML

Subject Matter Expert