This course does not require any programming or computer science base and is designed to introduce the basics of AI to anyone whether you have a technical background or not.In this you will explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.
This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not. You will also demonstrate AI in action with a mini project.Become an expert in the exciting new world of AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry grade projects.
This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer. You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
In this project-based course, you will be able to: Import Key libraries, dataset and visualize images. Perform data augmentation to increase the size of the dataset, improve model generalization. Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend. Compile and fit Deep Learning model to training data. Assess the performance of trained CNN and ensure its generalization using various KPIs. Improve network performance using regularization techniques like dropout.