Course Summary
Artificial Intelligence(AI) entered the mainstream and revolutionized the way we do the business. It won't surprise you to know that more than 50% of current roles humans perform will be replaced by AI in the next 3 to 5 years. There is already a big demand for AI specialists and this demand will be exponentially growing in the coming years. Artificial Intelligence is revolutionizing the way business and gaining Deep Learning skills will make you a scare, highly in demand resource, and has the potential to supercharge your career growth. In simple words, this course will help you build a career in the most coveted domain, AI.
Deep Learning with Python courses will get you ready for an AI career. Deep Learning is also one of the highly coveted skills in the tech industry. This course covers popular Deep Learning algorithms: Convolutional Networks, BatchNorm, RNNS, etc., with the case studies from autonomous driving, healthcare, Natural language processing, etc., With this course, you will not only master the concept behind deep learning but also see the application of the real world.
Course Objectives
- Introduce Deep Learning AI algorithms
- Improving Deep Learning models with Optimization and Hyperparameter tuning
- Convolutional Neural Networks
- Structuring Deep Learning Projects
- Hands-on Case Study
Course Outline
Introduction to Deep Learning
- What is a neural network?
- Supervised Learning with Neural Networks - Python
- How Deep Learning is different from Machine Learning
Overview of Machine Learning Concepts
- What is Machine Learning?
- Supervised Machine Learning algorithms
- K-Nearest Neighbors (KNN) concept and application
- Naive Bayes concept and application
- Logistic Regression concept and application
- Classification Trees concept and application
- Unsupervised Machine Learning algorithms
- Clustering with K-means concept and application
- Hierarchical Clustering concept and application
Deep Learning Models
- Restricted Boltzmann Machines
- Deep Belief Nets
- Convolutional Networks
- Recurrent Nets
- Autoencoders
Building Deep Learning Neural Networks
- Deep L-layer neural network
- Forward Propagation in a Deep Network
- Building blocks of deep neural networks
- Parameters vs Hyperparameters
Hands on Case Study
- Deep Learning Use Cases
- Deep Neural Network – Application
Who can benefit from this course?
Professionals with essential knowledge in Machine Learning and aspiring to be AI specialists can opt this course.
Prerequisites
- Machine Learning essential knowledge is mandatory
- Basic Python programming skills is required
Course Outline
Introduction to Deep Learning
- What is a neural network?
- Supervised Learning with Neural Networks - Python
- How Deep Learning is different from Machine Learning
Overview of Machine Learning Concepts
- What is Machine Learning?
- Supervised Machine Learning algorithms
- K-Nearest Neighbors (KNN) concept and application
- Naive Bayes concept and application
- Logistic Regression concept and application
- Classification Trees concept and application
- Unsupervised Machine Learning algorithms
- Clustering with K-means concept and application
- Hierarchical Clustering concept and application
Deep Learning Models
- Restricted Boltzmann Machines
- Deep Belief Nets
- Convolutional Networks
- Recurrent Nets
- Autoencoders
Building Deep Learning Neural Networks
- Deep L-layer neural network
- Forward Propagation in a Deep Network
- Building blocks of deep neural networks
- Parameters vs Hyperparameters
Hands on Case Study
- Deep Learning Use Cases
- Deep Neural Network – Application
Who can benefit from this course?
Professionals with essential knowledge in Machine Learning and aspiring to be AI specialists can opt this course.
Prerequisites
- Machine Learning essential knowledge is mandatory
- Basic Python programming skills is required
