AI & Deep Learning Course Training in USA
- 24 Hours Classroom & Online Sessions
- 60+ Hours Assignments & eLearning
- 100% Job Assistance
- 2 Capstone Projects
- Industry Placement Training
Academic Partners & International Accreditations
"The size of the North American market for Artificial Intelligence will be $29,000 million by 2025." - (Source). Huge investments and interest in Artificial Intelligence are expected to increase in the long run in the near future. As per McKinsey predictions, about 15% of vehicles will be fully automated in 2030. The number of startups based on AI in the US has increased by 114% between 2015 to 2018. By the latest advancements in Deep Learning, AI is being extensively used for search engines, virtual assistants, online translators, and many sales and marketing decisions. Tesla and Audi manufactured semi-autonomous cars and are working to improve to reach into full automated cars. AI is rapidly being adopted by many sectors and there is a demand for AI professionals.
AI & Deep Learning
- Computer Skills
- Basic Mathematical Knowledge
- Basic Data Science Concepts
Artificial Intelligence Training Overview
The objective of this offering is to provide not only a conceptual understanding of the deep learning skills but also the practical applications in marketplace scenarios prevailing in the USA. This course serves as a perfect launchpad for professionals with an appreciation of statistics and knowledge of programming languages such as Python, R and RStudio into a career of AI and Deep Learning. Students will learn how to build AI applications, understand the ever-evolving neural network architectures, create AI algorithms, and minimize errors through advanced optimization techniques. By successfully graduating from this course, they will be able ready for careers in computer vision related image processing domains.
Artificial Intelligence Course Outcomes
The field of Artificial Intelligence is morphing into an unstoppable force that the US-based companies are looking to capitalize on. This provides a tremendous opportunity for professionals to get into this market and command handsome salaries. The demand for AI professionals has grown by 29% from 2018 to 2019 and the average salary could be well over $100,000. This alone should be a prime motivating factor for professionals on the edge about whether to begin their careers in this hot domain. 360DigiTMG’s much researched and backed by industry experts, Artificial Intelligence training for students in the USA ensures that participants become seasoned practitioners in dealing with both structured and unstructured data. From this training, the students will learn concepts of Deep learning algorithms and Natural language processing.
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2 Live Projects
Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
AI & Deep Learning Training Modules
360DigiTMG has instructors that are industry-leading experts in Machine Learning, Data Science, and related technologies. Artificial Intelligence and Deep Learning training course in the USA is delivered by 360DigiTMG. The main objective of this training is to provide a workforce and bridge the gap between business needs and talent. The module of the course is designed comprehensively by industry stalwarts. The module introduces Artificial Intelligence and Deep learning in neural networks. Students will learn Python libraries that include Keras, Tensor Flow, and OpenCV. The modules also cover different algorithms like perceptron and backpropagation. Learn Images processing and Computer vision data which is quite different from the regular tabular data and needs to be handled in a particular manner. Students also learn LSTMs that are particular types of RNNs. They have something called a ‘memory’ cell which ‘remembers’ information as it flows through the network. Hence this makes LSTMs best for forecasting type problems or any use case that has a temporal component. The modules also explain deeply about Reinforcement and Q-learning. This is different from supervised and unsupervised learning, which uses a concept called reinforcement. It builds upon the Markov Decision Process and builds an architecture that differs from both supervised and unsupervised approaches. Students will have the opportunity to build a chatbot from scratch. We start from a simple rules-based one to a more complex using NLP techniques. 360DigiTMG ensures that the students should gain a thorough knowledge of concepts and applications of statistical tools through hands-on experience. It also helps students to develop relevant skills that are required to grab lucrative jobs in giant companies.
This module serves as an introduction to Artificial Intelligence and specifically Deep learning for neural networks. How has AI evolved over the years and what is driving the current surge in deep learning revolution and discuss the state of the art.
This module introduces deep learning frameworks and libraries like TensorFlow, TensorFlow 2.0, OpenCV and Keras. Some of the salient features of each of the libraries are introduced.
This module introduces the perceptron and backpropagation methods which form the backbone of neural networks.
This module extends the concepts introduced in the previous chapters to multiple hidden layers.
Convolutional neural networks are a vast improvement on the MLP algorithm which has greatly increased the efficiency of ANNs. We discuss the fundamental concepts and basic building blocks of CNNs.
Images data is quite different from the regular tabular data and needs to be handled in a particular manner. Since machine learning algorithms rely on mathematical computations, images need to be processed and converted into numerical (preferably vectorised) representations. This module addresses how that is accomplished.
RNNs are a particular type of deep learning architecture that has had a very high success rate in image and object detection problems. This module introduces the architecture of RNNs and the different variations.
LSTMs are particular types of RNNs. They have something called a ‘memory’ cell which ‘remembers’ information as it flows through the network. Hence this makes LSTMs best for forecasting type problems or any use case that has a temporal component.
This architecture is another specialized type of LSTMs. This module goes into the most efficient implementation of a GRU.
Auto encoders are a type of unsupervised networks. RBMs are no longer used, but are important for historical purposes.
This type of architecture belongs to the generative class of neural networks. The differentiating feature of this architecture is that there is no connection between the cells/neurons in a single layer.
Another type of generative architecture, this class of networks have two competing networks. One network tries to create a clone of the original input while the other tries to spot the fake. After enough training the networks produce near identical clones of the original input (typically images)
This is different from Supervised and unsupervised learning, which uses a concept called reinforcement. It builds upon the Markov Decision Process and builds an architecture that differs from both supervised and unsupervised approaches.
This is the part where you use all your knowledge to build a chatbot from scratch. We start from a simple rules based one, to a more complex one using NLP techniques.
Artificial Intelligence Trends in USA
Artificial Intelligence is growing exponentially and its applications are used in every area of life. The AI is effecting and is going to significantly change and improve the process of work. Let’s peek into the latest trends of AI and Deep Learning. AI is playing a pivotal role in the Retail business. Amazon has efficiently started implementing AI technology in its physical stores. It has incorporated technology that is used in Self-driving cars including computer monitoring and deep learning processes in its business. The AI inbuilt tools help in detecting the products and getting them charged in an individual’s amazon account making the shopping experience worthwhile and easy for customers. Walmart has introduced using thousands of robots in its store replacing the human workforce. Apart from biometrics, facial recognition tools are driving a huge momentum in the market and going to be implemented very soon in many stores. By analyzing the facial expression while observing the various products, the personalized promotion of the products will be done by sensors observations.
As per the Mckinsey global institute predictions, by 2055 the robots will perform half of our work tasks which are repetitive and routine, making humans concentrate on creative works. For eg: JPMorgan Chase & Co. introduced COIN which means Contract Intelligence, based on AI. This COIN helps to analyze the commercial loan agreements in a fraction of seconds which is usually done by a team of lawyers for hours. This technology has reduced time, effort, and cost. AI and deep learning technologies are being used in Healthcare for diagnosis and treatment. IBM introduced an AI assistant named Watson, which is being used by many hospitals across the world in detecting cancer. AI tools are going to improve healthcare significantly in the coming years. AI chatbots became popular in a short time and are being used by many companies for an effective conversation with their customers leading to an increase in sales and production. AI and deep learning technologies are going to impact the world by reducing human errors and the workforce.
How We Prepare You
- Additional Assignments of over 60 hours
- Live Free Webinars
- Resume and LinkedIn Review Sessions
- 3 Month Access to LMS
- 24/7 Support
- Job Assistance in Artificial Intelligence Fields
- Complimentary Courses
- Unlimited Mock Interview and Quiz Session
- Hands-on Experience in a Live Project
- Life Time Free Access to Industry Webinars
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