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Anybody can take this Artificial Intelligence Course in New York regardless of their prior skills.
New York is at the center of US business, commerce, technology and finance. As such the use of Artificial Intelligence in the New York is extensive creating huge job opportunities for those who are trained and certified in Artificial Intelligence and Deep Learning.
The Artificial Intelligence market is growing rapidly due to the humongous nature of the New York metropolitan area and its clout in the USA economy. If you learn about Artificial Intelligence along with specialization in Deep Learning then you can make the best use of this boom Artificial Intelligence market in New York.
Artificial Intelligence is the next frontier. Within the next decade most of the industry verticals will be fully invested in this revolutionary technology. Getting into this technology right now by learning about Artificial Intelligence course in New York from Intellipaat can definitely take your career to the next level.
Field of Machine Learning, its impact on the field of Artificial Intelligence, the benefits of Machine Learning w.r.t. traditional methodologies, Deep Learning introduction and how it is different from all other Machine Learning methods, supervised and unsupervised learning, system training with the training data, classification and regression in supervised learning, clustering and association in unsupervised learning, algorithms that are used in these categories, introduction to AI and neural networks, Machine Learning concepts, supervised learning with neural networks, fundamentals of statistics, hypothesis testing, probability distributions, and hidden Markov models.
Multi-layer network introduction, regularization, deep neural networks, multi-layer perceptron, overfitting, and capacity, neural network hyperparameters, logic gates, different activation functions used in neural networks, including ReLu, Softmax, Sigmoid, and hyperbolic functions, back propagation, forward propagation, convergence, hyperparameters, and overfitting.
Various methods that are used to train artificial neural networks, perceptron learning rule, gradient descent rule, tuning the learning rate, regularization techniques, optimization techniques, stochastic process, vanishing gradients, transfer learning, regression techniques, including Lasso L1 and Ridge L2, unsupervised pre-training, Xavier initialization, and more.
Understanding how Deep Learning works, its activation functions, illustrating perceptron, perceptron training, multi-layer perceptron, key parameters of perceptron; TensorFlow introduction and its open-source software library that is used to design, create, and train Deep Learning models followed by Google’s Tensor Processing Unit (TPU) Programmable AI, Python libraries in TensorFlow, code basics, variables, constants, placeholders, graph visualization, use-case implementation, Keras, and more.
Keras high-level neural network for working on top of TensorFlow, defining complex multi-output models, composing models using Keras, sequential and functional composition, batch normalization, deploying Keras with TensorBoard, and neural network training process customization.
Using TFLearn API to implement neural networks, defining and composing models, and deploying TensorBoard.
Mapping the human mind with deep neural networks (DNNs), several building blocks of artificial neural networks (ANNs), the architecture of DNN and its building blocks, reinforcement learning in DNN concepts, various parameters, layers, and optimization algorithms in DNN, and activation functions.
‘What is a convolutional neural network?’ understanding the architecture and use-cases of CNN, ‘what is a pooling layer?’ how to visualize using CNN, how to fine-tune a convolutional neural network, ‘what is transfer learning?’ understanding recurrent neural networks, Kernel filter, feature maps, and pooling, and deploying convolutional neural networks in TensorFlow.
Introduction to the RNN model, use cases of RNN, modeling sequences, training RNNs with back propagation, long short-term memory (LSTM), Recursive Neural Tensor Network theory, the basic RNN cell, unfolded RNN, RNN training, dynamic RNN, and time-series predictions.
GPUs’ introduction, ‘how are they different from CPUs?,’ the significance of GPUs in training Deep Learning networks, forward pass and backward pass training techniques and GPU constituent with simpler core and concurrent hardware.
RBM and autoencoders’ introduction, deploying RBM for deep neural networks, using RBM for collaborative filtering, autoencoders’ features, and the applications of autoencoders.
Image processing, Natural Language Processing (NLP), speech recognition, and video analytics.
Automated conversation bots leveraging any of the following descriptive techniques: IBM Watson, Microsoft’s Luis, Google API.AI, Amazon Lex, Open–Closed domain bots, Generative model, and the sequence to sequence model (LSTM).
What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in Python, sentiment analysis in Python (Twitter sentiment analysis), text analysis.
Hands-on Exercise – Analyzing time-series data, sequence of measurements that follow a non-random order to identify the nature of the phenomenon and to forecast the future values in the series.
Project 1: Image Recognition with TensorFlow
Industry: Internet Search
Problem Statement: Creating a Deep Learning model to identify the right object on the Internet as per the user search for the corresponding image
Description: In this project, you will learn how to build a convolutional neural network using Google TensorFlow. You will do the visualization of images using training, providing input images, losses, and distributions of activations and gradients. You will learn to break each image into manageable tiles and input them to the convolutional neural network for the desired result.
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Project 2: Building an AI-based Chatbot using IBM watson LAB
Industry: Ecommerce
Problem Statement: Building a chatbot using Artificial Intelligence
Description: In this project, by understanding the customer needs, you will be able to offer the right services through Artificial Intelligence chatbots. You will learn how to create the right artificial neural network with the right amount of layers to ensure that the customer queries are comprehensible to the Artificial Intelligence chatbot. This will help you understand Natural Language Processing, going beyond keywords, data parsing, and providing the right solutions.
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Project 3: Ecommerce Product Recommendation
Industry: Ecommerce
Problem Statement: Recommending the right products to customers using Artificial Intelligence with TensorFlow
Description: This project involves working with recommender systems to provide the right product recommendation to customers with TensorFlow. You will learn how to use Artificial Intelligence to check for users’ past buying habits, find out the products that go hand-in-hand, and recommend the best products that can be bought together with a particular product.
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Artificial Intelligence Tutorial
The entire content of this AI course is developed by leading AI professionals to help you find the best artificial intelligence engineering job at the top MNCs. During the certification training, you will work on real-world projects that will help evaluate your skills and learning in real-time business scenarios, thus helping you accelerate your career effortlessly.
Upon the completion of this artificial intelligence online course, there will be quizzes that reflect the type of questions asked in the certification examination and will help you score better.
Intellipaat Course Completion Certification will be awarded on the completion of the project work (after the expert review) and upon scoring at least 60 percent marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 corporations.
Our Artificial Intelligence online training involves the simultaneous participation of both learners and instructors in an online environment. Being a learner, you can log in to our applied AI course sessions from anywhere and attend the class without having to be present physically. Also, we record the proceedings of all AI classes and equip you with them to further enhance your learning process. On the completion of this AI training online, your experience will be equivalent to that of a professional who has worked for 6 months in the industry.
Good learning material
Intellipaat offered fully updated learning material for the best Artificial Intelligence training. The video quality and other course materials were appreciable. I would definitely recommend Intellipaat’s AI training to my friends.
Knowledgeable trainers
Artificial Intelligence trainers of Intellipaat simplified the topics of Artificial Intelligence, Machine Learning, and TensorFlow to help even an average learner grasp these toughest concepts. Thank you, Intellipaat!
Awesome AI Projects
Intellipaat’s Artificial Intelligence training had great projects in building chatbots and other cool real-world applications. The entire certification training course was up to the mark. I found it one of the best AI courses online.
Well-organized classes and highly intellectual instructors.
The course material was very well organized. The trainer explained the basics of each module to me. All my queries were addressed very clearly. The trainer also made me realize how important this course is for beginners in IT stream. I suggest this is the best training course.