Artificial Intelligence Course and Training (Using Tensorflow)

The main goal of this course is to familiarize you with all aspects of AI so that you can start your career as an artificial intelligence engineer. A few of the many topics/modules that you will learn in the program are:

• Basics of Deep Learning techniques
• Understanding artificial neural networks
• Training a neural network using the training data
• Convolutional neural networks and its applications
• TensorFlow and Tensor processing units
• Supervised and unsupervised learning methods
• Machine Learning using Python
• Applications of Deep Learning in image recognition, NLP, etc.
• Real-world projects in recommender systems, etc.

• Professionals working in the domains of analytics, Data Science, e-commerce, search engine, etc.
• Software professionals and new graduates seeking a career change.

Anyone can take this online course and be a successful machine learning engineer or AI engineer regardless of their previous knowledge.

Today, Artificial Intelligence has conquered almost every industry. Within a year or two, nearly 80% of emerging technologies will be based on AI. Machine Learning, especially Deep Learning, which is the most important aspect of Artificial intelligence, is used from AI-powered recommender systems (Chatbots) and Search engines for online movie recommendations. Therefore, to remain relevant and gain expertise in this emerging technology, enroll in Intellipaat’s AI Course.

This will help you build a solid AI career and get the best artificial intelligence engineer positions in leading organizations.

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.

Highlights:

• Constructing a convolutional neural network using TensorFlow
• Convolutional, dense, and pooling layers of CNNs
• Filtering images based on user queries

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.

Highlights:

• Breaking user queries into components
• Building neural networks with TensorFlow
• Understanding Natural Language Processing

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.

Highlights:

• Building neural networks with TensorFlow
• Looking at huge amounts of data and gaining insights
• Building a recommendation engine with TensorFlow Graph