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Artificial Intelligence and Deep Learning Training

Artificial Intelligence and Deep Learning Training

About Artificial Intelligence and Deep Learning Training

AI and Deep Learning Training Program are guided under expert trainers to provide a solid understanding of the key concepts of Artificial Intelligence, Data Science, Machine Learning and Deep Learning. In this training course, you'll gain knowledge of every aspect of artificial neural networks, Python for scripting machine learning, logistic regression, TensorFlow, deep learning libraries.

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About Course

What is AI & Deep Learning?

Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing data and making patterns for use in decision making. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that's unstructured or unlabelled. Also called Deep Neural Learning or Deep Neural Network.

What does an AI professional do? 

A role for an AI professional is to program computers to test hypotheses in relation to how the human mind works, through cognitive simulation. Besides, the role of an artificial Intelligence specialist is to enhance the offerings and operations within a number of industries. Their purpose is to program computer smart systems, which cover a variety of aspects, ranging from recognizing voices to solving complex issues.

Why learn Artificial Intelligence and Deep Learning Training?

Artificial Intelligence nowadays is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most vital aspects of AI that are being deployed everywhere from search engines to online movie recommendations. Taking this Deep Learning training will help professionals to make a solid career in a rising technology domain and find the best jobs in top organizations.

Artificial Intelligence with Deep Learning provides innovative solutions and is the most trending and futuristic career option. 

AI certified professionals are one of the top salary holders in the job industry. According to, Machine Learning Engineer with Deep Learning Skills holds an approximate average pay scale for about $117,073 per year.

Thus Deep Learning Professional with expertise in AI can demand good high salaries and also having job options in big companies. Because AI and Deep Learning in majorly used by many large companies.

Get trained with our AI and deep learning course and Fast-track your career to take on more lucrative job roles and take your career to the next level.

What you’ll learn in AI and Deep Learning Training?

  • Fundamentals of Deep Learning techniques
  • Understanding an Artificial Neural Network
  • How to train a neural network with training data
  • Convolutional neural networks and its applications
  • TensorFlow and Tensor processing Unit
  • Supervised and unsupervised learning methodology
  • Machine learning with Python language
  • Application of dl in image recognition, NLP and more
  • Real world projects in recommender systems and others

Who should take this course?

  •  Professionals in analytics, data science domains, eCommerce, search engine domains
  • Software professionals who are looking for a career switch
  • Analytics professionals
  • Data Science professionals
  • Graduates looking to start a career in Machine Learning and Artificial Intelligence


Anybody can take this Training Course despite their prior skills.

Programming skills with basic knowledge in python or R and applied statistics will be plus.

AI & Deep Learning Course Highlights

  • Introduction to Artificial Intelligence, Deep learning, and Machine Learning
  • Supervised and unsupervised learning concepts and the Statistical Modelling Paradigm
  • Solving business problems using Artificial Intelligence and Machine Learning
  • In this training program, you'll gain the knowledge of the various aspects of artificial neural networks – DNN and CNN , graph visualization, vectorization, Python for scripting machine learning applications, logistic regression with neural network, TensorFlow, binary classification, deep learning libraries, Keras & TFLearn API, GPU in deep learning, backpropagation, and hyperparameters through hands-on projects.
  • Comprehending theoretical concepts and how they relate to the practical aspects of machine learning and AI
  • Applying concepts such as regression, clustering, retrieval, classification, dimensional reduction, and engine recommendation
  • Basic techniques and ideas to design intelligent computer systems
  • Working on real-life industry-based projects
  • Guidance from experts on advanced level application/s building
  • The opportunity of personal assistance to every participant enrolled
  • Mock interview sessions, resume services and Interview questions to prepare you to attend interviews with confidence.

How EnhanceLearn Training can help you

  • 48 Hours of hands-on session per batch and once enrolled you can take any number of batches for 90 days
  • 24x7 Expert Support and GTA (Global Teaching Assistant, SME) support available even to schedule a one on one session for doubt clearing
  • Project Based learning approach with evaluation after each module
  • Project Submission mandatory for Certification and thoroughly evaluated
  • 3 Months Experience Certificate on successful project completion

For becoming an AI & Deep Learning expert, choose our best Training and Placement Program. If you are interested in joining the EnhanceLearn team, please email at

Course Curriculum

Module 1: Introduction to Deep Learning & Neural Networks

  • Machine learning and its implications to the artificial intelligence sector
  • Advantages of machine learning over other conventional methodologies
  • Introduction to Deep Learning within machine learning
  • Training the system with training data
  • Supervised learning
    • Classification
    • Regression
  • Unsupervised learning
    • Clustering
    • Association
  • Introduction to AI
  • Introduction to Neural Networks
  • Supervised Learning with Neural Networks
  • Concept of Machine Learning
  • Probability distributions
  • Hypothesis testing
  • Hidden Markov Model
  • Introduction to Multi-Layer Network
  • Concept of Deep neural networks, Regularization
  • Multi-layer perceptron, capacity, and overfitting
  • Neural network hyperparameters, logic gates
  • the various activation functions in neural networks like Sigmoid
  • Hyperbolic functions
  • Backpropagation, convergence
  • Forward propagation, overfitting, hyperparameters
  • The various techniques used in training of artificial neural networks
  • Gradient descent rule, perceptron learning rule, tuning learning rate
  • A stochastic process, optimization techniques
  • Regularization techniques, regression techniques
  • Lasso L1, Ridge L2, vanishing gradients, transfer learning
  • Xavier initialization, and vanishing gradients
  • How Deep Learning Works
  • Activation Functions
  • Illustrate Perceptron
  • Important Parameters of Perceptron
  • Multi-layer Perceptron
  • What is Tensorflow
  • Introduction to TensorFlow open source software library for designing
  • building and training Deep Learning models
  • Python Library behind TensorFlow
  • Tensor Processing Unit (TPU) programmable
  • AI accelerator by Google
  • Tensorflow code-basics
  • Graph Visualization, Constants, Placeholders, Variables
  • Step by Step – Use-Case Implementation, Keras
  • Keras high-level neural network for working on top of tensorflow
  • Defining complex multi-output models
  • Composing models using Keras
  • Batch normalization
  • Deploying Keras with tensorboard
  • Neural network training process customization
  • Implementing neural networks using TFLearn API
  • Defining and composing models using TFLearn
  • Deploying TensorBoard with TFLearn
  • Mapping the human mind with Deep Neural Networks
  • The various building blocks of Artificial Neural Networks
  • Architecture of DNN
  • Concept of reinforcement learning in DNN
  • Activation functions and optimization algorithms in DNN
  • What is a Convolutional Neural Network
  • Understanding the architecture of CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Use cases of CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks
  • Feature maps, Kernel filter, pooling
  • Deploying convolutinal neural network in TensorFlow
  • Introduction to RNN
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Recursive Neural Tensor Network Theory
  • Long Short-Term Memory (LSTM)
  • Recurrent Neural Network Model
  • Basic RNN cell, unfolded RNN, training of RNN, and dynamic RNN
  • Time-series predictions
  • Introduction to GPUs and how they differ from CPUs
  • the importance of GPUs in training Deep Learning Networks
  • the forward pass and backward pass training technique
  • the GPU constituent with simpler core and concurrent hardware
  • Introduction to RBM and autoencoders
  • Deploying it for deep neural networks
  • Collaborative filtering using RBM
  • Features of autoencoders
  • Applications of autoencoders
  • Automated conversation bots using one of the descriptive techniques
    • IBM Watson
    • Google API.AI
    • Microsoft’s Luis
    • Amazon Lex
    • Generative
    • Open-Close Domain Bots
    • Sequence to Sequence model (LSTM)
  • Working on Live Projects (as applicable)


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Artificial Intelligence and Deep Learning Training
Artificial Intelligence and Deep Learning Certificate
Job Overview
Key Features
Training FAQs
Are Artificial Intelligence and Machine learning both same?

Artificial Intelligence (AI) and Machine Learning (ML) are 2 very popular buzzwords right now, and sometimes seem to be used interchangeably. They’re not quite a similar thing, but the perception that they're can generally lead to some confusion. So I thought it might be worth writing a piece to explain the difference.

What language is used for Artificial Intelligence?
What do you mean by Machine Learning in Artificial Intelligence?
Where will AI be in five-to-ten years?
Does AI aim at human-level intelligence?
Is there any type of advanced AI that would be safer than others?
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Kabeer Kohli 
Great Job EnhanceLearn!!

The Artificial intelligence trainers chosen by EnhanceLearn made me grasp this in record time, clear the AI certification and land my dream job. Great job EnhanceLearn. Keep it up. Great way to learn Artificial intelligence!

Charlie Shaw 
AI training at its best!

My AI course just finished, and I must say I learnt a lot from my trainer, he is a very good teacher knows the topics well and give really good explanations and examples. It’s a lot of information to take in so it is important to go over the material and videos which can add up with time, but overall I think this course was worth it.

Pradeep Amble 
Excellent AI training!

I came to know about EnhanceLearn through my friends, I must say it’s a good institution with expert mentors and good course content. I also like to mention that their assignments have helped me lot in learning AI topics. All in all I am satisfied with the course.

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