Best Artificial Intelligence (AI) Training Nagpur

Artificial Intelligence is one of the fastest-growing and most energizing fields in technology today Information on Machine Learning and  Deep Learning on highly esteemed by organizations that are making bleeding edge technology and experts with these aptitudes can anticipate that their vocation will soar in the coming years.

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Artificial Intelligence Training course overview

This Training and internship explain how to use Artificial Intelligence, Machine Learning and Deep learning in Development to create wise and intelligence applications. After completing our Artificial Intelligence, Machine Learning and Deep learning in training and internship you will be able to create Dynamic web applications.

Artificial Intelligence is one of the fastest-growing and most energizing fields in technology today Information on Machine Learning and Deep Learning are highly esteemed by organizations that are making bleeding edge technology and experts with these aptitudes can anticipate that their vocation will soar in the coming years. Appzmine offers Training and accreditation courses in TensorFlow and Mahout to enable you to exploit the vocation openings in Artificial Intelligence.

Appzmine Deep Learning in TensorFlow with Python Certification Training is designed by industry experts according to the business need and demand. You will ace ideas, for example, YOLO Algorithm, Autoencoder Neural Networks, SoftMax work, Restricted Boltzmann Machine (RBM) and work with libraries like Keras and TFLearn

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Prerequisites for AI Machine Learning and Deep Learning Course

  • Good knowledge of Python Programming Language is mandatory 
  • Basic Knowledge of Statistics, Probability, Calculus, Linear Algebra
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Training from professional Artificial Intelligence Engineer

Appzmine Artificial Intelligence Expert has been implementing professional intelligence application for many years. Those consultants write and teach our Artificial Intelligence training courses, so their experience directly informs course content

Artificial Intelligence Course Content

INTRODUCTION TO MACHINE LEARNING
  • What is Machine learning?
  • Overview of sci-kit learn and tensorflow
  • Types of ML
  • Some complementing fields of ML
  • ML algorithms
  • Machine learning examples
  • What is ML..?
  • Types of ML
  • Decision trees
  • Linear regression
  • Logistic regression
  • Naive Bayes
  • k-Nearest Neighbors

 

Restricted Boltzmann Machine (RBM) and Autoencoders
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • SUPPORT VECTOR MACHINES(SVM)

Understanding Neural Networks with TensorFlow
  • How Deep Learning Works?
  • Activation Functions
  • Illustrate Perceptron
  • Training a Perceptron
  • Important Parameters of Perceptron
  • What is TensorFlow?
  • TensorFlow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step – Use-Case Implementation
CONVOLUTIONAL NEURAL NETWORKS
  • What is computer vision?
  • Why Convolutions (CNN)?
  • Introduction to CNN
  • Train a simple convolutional neural net
  • Explore the design space for convolutional nets
  • Pooling layer motivation in CNN
  • Design a convolutional layer application
  • Understanding and visualizing a CNN
  • Transfer learning and fine-tuning CNN
DETECTION & RECOGNITION
  • Padding & Edge Detection
  • Strided Convolutions
  • Networks in Networks and 1×1 Convolutions
  • Inception Network Motivation
  • Data Augmentation
  • Object Localization
  • Landmark Detection
  • Object Detection
  • Bounding Box Predictions
  • YOLO Algorithm
  • What is face recognition?
  • One Shot Learning
  • Siamese Network
  • Triplet Loss
  • Face Verification and Binary Classification
  • What is neural style transfer?
  • What are deep ConvNets learning?
Deep Networks Expert
  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation on SONAR dataset
  • Understand How Deep Network Works?
  • How Backpropagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent
  • Types of Deep Networks
Deep dive into Neural Networks with TensorFlow
  • Understand the limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow

TensorBoard

 

RECURRENT NEURAL NETWORKS
  • Why use sequence models?
  • Recurrent Neural Network Model
  • Notation
  • Back-propagation through time (BTT)
  • Different types of RNNs
  • Language model and sequence generation
  • Sampling novel sequences
  • Vanishing gradients with RNNs
  • Gated Recurrent Unit (GRU)
  • Long Short-Term Memory (LSTM)
  • Bidirectional RNN
  • Deep RNNs
MODELS OF RECURRENT NEURAL NETWORKS
  • Sequence models & Attention mechanism
  • Basic Models
  • Picking the most likely sentence
  • Beam Search
  • Refinements to Beam Search
  • Error analysis in beam search
  • Attention Model Intuition
  • Attention Model
  • Speech recognition
  • Trigger Word Detection

 

Keras API
  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras
TFLearn API
  • Define TFLearn
  • Composing Models in TFLearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFLearn
  • Customizing the Training Process
  • Using TensorBoard with TFLearn
  • Use-Case Implementation with TFLearn
Introduction to Text Mining and NLP
  • Overview of Text Mining
  • The need for Text Mining
  • Natural Language Processing (NLP) in Text Mining
  • Applications of Text Mining
  • OS Module
  • Reading, Writing to text and word files
  • Setting the NLTK Environment
  • Accessing the NLTK Corpora
  • Install NLTK Packages using NLTK Downloader
  • Accessing your operating system using the OS Module in Python
  • Reading & Writing .txt Files from/to your Local
  • Reading & Writing .docx Files from/to your Local
  • Working with the NLTK Corpora
Extracting, Cleaning and Pre-processing Text
  • Tokenization
  • Frequency Distribution
  • Different Types of Tokenizers
  • Bigrams, Trigrams & Ngrams
  • Stemming
  • Lemmatization
  • Stopwords
  • POS Tagging
  • Named Entity Recognition
  • Tokenization: Regex, Word, Blank line, Sentence Tokenizers
  • Bigrams, Trigrams & Ngrams
  • Stopword Removal
  • POS Tagging
  • Named Entity Recognition (NER)
  • Syntax Trees
  • Chunking
  • Chinking
  • Context Free Grammars (CFG)
  • Automating Text Paraphrasing
  • Parsing Syntax Trees
  • Chunking
  • Chinking
  • Automate Text Paraphrasing using CFG’s

 

CLASSIFIATION – SENTIMENT ANALYSIS
  • Machine Learning: Brush Up
  • Bag of Words
  • Count Vectorizer
  • Term Frequency (TF)
  • Inverse Document Frequency (IDF)
  • Demonstrate Bag of Words Approach
  • Working with CountVectorizer()
  • Using TF & IDF
  • Converting text to features and labels
  • Multinomial Naiive Bayes Classifier
  • Leveraging Confusion Matrix

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  • Training from professional Artificial Intelligence Expert
  • 10 years of experience
  • Training and Internship combined
  • Real-Time Development experience
  • Fully Equipped Lab, With AC & WIFI Internet available
  • Support and Careers Advice
  • We Offer Quality Training
  • and so much more…

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