Best Artificial Intelligence Training Institute in noida, Delhi

       Artificial Intelligence Training Institute in noida, Delhi

Inovi technologies is the Best Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow , Machine Learning , and other AI concepts, plus the programming languages needed to design intelligent agents , deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems.Artificial Intelligence Technical expert and a passionate trainer has expertise in the field of AI and Machine Learning , Deep Learning and Project Management , he has a proven work record of delivering Technical Training in various technologies and domains at the premier organizations.

AI (artificial intelligence) is the simulation of human intelligence processes by machines , especially computer systems. These processes include learning (the acquisition of information and rules for using the information) , reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems , speech recognition and machine vision.

About Artificial Intelligence Training

Artificial Intelligence (AI) has a long history but is still properly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and Tensorflow with Python. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. This Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.

Introduction to Data Science Deep Learning & Artificial Intelligence

Introduction to Deep Learning & AI

Deep Learning: A revolution in Artificial Intelligence


Limitations of Machine Learning

What is Deep Learning?


Need for Data Scientists


Foundation of Data Science


What is Business Intelligence


What is Data Analysis


What is Data Mining

What is Machine Learning?

Analytics vs Data Science


Value Chain


Types of Analytics


Lifecycle Probability


Analytics Project Lifecycle


Advantage of Deep Learning over Machine learning


Reasons for Deep Learning


Real-Life use cases of Deep Learning


Review of Machine Learning

Data


Basis of Data Categorization


Types of Data


Data Collection Types


Forms of Data & Sources


Data Quality & Changes


Data Quality Issues


Data Quality Story


What is Data Architecture


Components of Data Architecture


OLTP vs OLAP


How is Data Stored?

Big Data


What is Big Data?


5 Vs of Big Data


Big Data Architecture


Big Data Technologies


Big Data Challenge


Big Data Requirements


Big Data Distributed Computing & Complexity


Hadoop


Mapreduce Framework


Hadoop Ecosystem

Data Science Deep Dive


What Data Science is


Why Data Scientists are in demand


What is a Data Product


The growing need for Data Science


Large Scale Analysis Cost vs Storage


Data Science Skills


Data Science Use Cases


Data Science Project Life Cycle & Stages


Data Acuqisition


Where to source data


Techniques


Evaluating input data


Data formats


Data Quantity


Data Quality


Resolution Techniques


Data Transformation


File format Conversions


Anonymization
Python


Python Overview


About Interpreted Languages


Advantages/Disadvantages of Python pydoc.


Starting Python


Interpreter PATH


Using the Interpreter


Running a Python Script


Using Variables


Keywords


Built-in Functions


StringsDifferent Literals


Math Operators and Expressions


Writing to the Screen


String Formatting


Command Line Parameters and Flow Control.


Lists


Tuples


Indexing and Slicing


Iterating through a Sequence


Functions for all Sequences

Operators and Keywords for Sequences


The xrange() function


List Comprehensions


Generator Expressions


Dictionaries and Sets.

Numpy & Pandas


Learning NumPy


Introduction to Pandas


Creating Data Frames


GroupingSorting


Plotting Data


Creating Functions


Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops


Functions


Function Parameters


Global Variables


Variable Scope and Returning Values. Sorting


Alternate Keys


Lambda Functions


Sorting Collections of Collections


Classes & OOPs
Statistics


What is Statistics


Descriptive Statistics


Central Tendency Measures


The Story of Average


Dispersion Measures


Data Distributions


Central Limit Theorem


What is Sampling


Why Sampling


Sampling Methods


Inferential Statistics


What is Hypothesis testing


Confidence Level


Degrees of freedom


what is pValue


Chi-Square test


What is ANOVA


Correlation vs Regression


Uses of Correlation & Regression
Machine Learning, Deep Learning & AI using Python

Introduction


ML Fundamentals


ML Common Use Cases


Understanding Supervised and Unsupervised Learning Techniques

Clustering


Similarity Metrics


Distance Measure Types: Euclidean, Cosine Measures


Creating predictive models


Understanding K-Means Clustering


Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model


Case study

Implementing Association rule mining


What is Association Rules & its use cases?


What is Recommendation Engine & it’s working?


Recommendation Use-case


Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier


How to build Decision trees


What is Classification and its use cases?


What is Decision Tree?


Algorithm for Decision Tree Induction


Creating a Decision Tree


Confusion Matrix


Case study

Random Forest Classifier


What is Random Forests


Features of Random Forest


Out of Box Error Estimate and Variable Importance


Case study

Naive Bayes Classifier.


Case study

Project Discussion

Problem Statement and Analysis


Various approaches to solve a Data Science Problem


Pros and Cons of different approaches and algorithms.

Linear Regression


Case study


Introduction to Predictive Modeling


Linear Regression Overview


Simple Linear Regression


Multiple Linear Regression

Logistic Regression


Case study


Logistic Regression Overview


Data Partitioning


Univariate Analysis


Bivariate Analysis


Multicollinearity Analysis


Model Building


Model Validation


Model Performance Assessment AUC & ROC curves


Scorecard

Support Vector Machines


Case Study


Introduction to SVMs


SVM History


Vectors Overview


Decision Surfaces


Linear SVMS


The Kernel Trick


Non-Linear SVMs


The Kernel SVM

Time Series Analysis


Describe Time Series data


Format your Time Series data


List the different components of Time Series data


Discuss different kind of Time Series scenarios


Choose the model according to the Time series scenario


Implement the model for forecasting


Explain working and implementation of ARIMA model


Illustrate the working and implementation of different ETS models


Forecast the data using the respective model


What is Time Series data?


Time Series variables


Different components of Time Series data


Visualize the data to identify Time Series Components


Implement ARIMA model for forecasting


Exponential smoothing models


Identifying different time series scenario based on which different Exponential Smoothing model can be applied


Implement respective model for forecasting


Visualizing and formatting Time Series data


Plotting decomposed Time Series data plot


Applying ARIMA and ETS model for Time Series forecasting


Forecasting for given Time period


Case Study

Machine Learning Project

Machine learning algorithms Python


Various machine learning algorithms in Python


Apply machine learning algorithms in Python

Feature Selection and Pre-processing


How to select the right data


Which are the best features to use


Additional feature selection techniques


A feature selection case study


Preprocessing


Preprocessing Scaling Techniques


How to preprocess your data


How to scale your data


Feature Scaling Final Project

Which Algorithms perform best


Highly efficient machine learning algorithms


Bagging Decision Trees


The power of ensembles


Random Forest Ensemble technique


Boosting – Adaboost


Boosting ensemble stochastic gradient boosting


A final ensemble technique

Model selection cross validation score


Introduction Model Tuning


Parameter Tuning GridSearchCV


A second method to tune your algorithm


How to automate machine learning


Which ML algo should you choose


How to compare machine learning algorithms in practice

Text Mining& NLP


Sentimental Analysis


Case study

PySpark and MLLib


Introduction to Spark Core


Spark Architecture


Working with RDDs


Introduction to PySpark


Machine learning with PySpark – Mllib
Deep Learning & AI using Python

Deep Learning & AI


Case Study


Deep Learning Overview


The Brain vs Neuron


Introduction to Deep Learning

Introduction to Artificial Neural Networks


The Detailed ANN


The Activation Functions


How do ANNs work & learn


Gradient Descent


Stochastic Gradient Descent


Backpropogation


Understand 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


Building a multi-layered perceptron for classification


Why Deep Networks


Why Deep Networks give better accuracy?


Use-Case Implementation


Understand How Deep Network Works?


How Backpropagation Works?


Illustrate Forward pass, Backward pass


Different variants of Gradient Descent

Convolutional Neural Networks


Convolutional Operation


Relu Layers


What is Pooling vs Flattening


Full Connection


Softmax vs Cross Entropy


” Building a real world convolutional neural network


for image classification”

What are RNNs – Introduction to RNNs


Recurrent neural networks rnn


LSTMs understanding LSTMs


long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders


Restricted Boltzmann Machine


Applications of RBM


Introduction to Autoencoders


Autoencoders applications


Understanding Autoencoders


Building a Autoencoder model

Tensorflow with Python


Introducing Tensorflow


Introducing Tensorflow


Why Tensorflow?


What is tensorflow?


Tensorflow as an Interface


Tensorflow as an environment


Tensors


Computation Graph


Installing Tensorflow


Tensorflow training


Prepare Data


Tensor types


Loss and Optimization


Running tensorflow programs

Building Neural Networks using

Tensorflow


Tensors


Tensorflow data types


CPU vs GPU vs TPU


Tensorflow methods


Introduction to Neural Networks


Neural Network Architecture


Linear Regression example revisited


The Neuron


Neural Network Layers


The MNIST Dataset


Coding MNIST NN

Deep Learning using

Tensorflow


Deepening the network


Images and Pixels


How humans recognise images


Convolutional Neural Networks


ConvNet Architecture


Overfitting and Regularization


Max Pooling and ReLU activations


Dropout


Strides and Zero Padding


Coding Deep ConvNets demo


Debugging Neural Networks


Visualising NN using Tensorflow


Tensorboard

Transfer Learning using

Keras and TFLearn


Transfer Learning Introduction


Google Inception Model


Retraining Google Inception with our own data demo


Predicting new images


Transfer Learning Summary


Extending Tensorflow


Keras


TFLearn


K eras vs TFLearn Comparison



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