Best Artificial Intelligence institute in noida

  

Best Artificial Intelligence Training Institute in noida

Inovi Technologies is a market leader in Artificial Intelligence Training in Noida. Artificial Intelligence is going to be the next big thing in the field of technology and incredible number of organizations are adopting AI and the demand for AI experts is growing at a surprising rate! The Artificial Intelligence Course by Inovi Technologies will give you a comprehensive understanding of the concepts of artificial intelligence for beginners.

In this course, you will learn how to apply mathematical ideas in machine learning. You will find out the learning process and understand the application of various learning algorithms. The full AI course material has been prepared to get the best jobs in top multinational companies by industry professionals. As part of this Artificial Intelligence Training, you will work on real-time projects and assignments, which have a huge impact on the real world industry scenario, therefore, help you to easily promote your career. By the end of the course, AI certification will be provided to the candidates who will not be able to get back - Start with Inovi Technologies - Hold the wonderful opportunities. Learn the Artificial Intelligence Course today!

About Artificial Intelligence Training

Artificial Intelligence (AI) has a long history but is still growing and changing right and actively. In this course, you will learn about the basics of modern AI as well as some representative applications of AI such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and Python Tensorflow. By the way, we expect you to excite about many applications and huge possibilities in the field of AI, which continues to expand human capacity beyond our imagination. This Artificial Intelligence course will provide a comprehensive understanding of basic techniques for understanding intelligent computer systems and understanding how AI is going to be implemented.

https://www.inovitechnologies.com/Corporate-Training/Best-Artificial-Intelligence-training-institute-in-noida/

Introduction to Data Science Deep Learning & Artificial Intelligence

Introduction to Deep Learning & AI

Deep Learning: A revolution in Artificial Intelligence

1. Limitations of Machine Learning

What is Deep Learning?

1. Need for Data Scientists
2. Foundation of Data Science
3. What is Business Intelligence
4. What is Data Analysis
5. What is Data Mining

What is Machine Learning?

Analytics vs Data Science

1. Value Chain
2. Types of Analytics
3. Lifecycle Probability
4. Analytics Project Lifecycle
5. Advantage of Deep Learning over Machine learning
6. Reasons for Deep Learning
7. Real-Life use cases of Deep Learning
8. Review of Machine Learning

Data

1. Basis of Data Categorization
2. Types of Data
3. Data Collection Types
4. Forms of Data & Sources
5. Data Quality & Changes
6. Data Quality Issues
7. Data Quality Story
8. What is Data Architecture
9. Components of Data Architecture
10. OLTP vs OLAP
11. How is Data Stored?

Big Data

1. What is Big Data?
2. 5 Vs of Big Data
3. Big Data Architecture
4. Big Data Technologies
5. Big Data Challenge
6. Big Data Requirements
7. Big Data Distributed Computing & Complexity
8. Hadoop
9. Mapreduce Framework
10. Hadoop Ecosystem

Data Science Deep Dive

1. What Data Science is
2. Why Data Scientists are in demand
3. What is a Data Product
4. The growing need for Data Science
5. Large Scale Analysis Cost vs Storage
6. Data Science Skills
7. Data Science Use Cases
8. Data Science Project Life Cycle & Stages
9. Data Acquisition
10. Where to source data
11. Techniques
12. Evaluating input data
13. Data formats
14. Data Quantity
15. Data Quality
16. Resolution Techniques
17. Data Transformation
18. File format Conversions
19. Anonymization

Python

1. Python Overview
2. About Interpreted Languages
3. Advantages/Disadvantages of Python pydoc.
4. Starting Python
5. Interpreter PATH
6. Using the Interpreter
7. Running a Python Script
8. Using Variables
9. Keywords
10. Built-in Functions
11. Strings Different Literals
12. Math Operators and Expressions
13. Writing to the Screen
14. String Formatting
15. Command Line Parameters and Flow Control.

Lists

16. Tuples
17. Indexing and Slicing
18. Iterating through a Sequence
19. Functions for all Sequences

Operators and Keywords for Sequences

1. The xrange() function
2. List Comprehensions
3. Generator Expressions
4. Dictionaries and Sets.

Numpy & Pandas

1. Learning NumPy
2. Introduction to Pandas
3. Creating Data Frames
4. Grouping Sorting
5. Plotting Data
6. Creating Functions
7. Slicing/Dicing Operations.

Deep Dive – Functions & Classes & Oops

1. Functions
2. Function Parameters
3. Global Variables
4. Variable Scope and Returning Values. Sorting
5. Alternate Keys
6. Lambda Functions
7. Sorting Collections of Collections
8. Classes & OOPs

Statistics

1. What is Statistics
2. Descriptive Statistics
3. Central Tendency Measures
4. The Story of Average
5. Dispersion Measures
6. Data Distributions
7. Central Limit Theorem
8. What is Sampling
9. Why Sampling
10. Sampling Methods
11. Inferential Statistics
12. What is Hypothesis testing
13. Confidence Level
14. Degrees of freedom
15. what is p Value
16. Chi-Square test
17. What is ANOVA
18. Correlation vs Regression
19. Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python

Introduction

1. ML Fundamentals
2. ML Common Use Cases
3. Understanding Supervised and Unsupervised Learning Techniques

Clustering

1. Similarity Metrics
2. Distance Measure Types: Euclidean, Cosine Measures
3. Creating predictive models
4. Understanding K-Means Clustering
5. Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
6. Case study

Implementing Association rule mining

1. What is Association Rules & its use cases?
2. What is Recommendation Engine & it’s working?
3. Recommendation Use-case
4. Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

1. How to build Decision trees
2. What is Classification and its use cases?
3. What is Decision Tree?
4. Algorithm for Decision Tree Induction
5. Creating a Decision Tree
6. Confusion Matrix
7. Case study

Random Forest Classifier

1. What is Random Forests
2. Features of Random Forest
3. Out of Box Error Estimate and Variable Importance
4. Case study

Naive Bayes Classifier.

1. Case study

Project Discussion

Problem Statement and Analysis

1. Various approaches to solve a Data Science Problem
2. Pros and Cons of different approaches and algorithms.

Linear Regression

1. Case study
2. Introduction to Predictive Modeling
3. Linear Regression Overview
4. Simple Linear Regression
5. Multiple Linear Regression

Logistic Regression

1. Case study
2. Logistic Regression Overview
3. Data Partitioning
4. Univariate Analysis
5. Bivariate Analysis
6. Multicollinearity Analysis
7. Model Building
8. Model Validation
9. Model Performance Assessment AUC & ROC curves
10. Scorecard

Support Vector Machines

1. Case Study
2. Introduction to SVMs
3. SVM History
4. Vectors Overview
5. Decision Surfaces
6. Linear SVMS
7. The Kernel Trick
8. Non-Linear SVMs
9. The Kernel SVM

Time Series Analysis

1. Describe Time Series data
2. Format your Time Series data
3. List the different components of Time Series data
4. Discuss different kind of Time Series scenarios
5. Choose the model according to the Time series scenario
6. Implement the model for forecasting
7. Explain working and implementation of ARIMA model
8. Illustrate the working and implementation of different ETS models
9. Forecast the data using the respective model
10. What is Time Series data?
11. Time Series variables
12. Different components of Time Series data
13. Visualize the data to identify Time Series Components
14. Implement ARIMA model for forecasting
15. Exponential smoothing models
16. Identifying different time series scenario based on which different Exponential Smoothing model can be applied
17. Implement respective model for forecasting
18. Visualizing and formatting Time Series data
19. Plotting decomposed Time Series data plot
20. Applying ARIMA and ETS model for Time Series forecasting
21. Forecasting for given Time period
22. Case Study

Machine Learning Project

Machine learning algorithms Python

1. Various machine learning algorithms in Python
2. Apply machine learning algorithms in Python

Feature Selection and Pre-processing

1. How to select the right data
2. Which are the best features to use
3. Additional feature selection techniques
4. A feature selection case study
5. Preprocessing
6. Preprocessing Scaling Techniques
7. How to preprocess your data
8. How to scale your data
9. Feature Scaling Final Project

Which Algorithms perform best

1. Highly efficient machine learning algorithms
2. Bagging Decision Trees
3. The power of ensembles
4. Random Forest Ensemble technique
5. Boosting – Adaboost
6. Boosting ensemble stochastic gradient boosting
7. A final ensemble technique

Model selection cross validation score

1. Introduction Model Tuning
2. Parameter Tuning GridSearchCV
3. A second method to tune your algorithm
4. How to automate machine learning
5. Which ML algo should you choose
6. How to compare machine learning algorithms in practice

Text Mining & NLP

1. Sentimental Analysis
2. Case study

PySpark and MLLib

1. Introduction to Spark Core
2. Spark Architecture
3. Working with RDDs
4. Introduction to PySpark
5. Machine learning with PySpark – Mllib

Deep Learning & AI using Python

Deep Learning & AI

1. Case Study
2. Deep Learning Overview
3. The Brain vs Neuron
4. Introduction to Deep Learning

Introduction to Artificial Neural Networks

1. The Detailed ANN
2. The Activation Functions
3. How do ANNs work & learn
4. Gradient Descent
5. Stochastic Gradient Descent
6. Backpropagation
7. Understand limitations of a Single Perceptron
8. Understand Neural Networks in Detail
9. Illustrate Multi-Layer Perceptron
10. Backpropagation – Learning Algorithm
11. Understand Backpropagation – Using Neural Network Example
12. MLP Digit-Classifier using TensorFlow
13. Building a multi-layered perceptron for classification
14. Why Deep Networks
15. Why Deep Networks give better accuracy?
16. Use-Case Implementation
17. Understand How Deep Network Works?
18. How Backpropagation Works?
19. Illustrate Forward pass, Backward pass
20. Different variants of Gradient Descent

Convolutional Neural Networks

1. Convolutional Operation
2. Relu Layers
3. What is Pooling vs Flattening
4. Full Connection
5. Softmax vs Cross Entropy
6. ” Building a real world convolutional neural network
7. for image classification”

What are RNNs – Introduction to RNNs

1. Recurrent neural networks rnn
2. LSTMs understanding LSTMs
3. long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

1. Restricted Boltzmann Machine
2. Applications of RBM
3. Introduction to Autoencoders
4. Autoencoders applications
5. Understanding Autoencoders
6. Building a Autoencoder model

Tensorflow with Python

1. Introducing Tensorflow
2. Introducing Tensorflow
3. Why Tensorflow?
4. What is tensorflow?
5. Tensorflow as an Interface
6. Tensorflow as an environment
7. Tensors
8. Computation Graph
9. Installing Tensorflow
10. Tensorflow training
11. Prepare Data
12. Tensor types
13. Loss and Optimization
14. Running tensorflow programs

Building Neural Networks using

Tensorflow

1. Tensors
2. Tensorflow data types
3. CPU vs GPU vs TPU
4. Tensorflow methods
5. Introduction to Neural Networks
6. Neural Network Architecture
7. Linear Regression example revisited
8. The Neuron
9. Neural Network Layers
10. The MNIST Dataset
11. Coding MNIST NN

Deep Learning using

Tensorflow

1. Deepening the network
2. Images and Pixels
3. How humans recognise images
4. Convolutional Neural Networks
5. ConvNet Architecture
6. Overfitting and Regularization
7. Max Pooling and ReLU activations
8. Dropout
9. Strides and Zero Padding
10. Coding Deep ConvNets demo
11. Debugging Neural Networks
12. Visualising NN using Tensorflow
13. 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

Keras vs TFLearn Comparison

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