Advanced Artificial Intelligence Training in Bengaluru

Social Prachar is the Top rated Artificial Intelligence training institute in Bangalore. We provide Artificial Intelligence Course in Bangalore with real time trainers and live projects. We train students from basics to advanced concepts with real-time client scenarios and case studies. Our AI Course training makes you strong in Artificial Intelligence areas and gives you a new height to the future. We provide excellent platform to the students to learn Advanced technologies and explore the Subject from Industry experts with our Artificial Intelligence Master Program.

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“4 Million jobs to be created by 2020”

  • Artificial Intelligence course is on demand and most adorable technology for the fresh graduates as well as professionals who are willing to Kick-start their career in robotic /artificial world. Artificial intelligence is a process of the computers or robots can perform tasks intelligently by using Machine Learning,Computer Vision, Natural Language Processing and Deep Learning techniques.
  • Artificial intelligence (AI) is a new factor of production and has the potential to introduce new sources of growth, changing how work is done and reinforcing the role of people to drive growth in business.
  • Accenture research on the impact of AI in 12 developed economies reveals that AI could double annual economic growth rates in 2035 by changing the nature of work and creating a new relationship between man and machine. The impact of AI technologies on business is projected to increase labor productivity by up to 45 percent and enable people to make more efficient use of their time.

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AI Job Roles Available

Machine learning engineer
Data scientist
Research scientist
Business intelligence developer
Computer vision engineer

Who to Join AI

Graduates
Post Graduates
IT Professionals
Data Analysts, Business Analysts
Python Professionals
Also, anyone having interest to learn Artificial Intelligence

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AI Course Content

  INTRODUCTION TO STATISTICS
Basic business statistics How businesses use statistics |The basic vocabulary of statistics |The types of data used in business
Organizing and Visualizing Data The sources of data used in business | To construct tables and charts for numerical data | To construct tables and charts for categorical data | The principles of properly presenting graphs
Numerical Descriptive Measures To describe the properties of central tendency, variation, and shape in numerical data | To construct and interpret a boxplot | To compute descriptive summary measures for a population | To compute the covariance and the coefficient of correlation

 

Basic Probability and Terms Basic probability concepts | Conditional probability | To use Bayes’ Theorem to revise probabilities | Various counting rules
Probability Distributions Types of Distributions like Discrete and Continuous | Functions of Random Variables | Probability Distribution

Graphs

 

Sampling and Sampling Distributions To distinguish between different sampling methods | The concept of the sampling distribution | To compute probabilities related to the sample mean and the sample proportion | The importance of the Central Limit Theorem
Confidence Interval Estimation To construct and interpret confidence interval estimates for the mean and the proportion | How to determine the sample size necessary to develop a confidence interval estimate for the mean or proportion | How to use confidence interval estimates in auditing
Fundamentals of Hypothesis Testing Null and Alternate hypothesis | One sample Tests | Test statistic and critical values | Possible errors in testing | p-value approach | t and z-tests | Testing on proportions

 

DATA TRANSFORMATION AND VISUALIZATION

 

Data Transformations Merge, Rollup, Transpose and Append | Smoothing | Aggregation | Normalization | Attribute construction
Feature Engineering Missing value Imputation | Outlier Analysis and Treatment | Binning | Creating dummy variables | feature scaling | Extracting Date | Log Transformation | Feature split | Label Encoding | One-Hot Encoding
Exploratory Data Analysis Summarizing and Visualizing the Important Characteristics of Data | Visualization techniques – Box plot | Histogram | Multi-variate chart | Pareto chart | Scatter plot | Stem-and-leaf plot.

 

PYTHON PROGRAMMING

 

 

 

 

Introduction to Python Installation | Python Basics | Spyder IDE | Jupyter Notebook | Floats and Strings Simple Input & Output | Variables | Operators | Single and Multiline Comments | Taking input from user
Data Structures List | Strings | Tuple | Dictionary | Sets and their examples
   
Conditional Statements If | if-else | if-elif-else | nested if else
Loops For | while | nested loops| loop control statements
Functions Creating user defined Functions | Function arguments like-Required, Keyword, Default and variable-length | Scope of variables in creating functions | Anonymous Functions – Lambda
Exception and File handling Exception Handling | Raising Exceptions | Assertions | Files I/O

 

 

 

 

  INTRODUCTION TO MACHINE LEARNING
Introduction What is Machine Learning? | End-to-end Process of Investigating Data Through a Machine Learning Lens | Evolution and Trends | Application of Machine Learning | Best Practices of Machine Learning
Machine Learning Methods Supervised | Unsupervised
Machine Learning Algorithms Classification |  Regression | Time Series | Collaborative Filtering | Clustering | Principal Component Analysis

 

 

 

  MACHINE LEARNING WITH PYTHON
Numpy module Basics of numpy | creating arrays | Numpy functions
Pandas Introduction to Pandas | IO Tools | Pandas – Series and Dataframe and their functions
Matplotlib and Seaborn Graphical representation of data using various plots
Scikit learn Introduction to SciKit Learn | Load Data into Scikit Learn | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classification & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Decide What’s the Best Model for Every Scenario
   
  MACHINE LEARNING APPLICATIONS
Linear Regression Implementing Simple & Multiple Linear Regression | Making Sense of Result Parameters | Model Validation | Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity | Prediction and Confidence Intervals | Use Cases
Logistic Regression Implementing Logistic Regression| Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures | Model Validation: Cross Validation, ROC Curve, Confusion Matrix | Use Cases
Decision Tree Implementing Decision Trees | Homogeneity | Entropy Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Errors

 

Naïve Bayes Bayes Theorem | Gaussian Naïve Bayes & its implementation | Multinomial Naïve Bayes, Count vectorizer, TF-IDF Vectorizer and its use cases on Text Classification
K-Nearest Neighbors Concept of nearest neighbors | Euclidean Distance | Use Cases of KNN Classifier & Regressor
Support Vector Machine Introduction to support vectors | Concept of hard & soft margins | slack variable | Lagrangian Primal & Dual | Kernel Trick | Use Cases
Time Series Introduction | Components of Time Series | Stationarity | ACF | PACF | ARIMA model for forecasting | Use Cases
K-Means Clustering Clustering concept | Finding optimal number of clusters | Use Cases
Hierarchical Clustering Agglomerative & Divisive Clustering | Dendrograms | Linkage Matrix like Single, Complete & Average | Use Cases
Ensemble Techniques Bagging | Boosting | Stacking | Regularization | Different cross-validation techniques used to treat Over fitting and Under fitting in machine learning models
Principal Component Analysis Concept of Dimensionality reduction | Eigen values | Eigen

Vectors | Use Cases

 

 

 

 

  PROJECTS
PROJECT 1 Title: Real Estate Price Prediction using Linear Regression

Industry: Real Estate

Description: The goal of this Use-case is to make property price predictions using Real Estate data. The dataset contains the of the price of apartments and various characteristics of the property. Based on this data, decide on the price of new properties.

PROJECT 2 Title: Loan Prediction using Logistic Regression & Decision Tree

Industry: Finance

Description: The goal is to build a classification model to predict if a loan is approved or not. Dataset contains demographic information like age, income etc. of various customers. Based on this, predict for a new customer whether loan will be approved or not

PROJECT 3 Title: Student’s Performance Prediction using KNN & Naïve Bayes

Industry: Education

Description: The goal is to build a classification model to predict student’s performance from various characteristics associated with student’s academics

PROJECT 4 Title: Handwritten digit recognition using Decision Tree &Random forest

Description: Dataset contains various pixel values of handwritten digits between 0 to 9. The goal is to build a classification model to predict the digit from its pixel values.

PROJECT 5 Title: Amazon fine food review-Sentiment Analysis

Industry: Amazon

Description: The analysis is to study Amazon food review from customers and try to predict whether a review is positive or negative. The dataset contains more than 500k reviews with number of upvotes and total votes to those comments

 

 

DEEP LEARNING APPLICATIONS

 

 

 

 

Neural Networks Understanding Neural Networks  |  The Biological Inspiration  |  Perceptron Learning & Binary Classification  | Backpropagation Learning  |  Learning Feature Vectors for Words  |  Object Recognition
Keras Keras for Classification and Regression in Typical Data Science Problems  |  Setting up KERAS  |  Di erent Layers in KERAS  |  Creating a Neural Network Training Models and Monitoring  |  Artificial Neural Networks
Tensorflow Introducing Tensorflow  |  Neural Networks using Tensorflow  |  Debugging and Monitoring  |  Convolutional Neural Networks
Recurrent Neural Networks Introduction to RNN | RNN long & short term dependencies | Vanishing gradient problem | Basic LSTM | Step by step walk through LSTM | Use cases
PROJECT 1

ANN ON KERAS

Title: Credit Default using ANN on Keras

Industry: Finance

Description: This research aimed at the case of customers’  default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification – credible or not credible clients.

 

PROJECT 2

CNN ON KERAS & TENSORFLOW

Title: Handwriting/Facial recognition using CNN on TensorFlow & Keras

Industry: Pattern Recognition

Description: This project will help build a model using Convolutional Neural Network to recognize handwriting/faces

 

PROJECT 3

LSTM

Title: Google stock price prediction

Industry: Finance

Description: Dataset contains dates, volume, open, close, high and low prices of stocks. Based on this build a LSTM model to predict current and future stock price

 

 

 

Computer Vision with Python:

  • Introduction to OpenCV
  • Core Operations
  • Image Processing in OpenCV
  • Feature Detection and Description
  • Video Analysis
  • Machine Learning
  • Object Detection
  • OpenCV-Python Bindings

 

NLP with Python:

  • Introduction to NLTK
  • Tokenizing words and Sentences with NLTK
  • Stop words and Stemming with NLTK
  • Part of Speech Tagging with NLTK
  • Chunking & Chinking
  • Lemmatizing with NLTK
  • Wordnet with NLTK
  • Converting words to Features
  • Text Classification with NLTK
  • Combining Algorithms with NLTK
  • Creating a module for Sentiment Analysis
  • Twitter Sentiment Analysis with NLTK
  • Named Entity Recognition with Stanford NER Triggers
  • Testing NLTK and Stanford NER Triggers for Accuracy and Speed

 

R-Programming

  • Over view and basic syntax of R
  • R variables and operators
  • R Objects
    • Vector
  • List
  • Matrix
  • Array
  • Factor
  • Data Frame
  • Conditional statements of R
    • If
  • Else-if
  • If -else if-else
  • Nested if-else
  • Loops in R
    • Repeat loop
  • While loop
  • For loop
  • Loop control statements
  • Creating user defined functions in R
  • Data reshaping and File handling
    • Reading excel, csv, xml files in R
  • Reading Web data in R
  • Connecting to Data bases in R
  • R-Packages
  • Data manipulation with dplyr package(Case studies on data manipulation with real time datasets)
  • Data visualization with ggplot package & case studies
  • Creating Interactive Dash Boards with R markdown and Shiny

 

SQL (Structured Query Language)

  • Introduction to SQL
  • SQL Select Statements
  • Execute a basic SELECT statement
  • Restricting and Sorting Data
  • Limit the rows retrieved by a query
  • Sort the rows retrieved by a query
  • Single-Row Functions
  • Describe various types of functions available in SQL
  • Use character, number, and date functions in SELECT statements
  • Describe the use of conversion functions
  • Displaying Data from Multiple Tables
  • Write SELECT statements to access data from more than one table using equality and nonequality joins
  • View data that generally does not meet a join condition by using outer joins
  • Join a table to itself by using a self join
  • Aggregating Data Using Group Functions
    • Identify the available group functions
  • Describe the use of group functions
  • Group data using the GROUP BY clause
  • Include or exclude grouped rows by using the HAVING clause
  • Subqueries
  • Manipulating Data
    • Describe each DML statement
  • Insert rows into a table
  • Update rows in a table
  • Delete rows from a table
  • Merge rows in a table
  • Control transactions
  • Creating and Managing Tables
  • Including Constraints
    • Describe constraints
  • Create and maintain constraints

 

Tableau

  • Introduction to Tableau Software
  • Tableau Worksheets
  • Tableau Calculations
  • Tableau sort and filter
  • Tableau charts
  • Tableau Advanced
  • Case Study with Tableau

 

ALSO READ

Why Artificial Intelligence ?

  1. To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
  2. To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.
  3. The goal of AI is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and feel.

Real Life Applications of AI

  1. Expert Systems

The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

Examples: Flight-tracking systems, Clinical systems

  1. Natural Language Processing

Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.

Examples: Google Now feature, speech recognition, Automatic voice output

  1. Neural Networks Examples

Yet another research area in AI, neural networks, is inspired from the natural neural network of human nervous system.

Examples: Pattern recognition systems such as face recognition, character recognition, handwriting recognition.

  1. Robotics

Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots.

Examples: Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving etc.

5. Fuzzy Logic

Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.

Examples: Consumer electronics, automobiles, etc

 

“Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications”

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