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Artificial Intelligence Course Training in Bengaluru 

SocialPrachar offers Best Artificial Intelligence Course training in Bengaluru. 3000+ Trainees rated us Best  Artificial Intelligence Course Institute  Training in Maratahalli, Bangalore. 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 with 15+ Projects

Modes of Training Available:

  1. Artificial Intelligence Course Training in Bengaluru (offline)
  2. Daily & Weekend Batches – Artificial Intelligence Course Training in Bengaluru 
  3. AI Advanced+Internship Course
  4. AI Online Course (Daily Batches)
  5. AI Crash Course
  6. Artificial Intelligence Course Training in Bengaluru (Corporate Training)

New Offline/Classroom Daily  batch starts on 19th October 2020

Offer valid till Dec 14th 2020

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  • Get instant 20% Off , if you are 2016-2018 passed-out

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Academy of the Year 2019 - '20

We are happy to announce that Social Prachar has been awarded as the Best Academy of the Year 2019 – 2020 @7th Asian Education Summit, Mumbai Presented by Juhi Chawla, former Miss India

We Are BACK !!! New Classroom/offline batch Starts on 19th October 2020.

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A NOTE FROM OUR CEO

Dear Engineers please upskill fast enough to meet ever-changing market needs. UpGrading & UpSkilling to the market requirements is the need of the Hour. A Wipro study reveals that 75% organizations find the need to upgrade IT infrastructure. At a Time when most organizations take digital transformation plunge, AI, Data Science, Cloud computing, Cyber security & UI/UX related jobs remain in HIGH DEMAND. Your Flexibility and skills will be a HUGE asset to you post covid19 job market.

Learn TODAY ~ Lead TOMORROW

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“58 Million jobs to be created in Next Few Years”

According to the World Economic Forum Report. The growth of Artificial Intelligence could create 58 million jobs in next few years.

Enroll for Our Specialization Program – AI & ML

  • 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.

Take a look on Top Artificial Intelligence Companies, Job Roles and Packages  in India

 

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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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Program Overview

Artificial Intelligence Course Training in Bengaluru - Key Highlights

Artificial Intelligence Course Training in Bengaluru - Course Content

  1. What is Data Science? – Introduction.
  2. Importance of Data Science.
  3. Demand for Data Science Professional.
  4. Brief Introduction to Big data and Data Analytics.
  5. Lifecycle of data science.
  6. Tools and Technologies used in data Science.
  7. Installing Python IDE
  8. Installing Python Environments like Jupyter, Pycharm, Spyder etc.
  9. Installing Packages – Loading and Unloading Packages

 

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

 

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
Object Oriented Programming Introduction | Class & Instance Attributes | Properties vs getters and setters | Inheritance | Abstract Classes

 

DATA ANALYSIS & VISUALIZATION WITH PYTHON
Numpy module Basics of numpy | creating multidimensional arrays | Numpy operations
Pandas Introduction to Pandas  |  IO Tools  |  Pandas – Series and Dataframe and their wide range of functionalities
Matplotlib, Seaborn & Word Cloud Graphical representation of data using various plots like bar plots, Pie plot, Histogram, Scatter plot, Box plot etc.|

Creating word clouds with text data

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
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

 

 

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 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: Recommendation for Movie, Summary

Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details, and others.   

 

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 ALGORITHMS & 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  |  Different 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/facial images

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

PROJECT 4

 

Title: Traffic Signs Recognition

Description:

In self-driving cars in which the passenger can fully depend on the car for traveling. But to achieve level 5 autonomous, it is necessary for vehicles to understand and follow all traffic rules.

In the world of Artificial Intelligence and advancement in technologies, many researchers and big companies like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, etc are working on autonomous vehicles and self-driving cars. So, for achieving accuracy in this technology, the vehicles should be able to interpret traffic signs and make decisions accordingly.

Highlights: This Python project is about building a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

 

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

 

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

Artificial Intelligence Course Training in Bengaluru – Next Batch on Oct 14th 2020

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, AI Chatbots

  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

The Important Roles of AI Engineers in 2020

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

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