Artificial Intelligence course Training Institute in Hyderabad

Best Artificial Intelligence Course Training in Hyderabad with Real-Time Experts. We Provide Artificial Intelligence Online Training and Classroom Training in Hyderabad. AI specialists can draw salaries in the range of whopping $300,000 to $500,000. By the end of 2020, 62% of the business will be depending on AI experts for a much better customer engagement and customized chat bots.

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✓ Practical Sessions

✓ Certifications

✓ Advanced curriculum

✓ Weekly Assignments

✓ Mock Interviews

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“Master Data Science with AI”

“4 Million jobs to be created by 2020”

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. It  is a field of Computer Science that gives computers the ability to learn without being explicitly programmed.

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering.

We are Happy to announce that Social Prachar awarded  as the Best Academy of the Year 2019 @ 7th Asian Education Summit, Mumbai.

Thanks to all my Students, Clients and Well wishers.

-Mahesh Babu Channa, Founder & CEO, Social Prachar

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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

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

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Program Overview

Key Highlights


Dedicated Portal


Internship Offers for Freshers


Weekly Assignments


Timely Doubt Sessions


Dedicated HR Team


Advanced Curriculums


1-1 Mentorship




Resume Preparation Tips


Interview Guidance and Support

AI CAREER+ Services (Hyderabad)

Quick list

AI Course Content

I. How to Be Successful with this Course

  • Train Your Brain
  • Methodology to Understand the Concepts Faster and Not Forget
  • Prepare Your Own PDF Material from IPython Notebooks
  • Plagiarism
  • Saving Your Work
  • Error Debugging

II. Introduction to Data Science and How to Be a Good Data Scientist:

  • What Is Data Science?
  • Why We Need Data Science?
  • What Data Scientist Do?
  • Roles in Data Science
  • Data Science Future
  • Skills Required for Data Science?
  • Math Requirement?
  • Statistics Requirement?
  • Knowledge Resources


III. Introduction to Case studies demonstrated and explained during course

  1. Case study 1: Natural Language Processing and Understanding Deep Learning
  2. Case study 2: Computer vision Processing techniques example in healthcare.
  3. Case study 3: Financial, Risk and Predictive solution modeling and development for various industries
  4. Case study 4: AI text-video-audio assistants, Product recommendations and self-driving AI.

1. SQL Fundamentals for Data Science

  • SQL Basics
  • Retrieve Data from SQL Database

2. R based Programming Fundamentals for Data Science

  1. R Basics, background
  2. Installing Packages – Loading And Unloading Packages
  3. Getting familiar with basics
  4. Operators in R – Arithmetic,Relational,Logical and Assignment Operators
  5. Variables,Types Of Variables,Using variables Conditional statements, ifelse(), switch
  6. Loops: For Loops,While Loops,Using Break statement,Switch
  7. The R Programming Language- Data Types
  8. creating data objects from the keyword.
  9. How to make different type of data objects.
  10. Types of data structures in R
  11. Arrays And Lists- Create Access the elements
  12. Vectors – Create Vectors,Vectorized Operations,Power of Vectorized Operations Matrices- Building the first matrices,Matrix Operations,Subsetting,visualising subset
  13. Data Frames- create and filter data frames,Building And Merging data frames.
  14. Functions And Importing data into R
  15. Function Overview – Naming Guidelines
  16. Arguments Matching,Function with Multiple Arguments
  17. Additional Arguments using Ellipsis,Lazy Evaluation Multiple Return Values Function as Objects,Anonymous Functions
  18. Importing and exporting Data into R- importing from files like excel,csv and minitab.
  19. Import from URL and excel Files
  20. Import from database.
  21. Data Descriptive
  22. Statistics,Tabulation,Distribution
  23. Summary Statistics for Matrix Objects. apply() Command. Converting an Object into a Table
  24. Histograms, Stem and Leaf Plot, Density
  25. Normal Distribution
  26. Graphics in R – Types of graphics
  27. Bar Chart,Pie Chart,Histograms- Create and edit.
  28. Box Plots- Basics of Boxplots- Create and Edit Visualisation in R using ggplot2.
  29. More About Graphs: Adding Legends to Graphs,Adding Text to Graphs, Orienting the Axis Label.

3. Big data & Statistical Analysis – Visualisation Using R ,Python, SPSS , SAS,Tableau & Excel

  1. Data Types
  2. Exploratory Data Analysis ( Mean , media , Mode , Range, SD , Variance, Skewness ,
  3. Kurtosis
  4. Data Visualizations
  5. Graphical Representation of Various Charts (Bar Plot, Box Plot etc…)
  6. Probability Distribution
  7. Confidence Interval
  8. Z Test
  9. T Test
  10. Anova
  11. Hypothesis Testing ( Type I and TYPE II ERRORS)

Imputation Technique & Regression Technique

  1. Scatter Plot
  2. Regression Analysis
  3. Simple Linear Regression with R
  4. Multiple Linear Regression with R
  5. Multiple Logistic Regression with R

Data Exploration

  • Data Mining – Unsupervised Learning using R
  • Data Mining – supervised Learning using R
  • Dimension Reduction – Principal Component Analysis Using R
  • Association Rules using R

4. Understanding Python based Programming fundamentals for Data Science

  • Getting started with Python
  • Python Overview
  • About Interpreter languages
  • Advance /Disadvantages of Python
  • Starting Python
  • Interpreter Path
  • Using the interpreter
  • Running a Python Script
  • Keywords
  • Built-In Function
  • String Different Literals
  • Math Operators and Expressions
  • Writing on the screen
  • String formatting
  • Command line parameters and Flow control
  • Numbers and Math:Arithmetic, Floats and Modulo
  • Ordering Operations
  • Variables and Inputs
    • Creating Variables
    • Input functions
  • Dictionary
  • Conditional Statements
    • If
    • Else
    • Elif
  • Loops
    • While loop
    • For loop
  • Reading and Writing
  • Modules and Packages

Sequence and File operations

  • string
  • Lists
  • Tuples
  • Set
  • Dictionary
  • Indexing and Slicing
  • Iterating through a sequence
  • Functions for all sequence
  • Using Enumerate()
  • Operators and Keywords for sequence
  • The xrange() Function
  • List comprehensions
  • Generator expression

Deep Dive – Function sorting Error and Exception Handling

  • Functions
  • Functions parameters
  • Variable scope and Returning values. sorting
  • Alternative Keys
  • Lambda Functions
  • Sorting collection of collections
  • Sorting dictionaries
  • Sorting list in place
  • Errors and Exceptions handling
  • Handling Multiple Exceptions
  • The standard Exception hierarchy
  • Using Modules
  • The import statement Module search path
  • Package installation ways

Regular Expression Packages and Object oriented programming in python

  • The Sys Module
  • Interpreter iteration
  • Launching external programs
  • Paths Directories and filenames
  • Walking Directory tree
  • Math function
  • Random numbers
  • Zipped Archives
  • Introduction to Python class
  • Defining classes
  • Initializes
  • Instance Methods
  • Properties
  • Class Methods and Data Static Methods
  • Private Methods and Inheritance
  • Module Aliases and Regular Expressing


  • Crash course on Pandas –For Data Manipulation
  • Crash course on Numpy – For Array-Processing
  • Crash course on Matplotlib – For Visualization
  • Scipy
  • Scikit-Learn
  • Keras
  • Seaborn
  • Cufflinks
  • NLTK

Debugging, Databases and project skeletons

  • Debugging
  • Dealing with Errors


Linear Algebra

  • Introduction to Linear Algebra
  • Applications of Linear Algebra

5. Deep Dive into Statistics

Understand Your Data Using Descriptive Statistics

  • Measure of central tendency
  1. Mean
  2. Median
  3. Mode
  • Measures of Dispersion
  1. Range quartile
  2. Interquartile range
  3. Variance and Standard deviation
  • Measures to describe shape of distribution
  1. Skewness
  2. Kurtosis
  • Probability Distributions
  • Sampling Distributions and Confidence Intervals

Understand Your Data Using Inferential Statistics

  1. Hypothesis testing
  2. Chi Square Statistic and Contingency Tables
  3. T-test or ANOVA
  4. Correlation

6. Concepts of Data

  • Collecting data from different sources
  • Analyzing data
  • Data preprocessing
  • Data munging
  • Data mining
  • Data manipulation
  • Data visualization
  • Feature Selection
  • Feature Scaling
  • Dimensionality reduction

7. Data Exploration or Exploratory Data Analysis (EDA)

  1. Variable Identification
  2. Univariate Analysis (Exploring Individual Features)
  3. Bivariate Analysis (Exploring Two or Multi-Feature Relationships)
  4. Covariance & Correlation
  5. Multicollinearity
  6. Dimensionality Reduction using PCA


8. Data Preparation and Transformation

  • Data Type Conversion
  • Missing Values Treatment
  • Outlier Treatment
  • Variable Transformation
    • Data Normalization
    • Data Standardization
    • Box Cox Transformation
  • Variable creation
    • Dummy Variable Creation
    • Feature Engineering

Performance Measurement of Models

  1. Measures for a classification model:
  • Accuracy
  • Confusion matrix, TPR, FPR, FNR, TNR
  • Precision and recall, F1-score
  • Receiver Operating Characteristic Curve (ROC) curve and AUC
  • Log-loss
  1. Measures for a Regression model:
  • R-Squared/Coefficient of determination
  • Median absolute deviation (MAD)

9. Machine Learning

Supervised Machine Learning

Unsupervised Learning/Clustering

  • K Means Clustering
  • Hierarchical Clustering
  • DBSCAN (Density Based Clustering)
  • Evaluation Metrics for Clustering

Reinforcement Learning

Linear Regression

  • Intuition of linear regression
  • Mathematical formulation
  • How to use linear regression in real world
  • Interpret the results of linear regression

Logistic Regression:

  • Intuition of Logistic Regression
  • Creating a Sigmoid Function from Linear Equation
  • Probabilistic Interpretation

Decision Trees

  • Intuition of decision tree
  • Various ways in building decision tree
    • Entropy
    • Information Gain
    • Gini Impurity
  • Preprocessing for Decision Tree
  • Overfitting and Under fitting
  • Prediction Using Decision Tress

K-Nearest Neighbors

  • Intuition of KNN
  • Distance measures
  • How to measure the effectiveness of k-NN?
  • Prediction Using KNN


Support Vector Machines

  • Intuition of KNN
  • Maximal-Margin Classifier and Its Calculation
  • Real World Problems with Margin Classifier
  • Different Type of Kernels
  • How to Learn a SVM Model?
  • Preparing Data for SVM
  • Prediction Using KNN

Ensemble Models

  • Bootstrap Method
  • Bagging
  • Random Forest
  • Variable Importance
  • Preparing Data

Re-sampling Techniques

  • Leave one out cross validation (LOOCV)
  • K-fold
  • Repeated Hold-out Data
  • Stratified k-fold cross validation

Solving Optimization Problems

  • What is Optimization
  • Why Optimization
  • Applications of Optimization
  • What is Gradient Descent?
  • Varieties in Gradient Descent algorithm
  • Implementation of Gradient Descent

Hyper Parameter Tuning

  • Why Tuning
  • Manual Tuning
  • Grid search

Model Deployment

  • Saving Model in a Pickle File
  • Model load from Pickle file and Prediction


10. Feature Engineering-Selection for Machine learning models

  • Univariate Selection.
  • Recursive Feature Elimination.
  • Principal Component Analysis (PCA)
  • Feature Importance.

11. Deep learning & Transfer Learning based solution engineering with Hadoop,Keras,Spark, pytorch, Tensorflow, AWS & Azure ML

  • How Biological Neurons work?
  • Introduction to Deep Learning
  • Introduction to tools used often in Machine Learning & Deep Learning
  • Setting up environment for Deep Learning
  • Perceptron Learning
  • Multi-Layered Perceptron (MLP)
  • Backpropagation
  • Activation functions
  • Neural Network Application and Parameter Tuning
  • Introduction to Transfer Learning

Artificial Neural Networks(ANN)

Convolutional Neural Networks(CNN)

  • How CNN works
  • Steps in CNN
  • Convolution in CNN
  • Convolutional Operation
  • Relu Layers’
  • What is pooling vs Flattening
  • Full connection
  • Softmax vs Cross Entropy
  • Max Pooling in CNN
  • Flattening in CNN

Recurrent Neural Network(RNN)

  • Exploding Gradient
  • Vanishing Gradient
  • LSTM

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 ARMA Model
  • Illustrate the working and implementation of different ETS Models
  • Forecast the data using the respective model
  • What is Time series data?
  • Time series variable
  • Different components of Time Series data
  • Visualize the data to identify time series component
  • Implement ARIMA Model for forecasting
  • Exponential smoothing model
  • 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

12. Natural Language Text-audio-video Processing/Mining/Analysis

  • Tokenization
  • Part of Speech Tagging
  • Chunking
  • Stemming
  • Important Tasks in NLP
  • Important Libraries for NLP
  • Traditional NLP/ NLP with Deep learning
  • Understand & Implement word2vec
  • Understand & build a project on GloVe
  • Project on Advanced Sentiment Analysis using Deep Learning
  • Introduction to Deep learning based Computer vision & Applications

IV. Monthly Tests

V. Mock interviews

VI. IBM Certification


Course Highlights

1.  A Dedicated Portal For Practicing.
2. Real Time Project Data Models to Work
3. 1-1 Mentorship
4. Internship Offers for Freshers.
5. Weekly Assignments.
6. Weekly Doubt Sessions
7. Advanced Curriculum
8. Certificates On successful Completion of Project .
9. Resume Preparation Tips
10. Interview Guidance And Support.
11. Dedicated HR Team for Job Support And Placement Assistance.
12. Experienced Trainers.

Course Highlights

  1. A Dedicated Portal For Practicing.
  2. 1-1 Mentorship
  3. Internship Offers for Freshers.
  4. Weekly Assignments.
  5. Weekly Doubt Sessions
  6. Advanced Curriculum
  7. Certificates On successful Completion of Project .
  8. Resume Preparation Tips
  9. Interview Guidance And Support.
  10. Dedicated HR Team for Job Support And Placement Assistance.

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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