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.

✓ Interview Guidance

✓ Mentorship

✓ Experienced Trainers

Dedicated Portal

✓ 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

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

Key Highlights

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Dedicated Portal

discount

Internship Offers for Freshers

assignment

Weekly Assignments

doubtsessions

Timely Doubt Sessions

hr

Dedicated HR Team

curriculum

Advanced Curriculums

hr

1-1 Mentorship

certificate

Certificates

resume

Resume Preparation Tips

interview

Interview Guidance and Support

  • According to Market estimates, close to 1,20,000 positions related to Analytics & Data Science are currently available to be filled in India.
  • This is almost 45% jump in the open job requirements, compared to last years job to employment ratio.
  • Compared to worldwide market estimates, India contributes 8% of open job openings currently. Growth in the number of data science jobs globally was very much higher than India but India will lead the job growth market by 2x before 2022.
  • Top 10 leading organizations with the most number of analytics openings this year are – Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte.
  • Almost 90% of analytics jobs advertised in India are of a full-time basis. Just 10% form the part-time, internship or contractual jobs.
  • Top designations advertised are: Data Scientist, Data Engineer, Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultant, Statistical Analyst etc
  •  Top industries hiring analytics talent: BFSI sector has the maximum demand for data science skills in India followed by e-commerce and telecom, Banking etc 
  • Increase in Data Science jobs offering more than 15 lakh per annum based on the experience of the candidate, companies and their requirements.
  • Current Hiring trend indicates demand for junior level talent rises with  According to our estimate, as compared to the previous year and Senior profiles are always Hottest in the market.

AI CAREER+ Services (Hyderabad)

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

 

Module 1-Introduction to course

  • Introduction to corporate training
  • Software life cycle
  • Methodologies
  • Soft skills
  • Professional Ethics
  • Make you strength
  • Where I Stands?

    Module 2-Data science introduction

  • What is data science
  • Need for data science?
  • Data science vs Business Intelligence
  • Prerequisite for learning data science
  • What does a data scientist do?
  • Data science life cycle with example
  • Demand for data science

    Module 3-Installations

• Installations required for data science

Module 4 -Python programming

  • Introduction to python
  • Operators
  • Data Types
  • Control Statements
  • Functions
  • Data Structures – Lists, Sets, Tuples, Strings, Dictionaries,
  • OOPS Concept

    Module 5 -Libraries

    • Numpy
    • Pandas
    • Scipy
    • Scikit-Learn • Keras

    • Matplotlib • Seaborn
    • Cufflinks • NLTK

    Note: Installations and work end to end. As per requirements going to work with different libraries.

Module 6 – Data Exploration

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

    Module 7 -Statistics

  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative vs. Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution

    Module 8 – Other Mathematics Concepts

  • Probability
  • Calculus
  • Linear algebra

    Module 9 -Machine Learning

  • Introducing Machine Learning models
  • Supervised learning
  • Regression and Classification models
  • Unsupervised learning
  • Clustering and Aggregation models
  • Semi supervised learning
  • Over fitting and under fitting(Linear, Logistic, Navi Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest …..)Note: Deals with Mathematics and Programming part

    Module 10 -Deep Learning and other algorithms

  • Introduction to Deep learning
  • OpenCV
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Tensor flow
  • Over fitting and under fitting

Module 11 -Natural Language Processing

  • Introduction to NLP
  • Text Preprocessing
  • Lemmatization
  • Stemming
  • Text to features
  • Important Tasks in NLP
  • Important Libraries for NLP

    WORKING ON DATA SCIENCE PROJECTS

  • Examine your problem
  • Prepare your data (raw data, feature extraction, feature engineering, etc.)
  • Spot-check a set of algorithms
  • Examine your results
  • Double-down on the algorithms that worked bestNote: Deals ‘n’ number of real time projectsWORK ENVIRONMENT
  • Status meetings
  • Meetings with stockholders
  • Participating in gathering requirements
  • Working on client projects
  • Coordinating teamSystem Requirements
  • Ram 4GB Min
  • Hard Disk 250GB
  • Processor i3 Min

II. Monthly Tests

III. Mock interviews

IV. Certifications

 

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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