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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“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.
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AI Job Roles Available
Machine learning engineer
Business intelligence developer
Computer vision engineer
Who to Join AI
Data Analysts, Business Analysts
Also, anyone having interest to learn Artificial Intelligence
AI CAREER+ Services (Hyderabad)
- 1 AI CAREER+ Services (Hyderabad)
- 2 Module 1-Introduction to course
- 3 Module 2-Data science introduction
- 4 Module 3-Installations
- 5 Module 4-Python programming
- 6 Module 5-Libraries
- 7 Module 6-Data exploration
- 8 Module 7-Statistics
- 9 Module 8- Other Mathematics Concepts
- 10 Module 9-Machine Learning
- 11 Module 10-Deep Learning and other algorithms
- 12 Module 11-Natural Language Processing
- 13 WORKING ON DATA SCIENCE PROJECTS
- 14 WORK ENVIRONMENT
- 15 Course Highlights
AI Course Content
Module 1-Introduction to course
- Introduction to corporate training
- Software life cycle
- 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
- Installations required for data science
Module 4-Python programming
- Introduction to python
- Data Types
- Control Statements
- Data Structures – Lists, Sets, Tuples, Strings, Dictionaries,
- OOPS Concept
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
- Basics of Statistics
- Descriptive Statistics
- Inferential Statistics
- Qualitative vs. Quantitative Analysis
- Hypothesis Testing
- Data Distribution
Module 8- Other Mathematics Concepts
- 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
- 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
- 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 best
Note: Deals ‘n’ number of real time projects
- Status meetings
- Meetings with stockholders
- Participating in gathering requirements
- Working on client projects
- Coordinating team
- Ram 4GB Min
- Hard Disk 250GB
- Processor i3 Min
- A Dedicated Portal For Practicing.
- 1-1 Mentorship
- Internship Offers for Freshers.
- Weekly Assignments.
- Weekly Doubt Sessions
- Advanced Curriculum
- Certificates On successful Completion of Project .
- Resume Preparation Tips
- Interview Guidance And Support.
- Dedicated HR Team for Job Support And Placement Assistance.
Why Artificial Intelligence ?
- To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
- To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.
- 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
- 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
- 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
- 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.
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