PROMPT ENGINEERING COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in is a premier training institute that excels in offering comprehensive engineering courses tailored for beginners, intermediate learners, and experts alike. Our courses are designed to cater to all skill levels, ensuring that everyone, from novices to seasoned professionals, can benefit from our training programs. With a focus on hands-on learning and practical applications, our courses empower beginners to build a strong foundation, guide intermediate learners to enhance their skills, and challenge experts with advanced knowledge. At LearnAI.co.in, we take pride in providing accessible, quality education to all, fostering a community of engineers who are well-prepared to tackle the challenges of today's dynamic tech landscape.

Upcoming Batches

CourseBranchTraining TypeWeekDay
Prompt EngineeringDilsukhnagarHybrid1stWednesday
Prompt EngineeringDilsukhnagarHybrid2ndMonday
Prompt EngineeringDilsukhnagarHybrid3rdWednesday
Prompt EngineeringDilsukhnagarHybrid4thMonday

What is Prompt Engineering?

Prompt engineering is the process of quickly designing and creating solutions to technical problems or challenges. It involves applying engineering principles and practical know-how to find swift and effective solutions for a variety of real-world issues.

Why to learnPrompt Engineering?

Learning prompt engineering is essential for career growth as it equips you with the skills to swiftly tackle complex technical problems, making you a valuable asset to employers. In today's fast-paced world, professionals with prompt engineering expertise are in high demand, offering excellent job prospects and opportunities for career advancement.

Who can Pursue Prompt Engineering?

  • Any Degree pursuing or Graduated,
  • Bachelor’s, Master’s, PhD, and
  • Anyone interested in course
Enroll to Course Now

COURSE CURRICULUM

Python

  • Introduction
  • Installation
  • Fundamental of Python
  • Variables
  • Comments
  • Print Statement
  • Operators
  • Mutable Data Types
  • Data Types
  • Special Data Types
  • Conditions
  • Loops
  • Functions
  • Break, Continue and Pass Statements
  • String Object and working
  • List
  • Tuple
  • Set
  • Dictionaries
  • Map
  • Reduce
  • Filter
  • Classes
  • Objects
  • Inheritance
  • Multiple Inheritance
  • Modules
  • Error Handling

Data Visualization

  • Matplotlib
  • Seaborn
  • Plotly
  • Cuflinks
  • Bokesh

Libraries

  • Numpy
  • Pandas
  • Random
  • Math
  • Scipy
  • sklearn
  • Keras
  • Tensorflow
  • OpenCV
  • NLTK
  • Spacy
  • Lot more…

Tableau

  • Introduction to Data Visualization
  • Introduction to Tableau
  • Basics charts and dashboards
  • Special Char Types
  • Dashboard design and principles
  • Connections with servers
  • Local file access
  • Hands on experience with worksheet

Web Scraping

  • Url
  • Beautiful Soup

Data base

  • SQL
  • MongoDB

Statistics

  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Analysis
  • Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution
  • Probability Distribution
  • Normal Distribution
  • Poison Distribution
  • Outlier Detection
  • Other Statistical Fundamentals

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Semi Supervised Learning
  • Reinforcement Learning

Supervised Learning

  1. Regression Models
  2. Classification Models

Regression Models

  • Introduction to Regression Models
  • Linear Models
  • Linear Regression
  • Multiple Regression
  • Ordinary Least Square Method
  • Non-Linear Models
  • Support Vector Regressor
  • Random Forest Regressor
  • Decision Tree

Evaluation Metrics for Regression Models

  • R-Square
  • Adjusted R Square
  • Mean Square Error
  • Root Mean Square Error
  • Mean Absolute Error

Classification Models

  • Introduction to Classification Models
  • Logistics Regression
  • Naïve Bayes
  • Support Vector Classifier
  • K-NN Classifier
  • Decision Tree Classifier
  • Random Forest Classifier

Evaluation Metrics for Classification Models

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC Curve

Unsupervised Learning

  • Introduction to Unsupervised Learning

Clustering Models

  • Introduction to Clustering Models
  • K- Mean Clustering
  • Hierarchical Clustering
  • High Dimensional Clustering

Dimension Reduction

  • Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

  • Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

  • Feature Extraction
  • Model Defects & Evaluation Metrics
  • Model Selection and Tuning
  • Comparison of machine learning models

Neural Networks

  • Introduction to Deep Learning
  • Fundamental of Neural Networks
  • TensorFlow and Keras
  • Artificial Neural Networks (ANN)
  • Convolution Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Evaluation of Deep Learning
  • Neural Networks Basics
  • Gradient Descent
  • Introduction to Perceptron & Neural Networks
  • Batch Normalization
  • Activation and Loss Functions
  • Hyper parameter tuning
  • SoftMax
  • Deep Neural Networks
  • Weights initialization

Image Pre-processing

  • Computer Vision
  • Open CV
  • Noise Detection
  • Noise Reduce
  • Low Pass Filters
  • Forward Propagation
  • Backward Propagation
  • Pooling & Padding

Natural Language Processing

  • Introduction to NLP
  • Corpus
  • Natural Language Understanding (NLU)
  • Natural Language Generator (NLG)
  • Tokenization (Word, Sentence, Blank, RegEx)
  • Frequency Distribution
  • Filtering
  • Stemming
  • Lemmatization
  • Stop Words
  • Regular Expressions
  • POS Tagging
  • Syntax Tree
  • Chunking
  • Lemmas
  • Hypernyms
  • Hyponyms
  • Synonyms
  • Antonyms
  • Distance Between words
  • Wordnet
  • Name Entity Recognition
  • Bag of words or Document Matrix
  • Count Vectorization
  • Term Frequency
  • Inverse Document Frequency
  • Sentimental Analysis

Job Roles

  • Prompt Engineer
  • Problem Solver
  • Technical Analyst
  • Operations Manager
  • Project Manager
  • Quality Assurance Engineer
  • Network Administrator
  • Systems Architect
  • DevOps Engineer
  • Customer Support Specialist

Components of Prompt Engineering

  • Real-Time Problem Identification
  • Efficient Troubleshooting
  • Rapid Decision-Making
  • Collaborative Teamwork
  • Continuous Learning
  • Communication Skills
  • Adaptability
  • Resource Management
  • Testing and Validation
  • Documentation
  • Monitoring Tools
  • Security Awareness
  • Automation
  • Performance Optimization
  • Feedback Loop

What We Offer

24/7 Portal Access
Domain Expertise Trainers
Industrial Standard Course Structure
Job Oriented Programs
Recording Sessions
Assignments on Real time Scenarios
Job Support
Resume Preparations
Internships
Job Assistance
Working on Real Time Projects
Course Completion Certifications

We Provide Higher Quality Services

AND YOU’LL GET SOLUTIONS FOR EVERYTHING
Best Artificial Intelligence Training in Hyderabad with 100% placement Assistance. Learn Data Science with Python, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Statistics and Tableau.

Branches

KPHBSR NagarSecunderabad
Phone: +91 9390023585
Email: info@learnai.co.in

Head Office

Address: 1st Floor, Rajadhani Theatre Complex, Pillar Number 1546, above Siri Mobiles, Dilsukhnagar, Hyderabad, Telangana 500060.
Phone: +91 9390023585
Email: info@learnai.co.in
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