Croppinn Pre-Employment Training Program (CPET)

Where first job becomes lasting career

Croppinn Pre-Employment Training Program (CPET) allows limited candidates to apply for training on specific roles like AI Engineer, Data Engineer, Full Stack Engineer and Content Creator at Croppinn.

Candidates must fill and submit the CPET Application Form. The applicants will then receive a confirmation email. Upon confirmation, the slots are allotted to CPET applicants based on the order of their applications and the availability. About 100+ AI DS & CC job openings are coming up from us with in next few months.

Training: Croppinn Pre-Employment Training Program (CPET)

Courses: AIMP, DSMP, PEMP, FSDMP, ATMP, CCMP

Roles: AI Engineer, Data Engineer, Prompt Engineer, Full Stack Developer, Automation Test Engineer, Content Creator

Mode: Remote (Multiple Instructors based), Additional E-Learning access.

Duration: 6 Months starting from 8th Apr 2024 / 15th Apr 2024 / 6th May 2024

Who can Join: Jobseekers / Students

Application: CPET Application Form

If you have any inquiries, feel free to contact hr@croppinn.com

Artificial Intelligence Mastery Program (AIMP)

Beginner Level:

Duration: 100 hours

Prerequisites:

• Basic understanding of mathematics (algebra, calculus, probability)

• Basic programming skills (Python)

Hourly Schedule:

1. Introduction to AI and ML (2 hours)

   • Understanding the concepts of AI and ML

   • Importance and applications of AI and ML

2. Python Basics for AI and ML (8 hours)

   • Variables and Data Types

   • Control Flow and Loops

   • Functions and Modules

   • Data Structures (Lists, Tuples, Dictionaries)

   • File Handling

3. Mathematics for Machine Learning (20 hours)

   • Linear Algebra

   • Calculus

   • Probability and Statistics

4. Introduction to Machine Learning (10 hours)

   • Types of Machine Learning

   • Supervised Learning

   • Unsupervised Learning

   • Reinforcement Learning

5. Data Preprocessing (10 hours)

   • Data Cleaning

   • Data Transformation

   • Feature Scaling

   • Handling Missing Values

6. Supervised Learning Algorithms (20 hours)

   • Linear Regression

   • Logistic Regression

   • Decision Trees

   • Random Forests

   • Support Vector Machines

7. Unsupervised Learning Algorithms (10 hours)

   • K-Means Clustering

   • Hierarchical Clustering

   • Principal Component Analysis (PCA)

8. Model Evaluation and Validation (10 hours)

   • Cross-Validation

   • Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

   • Hyperparameter Tuning

9. Introduction to Deep Learning (8 hours)

   • Neural Networks

   • Activation Functions

   • Backpropagation

10. Hands-on Projects and Case Studies (10 hours)

    • Implementation of ML algorithms on real datasets

    • Building simple neural networks

Additional Skills Required:

• Critical thinking and problem-solving skills

• Strong analytical skills

• Familiarity with basic data manipulation libraries in Python (e.g., NumPy, Pandas)

• Ability to work in a team environment.

Advanced Level:

Duration: 200 hours

Prerequisites:

• Completion of Beginner Level or equivalent knowledge

Hourly Schedule:

1. Advanced Deep Learning (20 hours)

   • Convolutional Neural Networks (CNNs)

   • Recurrent Neural Networks (RNNs)

   • Transfer Learning

   • Generative Adversarial Networks (GANs)

2. Natural Language Processing (NLP) (20 hours)

   • Text Preprocessing

   • Word Embeddings (Word2Vec, GloVe)

   • Named Entity Recognition

   • Sentiment Analysis

3. Reinforcement Learning (20 hours)

   • Markov Decision Processes

   • Q-Learning

   • Deep Q-Networks (DQNs)

   • Policy Gradient Methods

4. Time Series Analysis (10 hours)

   • ARIMA Models

   • Exponential Smoothing Methods

   • Prophet Library

5. Advanced Model Evaluation and Deployment (20 hours)

   • Advanced Evaluation Metrics

   • Model Deployment (Flask, Docker)

   • Model Interpretability

6. Ethics in AI (10 hours)

   • Bias and Fairness

   • Privacy and Security

   • Ethical Considerations in AI Development

7. Advanced Projects and Case Studies (60 hours)

   • Large-scale data projects

   • Real-world AI applications

Additional Skills Required:

• Advanced programming skills in Python

• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)

• Ability to understand and implement complex algorithms.

• Strong communication skills for presenting findings and discussing concepts with peers.

Expert Level:

Duration: 300 hours

Prerequisites:

• Completion of Advanced Level or equivalent knowledge

Hourly Schedule:

1. Advanced Deep Learning Architectures (40 hours)

   • Transformer Networks

   • Variational Autoencoders (VAEs)

   • Deep Reinforcement Learning

2. Advanced Topics in NLP (30 hours)

   • Attention Mechanisms

   • BERT and Transformer Models

   • Sequence-to-Sequence Models

3. Advanced Topics in Computer Vision (30 hours)

   • Object Detection

   • Image Segmentation

   • Instance Segmentation

4. Advanced Reinforcement Learning Techniques (30 hours)

   • Model-Based Reinforcement Learning

   • Multi-Agent Reinforcement Learning

   • Meta-Learning

5. AI for Healthcare (20 hours)

   • Medical Image Analysis

   • Predictive Modelling in Healthcare

   • Electronic Health Records

6. AI for Finance (20 hours)

   • Algorithmic Trading

   • Risk Management

   • Fraud Detection

7. AI for Robotics (20 hours)

   • Robot Perception

   • Motion Planning

   • Robot Learning

8. Research Methods in AI (20 hours)

   • Literature Review

   • Experimental Design

   • Publication Process

9. Capstone Project (100 hours)

   • Independent research project or thesis under the guidance of a mentor

   • Implementation of a novel AI solution

Additional Skills Required:

• Proficiency in advanced mathematics and statistics

• Expertise in deep learning frameworks and libraries

• Ability to conduct independent research and experiment with new techniques.

• Strong problem-solving and critical thinking abilities

Data Science Mastery Program (DSMP)

Beginner Level:

Duration: 150 hours

Prerequisites:

• Basic understanding of mathematics (algebra, calculus, statistics)

• Proficiency in at least one programming language (preferably Python)

• Familiarity with data manipulation libraries (e.g., Pandas)

• Basic knowledge of machine learning concepts

Hourly Schedule:

1. Introduction to Data Science (10 hours)

   • Overview of Data Science

   • Importance and Applications of Data Science

   • Data Science Workflow

2. Python Fundamentals for Data Science (20 hours)

   • Basics of Python programming

   • Data structures and functions

   • Libraries for data manipulation (NumPy, Pandas)

3. Data Wrangling and Cleaning (20 hours)

   • Data Cleaning Techniques

   • Data Transformation and Feature Engineering

   • Exploratory Data Analysis (EDA)

4. Statistical Analysis for Data Science (30 hours)

   • Descriptive Statistics

   • Probability Distributions

   • Inferential Statistics

   • Hypothesis Testing

5. Introduction to Machine Learning (20 hours)

   • Basics of Supervised and Unsupervised Learning

   • Linear Regression

   • Classification Algorithms (Logistic Regression, Decision Trees)

   • Model Evaluation and Validation

6. Data Visualization (20 hours)

   • Visualization libraries (Matplotlib, Seaborn)

   • Creating effective visualizations

   • Dashboarding tools (e.g., Tableau, Power BI)

Additional Skills Required:

• Strong problem-solving and analytical skills

• Attention to detail for data cleaning and preprocessing

• Basic understanding of algorithms and their applications

Advanced Level:

Duration: 300 hours

Prerequisites:

• Completion of Beginner Level or equivalent knowledge

Hourly Schedule:

1. Advanced Machine Learning (40 hours)

   • Ensemble Learning (Random Forests, Gradient Boosting)

   • Support Vector Machines (SVM)

   • Neural Networks and Deep Learning

   • Time Series Analysis and Forecasting

   • Natural Language Processing (NLP) Basics

2. Big Data Analytics (30 hours)

   • Introduction to Big Data and Hadoop

   • Hadoop Ecosystem (Hive, Pig, HBase)

   • Apache Spark for Big Data Processing

   • Introduction to NoSQL Databases (MongoDB, Cassandra)

3. Advanced Statistical Analysis (40 hours)

   • Advanced Probability Distributions

   • Multivariate Analysis

   • Bayesian Statistics

   • Non-parametric Statistics

4. Feature Engineering and Selection (20 hours)

   • Feature Engineering Techniques

   • Dimensionality Reduction (PCA, LDA)

   • Feature Importance Methods

5. Model Optimization and Tuning (30 hours)

   • Hyperparameter Tuning

   • Cross-validation Techniques

   • Model Selection and Ensemble Methods

6. Advanced Data Visualization (30 hours)

   • Interactive Visualization Tools (Plotly, Bokeh)

   • Geospatial Data Visualization

   • Advanced Plotting Techniques

Additional Skills Required:

• Proficiency in machine learning algorithms and their implementation

• Understanding of distributed computing and big data technologies

• Ability to optimize models for performance and scalability

Expert Level:

Duration: 500 hours

Prerequisites:

• Completion of Advanced Level or equivalent knowledge

Hourly Schedule:

1. Deep Learning and Neural Networks (60 hours)

   • Deep Learning Architectures (CNNs, RNNs, Transformers)

   • Advanced Activation Functions

   • Transfer Learning and Fine-tuning

2. Reinforcement Learning (40 hours)

   • Markov Decision Processes

   • Q-Learning and SARSA

   • Deep Reinforcement Learning Algorithms

3. Advanced NLP and Text Mining (40 hours)

   • Word Embeddings (Word2Vec, GloVe)

   • Sequence-to-Sequence Models

   • Transformer Models (BERT, GPT)

4. Time Series Forecasting and Analysis (30 hours)

   • Advanced Time Series Models (ARIMA, SARIMA)

   • Long Short-Term Memory (LSTM) Networks

   • Causal Inference and Granger Causality

5. Advanced Topics in Big Data (40 hours)

   • Stream Processing with Apache Kafka

   • Advanced Spark Programming (GraphX, MLlib)

   • Distributed Deep Learning with TensorFlow on Spark

6. Model Deployment and Productionization (40 hours)

   • Containerization with Docker

   • Model Serving with Kubernetes

   • MLOps and Continuous Integration/Continuous Deployment (CI/CD) Pipelines

7. Ethical and Responsible AI (30 hours)

   • Bias and Fairness in AI

   • Explainable AI (XAI)

   • Privacy and Security Considerations

Additional Skills Required:

• In-depth knowledge of advanced machine learning and deep learning concepts

• Experience with distributed computing frameworks and cloud platforms

• Ability to deploy and manage machine learning models in production environments

Prompt Engineering Mastery Program (PEMP)

Beginner Level:

Duration: 50 hours

Prerequisites:

• Proficiency in at least one programming language (Python, JavaScript, etc.)

• Familiarity with basic command-line operations

• Understanding of data structures and algorithms

• Basic knowledge of machine learning concepts

Hourly Schedule:

1. Introduction to Prompt Engineering (10 hours)

   • Overview of Prompt Engineering

   • Understanding Prompt Engineering in Natural Language Processing (NLP)

   • Importance of Prompt Design in Model Fine-tuning

   • Introduction to GPT and Similar Models

2. Fundamentals of GPT (10 hours)

   • Understanding GPT Architecture

   • Pretrained GPT Models Overview (GPT-2, GPT-3)

   • Fine-tuning Process Overview

   • Introduction to Hugging Face Transformers Library

3. Hands-on Practice with Prompt Design (10 hours)

   • Creating Simple Prompts for Text Generation Tasks

   • Experimenting with Prompt Variations

   • Exploring Prompt Engineering Strategies for Different Tasks (Text Generation, Translation, Question Answering)

4. Evaluation and Optimization (10 hours)

   • Metrics for Evaluating Prompt Performance

   • Fine-tuning Parameters and Hyperparameter Optimization

   • Debugging and Troubleshooting Prompt-Model Interactions

5. Project Work (10 hours)

   • Guided Project: Fine-tune GPT Model for a Specific Task

   • Presenting and Discussing Project Results

Advanced Level:

Duration: 100 hours

Prerequisites:

• Completion of Beginner Level or equivalent knowledge

Hourly Schedule:

1. Advanced Prompt Engineering Techniques (10 hours)

   • Advanced Prompt Design Patterns

   • Multi-turn Prompt Engineering

   • Prompt Augmentation Strategies

2. Domain-specific Prompt Engineering (10 hours)

   • Adapting Prompts for Domain-specific Tasks (Medical, Legal, etc.)

   • Fine-tuning GPT Models for Specific Industries

3. Handling Large-scale Data and Models (10 hours)

   • Distributed Training Techniques

   • Efficient Data Handling for Large-scale Fine-tuning

   • Managing Memory and Computation Resources

4. Ethical and Responsible Prompt Engineering (10 hours)

   • Bias and Fairness in Prompt Design

   • Ethical Considerations in Fine-tuning Models

   • Mitigating Harmful Outputs

5. Project Work (10 hours)

   • Independent Project: Fine-tune GPT Model for a Domain-specific Task

   • Ethical and Responsible Implications Presentation

Expert Level:

Duration: 100 hours

Prerequisites:

• Completion of Advanced Level or equivalent knowledge

Hourly Schedule:

1. Advanced Model Interpretability (10 hours)

   • Interpreting Model Outputs

   • Probing Techniques for GPT Models

   • Visualizing Attention Mechanisms

2. Advanced Research Topics (10 hours)

   • Current Research Trends in Prompt Engineering

   • Innovations in Fine-tuning Approaches

   • Beyond GPT: Exploring Other Language Models

3. Scalability and Production Deployment (10 hours)

   • Productionizing Fine-tuned Models

   • Scaling Models for High-throughput Applications

   • Continuous Monitoring and Maintenance

4. Advanced NLP Techniques (10 hours)

   • Advanced Text Generation Strategies

   • Zero-shot and Few-shot Learning

   • Contextual Bandits for Adaptive Prompt Engineering

5. Capstone Project (10 hours)

   • Capstone Project: Solve a Real-world Problem using Prompt Engineering Techniques

   • Presentation and Discussion of Capstone Projects

Additional Skills Required:

• Critical thinking and problem-solving abilities

• Strong communication and presentation skills

• Ability to work independently and collaboratively

• Continuous learning and adaptation to emerging technologies and research

Full Stack Developer Mastery Program (FSDMP)

Beginner Level:

Duration: 100 hours

Prerequisites:

• Basic understanding of programming concepts

• Familiarity with HTML, CSS, and JavaScript

Hourly Schedule:

1. Introduction to Frontend Development with React (20 hours)

   • Introduction to React and JSX

   • Setting up a development environment with Create React App

   • Components, props, and state management

   • Handling events and forms in React

2. Backend Development with Node.js (20 hours)

   • Introduction to Node.js and asynchronous JavaScript

   • Building RESTful APIs with Express.js

   • Connecting to databases using SQL (e.g., MySQL)

   • User authentication and authorization

3. Introduction to Python for Algorithms (20 hours)

   • Introduction to Python syntax and data types

   • Basic data structures: lists, tuples, dictionaries

   • Control flow and functions in Python

   • Introduction to algorithmic problem-solving techniques

4. Relational Database Management with SQL (20 hours)

   • Introduction to relational databases and SQL

   • Querying databases using SELECT, INSERT, UPDATE, DELETE statements

   • Joins, subqueries, and aggregate functions

   • Indexes, transactions, and database normalization

5. Introduction to Azure DevOps (20 hours)

   • Introduction to Azure DevOps and version control with Git

   • Setting up CI/CD pipelines for React and Node.js applications

   • Automated testing and code quality checks

   • Deployment of applications using Azure DevOps

Advanced Level:

Duration: 100 hours

Prerequisites:

• Completion of Beginner level or equivalent experience

• Familiarity with React, Node.js, Python, SQL, and Azure DevOps

Hourly Schedule:

1. Advanced React Development (20 hours)

   • Advanced state management with Redux

   • React hooks and context API

   • Performance optimization techniques

   • Introduction to server-side rendering with Next.js

2. Advanced Node.js Development (20 hours)

   • Advanced asynchronous JavaScript concepts

   • Middleware development and error handling

   • Security best practices in Node.js applications

   • Introduction to microservices architecture

3. Advanced Python for Algorithms (20 hours)

   • Advanced data structures: sets, heaps, trees

   • Advanced algorithmic techniques: dynamic programming, greedy algorithms

   • Advanced graph algorithms and their implementations

   • Problem-solving exercises and algorithm analysis

4. Advanced Database Management (20 hours)

   • Advanced SQL techniques: window functions, common table expressions

   • Stored procedures, triggers, and views

   • Database optimization and performance tuning

   • Introduction to NoSQL databases (e.g., MongoDB)

5. Advanced Azure DevOps (20 hours)

   • Advanced CI/CD pipeline configurations

   • Infrastructure as Code (IaC) using Azure Resource Manager templates

   • Monitoring and logging with Azure Monitor and Azure Log Analytics

   • Advanced DevOps practices and continuous improvement strategies

Expert Level:

Duration: 100 hours

Prerequisites:

• Completion of Advanced level or equivalent experience

• Strong understanding of React, Node.js, Python, SQL, and Azure DevOps

Hourly Schedule:

1. Expert React Development (20 hours)

   • React performance optimization techniques

   • Advanced routing and navigation strategies

   • Advanced UI/UX concepts and design patterns

   • Integrating third-party libraries and APIs

2. Expert Node.js Development (20 hours)

   • Advanced debugging and troubleshooting techniques

   • Load balancing and scaling Node.js applications

   • Implementing security standards (e.g., OAuth, SSL/TLS)

   • Containerization with Docker and Kubernetes

3. Expert Python for Algorithms (20 hours)

   • Advanced algorithmic problem-solving strategies

   • Advanced graph algorithms: shortest paths, maximum flow

   • Advanced topics in computational complexity theory

   • Real-world problem-solving projects and case studies

4. Expert Database Management (20 hours)

   • Advanced database design principles and patterns

   • Advanced indexing strategies and query optimization techniques

   • Data warehousing and analytics with SQL

   • Introduction to Big Data technologies (e.g., Hadoop, Spark)

5. Expert Azure DevOps (20 hours)

   • Advanced DevOps automation using Azure DevOps YAML pipelines

   • Implementing infrastructure monitoring and alerting with Azure Monitor

   • Implementing security and compliance standards in DevOps processes

   • Architecting highly available and fault-tolerant cloud solutions

By the end of this comprehensive program, learners will have evolved into Masters of Full Stack Developers, equipped with advanced skills and practical experience in building robust and scalable web applications using modern technologies and best practices.

Automation Testing Mastery Program (ATMP)

Beginner Level:

Duration: 100 hours

Prerequisites:

• Basic understanding of software development life cycle (SDLC) and testing concepts.

• Familiarity with at least one programming language (e.g., Java, Python).

• Basic knowledge of databases and SQL.

• Understanding of web technologies (HTML, CSS, JavaScript).

Hourly Schedule:

1. Introduction to Software Testing (10 hours)

   • Overview of software testing

   • Importance of testing in SDLC

   • Types of testing: Functional, Non-functional, Regression, etc.

   • Test case design techniques: Equivalence partitioning, Boundary value analysis, Decision tables, etc.

   • Introduction to Test Management tools (e.g., Jira, TestRail)

2. Manual Testing Fundamentals (20 hours)

   • Test planning and documentation

   • Test case writing and execution

   • Defect tracking and reporting

   • Test data preparation

   • Exploratory testing techniques

   • Introduction to Black box and White box testing

3. Introduction to Automation Testing (20 hours)

   • Understanding automation testing

   • Introduction to Selenium WebDriver

   • Setting up Selenium WebDriver environment

   • Writing basic automation scripts in Selenium WebDriver

   • Handling different elements on web pages

   • Executing test scripts and reporting results

4. Introduction to ETL Testing (15 hours)

   • Understanding ETL process

   • ETL testing basics

   • Validating data transformations

   • Performing data integrity checks

   • ETL testing tools and techniques

5. Test Automation Frameworks (15 hours)

   • Introduction to automation frameworks

   • Data-driven framework

   • Keyword-driven framework

   • Hybrid framework

   • Best practices in framework development

Advanced Level:

Duration: 150 hours

Prerequisites:

• Completion of Beginner Level or equivalent knowledge

• Proficiency in at least one programming language (e.g., Java, Python).

• Hands-on experience with Selenium WebDriver and TestNG.

• Understanding of advanced testing concepts such as CI/CD, BDD, and TDD.

• Familiarity with version control systems (e.g., Git).

Hourly Schedule:

1. Advanced Selenium Concepts (20 hours)

   • Advanced locators and techniques

   • Handling dynamic web elements

   • Working with frames and windows

   • Handling alerts, pop-ups, and multiple tabs

   • Synchronization in Selenium

   • Cross-browser testing with Selenium Grid

2. Test Automation Best Practices (20 hours)

   • Designing robust test scripts

   • Handling exceptions and errors

   • Logging and reporting in automation testing

   • Code review and maintenance

   • Test data management in automation

   • Test script optimization techniques

3. Test Automation using Selenium with Java/Python (30 hours)

   • Advanced scripting techniques with Java/Python

   • TestNG framework for test automation

   • Parameterization in TestNG

   • TestNG listeners and annotations

   • Integrating automation tests with CI/CD pipelines (e.g., Jenkins)

4. Performance Testing Fundamentals (25 hours)

   • Introduction to performance testing

   • Types of performance testing: Load, Stress, Spike, Endurance

   • Performance testing tools (e.g., JMeter)

   • Load test planning and execution

   • Analysing performance test results

   • Performance optimization techniques

5. Advanced ETL Testing Techniques (30 hours)

   • Data completeness and accuracy testing

   • ETL performance testing

   • Data reconciliation techniques

   • ETL metadata testing

   • Advanced SQL queries for ETL testing

   • ETL automation using tools like Informatica or Talend

Expert Level:

Duration: 200 hours

Prerequisites:

• Completion of Advanced Level or equivalent knowledge

• Extensive experience in automation testing using Selenium and other tools/frameworks.

• In-depth understanding of software development methodologies (Agile, Scrum, etc.).

• Proficiency in performance testing tools like LoadRunner or Gatling.

• Strong knowledge of database concepts and SQL optimization techniques.

• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).

Hourly Schedule:

1. Advanced Test Automation Frameworks (25 hours)

   • Behavior-driven development (BDD) with Cucumber

   • Page Object Model (POM) design pattern

   • Hybrid automation framework design and implementation

   • Integrating test automation with DevOps practices

2. Continuous Testing and DevOps (30 hours)

   • Understanding DevOps principles

   • Continuous Integration/Continuous Deployment (CI/CD) pipeline setup

   • Automated deployment strategies

   • Containerization and orchestration with Docker and Kubernetes

   • Infrastructure as code (IaC) concepts

   • Test environment setup and management in DevOps

3. Advanced Performance Testing (30 hours)

   • Scripting complex scenarios in performance testing tools

   • Analyzing performance metrics and bottlenecks

   • Performance tuning strategies

   • Cloud-based performance testing

   • API performance testing using tools like Postman or RestAssured

4. Security Testing (25 hours)

   • Introduction to security testing

   • Common security vulnerabilities

   • OWASP top 10

   • Security testing tools (e.g., Burp Suite, OWASP ZAP)

   • Secure coding practices

   • Security testing in CI/CD pipelines

5. Advanced Topics and Emerging Trends (40 hours)

   • Test automation for mobile applications

   • Microservices testing strategies

   • AI/ML in testing

   • Test data management in complex environments

   • IoT testing

Note: The hours mentioned are approximate and can vary based on the learning pace and depth of understanding required by individual learners. Additionally, practical exercises, quizzes, and assessments should be integrated into each section to reinforce learning outcomes.