Training and Placement in Gen AI

at  Triunity Software

We are seeking candidates for Applied Machine Learning and Generative AI

APPLIED ML AND GEN AI BOOT CAMP CANDIDATE REQUIREMENTS:

1. Basic Oops
2. Data Structures
3. Advanced Math- Statistics, Algebra,
4. Excel for data manipulation and visualization
5. Database and SQL
6. Computer Science/Engineering Background

Foundational Knowledge (Prerequisite): 2 weeks

• Basic Python - Variables, Data Types, Loops, Conditional Statements, functions, classes, file handling, exception handling, etc.
• Mathematics & Statistics:
• Linear Algebra
• Descriptive Statistics - Measure of central tendency (Mean, Median, Mode), Measure of dispersion (variance, standard deviation)
• Inferential Statistics - Hypothesis testing, correlation, covariance, Z-test, t-test, ANOVA test, etc.
• Probability - Central limit theorem, Probability distribution, bayes theorem etc.

Data Analysis and Preparation (Exploratory Data Analysis): 1 week

• Data Handling and Manipulation using Numpy, Pandas, Scipy.
• Data Visualization using Matplotlib, Seaborn, Google Data Studio
• Feature engineering like imputing null values, handling outliers, scaling data.

Project: Data Analysis of any business use case with complete visualization and conclusion. E.g. Uber case analysis.Machine Learning: 2 weeks

• Introduction to frameworks like Scikit-Learn.
• Supervised Learning
• Introduction to both Regression and Classification problems.
• Train models using algorithms like Linear regression, logistic regression, Decision tree, random forest, SVC, KNN, etc.
• Evaluating models using metrics like RMSE, MAE, MSE, R2, accuracy, precision, recall, confusion matrix, F1-score etc.
• Unsupervised Learning
• Introduction to Clustering and Dimensionality Reduction problems.
• Learn unsupervised algorithms like K-Means, PCA, LDA, etc.
• Performance metrics like Elbow method, Silhouette Coefficient

Projects:

• Regression - use cases like house price prediction, etc.
• Classification - use cases like email spam or not, etc.
• Unsupervised - use cases like anomaly detection, etc.

Deep Learning: 1 week

• Introduction to frameworks like Tensorflow, Keras for Deep Learning.
• What are Neural Networks and how do they function as the core of deep learning?

Django (Framework for API integrations): 1 week

• Django Basics like creating projects, django views, mapping urls.
• Django Models to perform CRUD operations.
• Database operations

Project: Creating REST API to perform all CRUD operations with MySQL Database.Generative AI: 2 week

• Prompt Engineering
• Data Privacy - Context, Domain
• Best Practices- Token and Request optimization, Data Privacy considerations
• Tools - ChatGPT, Vertex AI, Dall-E2, GitHub Copilot, etc.

Project: Generate Job Description, Policy generation, etc.MLOps: 1 week

• Model Deployment using Cloud based platforms like GCP, Azure, etc.
• Testing Models and Data Pipelines
• ML Pipelines and ML workflows.
• Best Practices- cloud cost, Optimization of models
• GCP/Azure creating and deploying models, configuring VMs /GPU,
• Project : Install ML solution to GCP/Azure and update test data and rerun models

Elective Skills: 1 week

• Natural Language Processing: Dealing text data using NLTK, spacy framework. Introduction to algorithms like Lemmatization, stemming, NER, Word2Vec, etc.
• Computer Vision: Dealing with image data using OpenCV, PIL.

Capstone Project: 2 week

• Implement all the module learning and knowledge in a project like career coach, chatbot system, etc.

OutcomeCertification: AZURE AI Fundamentals,Qualified for the roles of : ML Engineer, Data Engineer,
Please contact to enroll @ 732 - 837 - 024

Please refer the Job description for details