Training and Placement in Gen AI Basking Ridge

Training and Placement in Gen AI

Full Time • Basking Ridge
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 - 0242




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