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Applied ML and Gen AI BootCamp Princeton, NJ

Applied ML and Gen AI BootCamp

Full Time • Princeton, NJ
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.
Outcome
Certification: AZURE AI Fundamentals,
Qualified for the roles of : ML Engineer, Data Engineer,
Compensation: $70,000.00 - $100,000.00 per year




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