Applied ML and Gen AI BootCamp

Job Description

Candidate Requirements:

Basic Oops

Data Structures

Advanced Math- Statistics, Algebra,

Excel for data manipulation and visualization

Database and SQL

Computer Science/Engineering BackgroundFoundational 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 CoefficientProjects:

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 operationsProject: 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 modelsElective 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,