Candidate Requirements:
- Basic Oops
- Data Structures
- Advanced Math- Statistics, Algebra,
- Excel for data manipulation and visualization
- Database and SQL
- 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,