Category: Comprehensive AI ML

Phase 1 – Building Blocks

SubjectCore Concepts to ApplyProject/Application Use Case (E-commerce & Fintech)
Mathematical Foundations for ML (AIMLZC416)Vector algebra, Gradient descent, & PCA.ML System Optimization: Using PCA for dimensionality reduction to optimize the performance of the ClickHouse-powered analytics dashboard you built at xyz.
Introduction to Statistical Methods (AIMLZC418)Bayes Theorem, Hypothesis Testing, & Time series analysis.Inventory Forecasting: Applying ARIMA/SARIMA time series models to your SKU-level inventory API at abc to predict stock-outs with higher statistical significance.
Machine Learning (AIMLZG565)Supervised/Unsupervised Learning & Ensemble methodsIntelligent Fraud Detection: Utilizing Random Forest or SVMs to enhance the real-time reconciliation system designed for upi payments at xyz.
Deep Neural Networks (AIMLZG511)Backpropagation, CNNs, & Transformers.B2B Communication Intelligence: Using Transformer-based NLP models to extract intent and automate tracking from the Order Notes API you designed.
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Comprehensive AI ML

Phase 1

  1. Mathematical Foundations for Machine Learning (AIMLZC416)
    • Focuses on linear algebra (vectors, matrices, eigenvalues), multivariate calculus (Jacobian, Hessian), and optimization techniques like Gradient Descent and PCA.
  2. Introduction to Statistical Methods (AIMLZC418)
    • Covers probability concepts, Bayes Theorem, hypothesis testing, and time series analysis (ARIMA, SARIMA).
  3. Machine Learning (AIMLZG565)
    • An introduction to various kinds of learning (supervised, unsupervised), model selection, Bayesian learning, and non-linear models like Decision Trees and Support Vector Machines (SVM).
  4. Deep Neural Networks (AIMLZG511)
    • Covers the approximation properties of neural networks, backpropagation, CNNs, RNNs, Transformers, and applications in time series modeling.

Phase 2

Phase 3

Phase 4 – Hands on