Deep Learning

Training Milestones

Book Counselling Session

Module 01:- ML as a Framework

    Essentials Covered:-

  1. 1. Understanding Machine Learning (ML) as a Framework.
  2. 2. Exploring Various Variants of ML.
  3. 3. Supervised Learning.
  4. 4. Unsupervised Learning.
  5. 5. Semi-Supervised Learning.
  6. 6. Introduction to Deep Learning as a Sub-field of ML.
Module 02:- Supervised Machine Learning Tasks in Supervised Learning

    Essentials Covered:-

  1. 1. Introduction to Linear Regression.
  2. 2. Principles and Applications in Predictive Modelling.
  3. 3. Logistic Regression
  4. 4. Understanding Binary Classification Problems.
  5. 5. Model Architecture: Components of ML Model Architecture.
  6. 6. Cost/Loss Function
  7. 7. Error Metrics
  8. 8. Optimizer
  9. 9. Overview of Decision Trees, Random Forests, and Support Vector Machines (SVMs) in Supervised Learning
Module 3:- Neural Networks as Computational Graphs

    Essentials Covered:-

  1. 1. Deep Dive into Feedforward Neural Networks (FFNNs).
  2. 2. Architecture and Key Concepts
  3. 3. Implementing Neural Networks from Scratch in Python.
  4. 4. Using NumPy, TensorFlow, PyTorch, and JAX
Module 04:- RNNS, LSTMs, and Transformers

    Essentials Covered:-

  1. 1. Introduction to Recurrent Neural Networks (RNNs) and their applications.
  2. 2. Understanding Long Short-Term Memory (LSTM) Networks for Handling Temporal Data.
  3. 3. Overview of Transformer Architecture and its Role in NLP
Module 05:- Generative AI

    Essentials Covered:-

  1. 1. Exploring the Concept of Generative Adversarial Networks (GANs) and their applications.
  2. 2. Understanding Transformer Architecture and Diffusion Models.
Module 06:- Working with Text Data

    Essentials Covered:-

  1. ⁠Introduction to Vector Databases