Machine Learning

Eligibility: Students, Professionals who like to pursue career in Machine Learning, Data Science can opt for it.

Course Objectives: The Machine Learning certification course with DLithe offers hands-on training covering basics, supervised and unsupervised learning, paradigms, data, model and many more. The aspirants can learn and implement machine learning.

Duration: The course is comprised of Instructor-led 40 hours, with hands-on sessions.

Chapter 1: Introduction & Concepts

• • What is Machine Learning: Learning from Data
  • ML Paradigms: Supervised, Unsupervised, Reinforcement
  • Approach for building ML products: the process
  • Intuition for Classification (Paper & Pen)

Chapter 2: Model Building: Tree-based

• • Building a Decision Tree
  • Encoding data, Training & Test Split
  • Choosing Error Metrics: Standard vs. Custom
  • Model Evaluation Approach

Chapter 3: Model Validation & Selection

• • Overfitting & Underfitting, Generalisation, Learning Curves
  • Regularisation in Trees
  • Cross-Validation: Hold-out, K-fold
  • Hyper-Parameter Tuning: Grid Search

Chapter 4: Ensemble Models

• • Ensemble Models for Generalisation
  • Resampling & Bootstrap Data
  • Bagging Approach: Random Forest
  • Boosting Approach: Gradient Boosting

Chapter 5: Building Model: Linear

• • Linear Regression
  • Normalisation & Standardistion
  • L1 / L2 Regularisation in Linear Models
  • Logistic Regression (for Classification)

Chapter 6: Feature Engineering

• • Feature Creation Approaches
  • Scale Transformations
  • Feature Importance & Selection
  • Domain Knowledge & Art of Feature Engineering

Chapter 7: Build & Deploy ML Service

• • Concept of ML Service for Prediction
  • Pipelines and Model Serialisation
  • Rest API and design
  • Deploy your ML Service – localhost API

Chapter 8: Interpret ML Models

• • Concept & Why ML Interpretation
  • Types: Feature, Instance & Model level
  • Local Surrogate Models (LIME), Shapely Values
  • Model Visualisation

Chapter 9: Dimensionality Reduction

• • The curse of dimensionality
  • Matrix Factorisation approaches
  • Usage for Unsupervised Learning: Similarity
  • Feature creation for Supervised Learning

Chapter 10: Clustering

• • Concept & Challenges of Clustering
  • Distance-based approaches e.g. K-Means
  • Measuring clustering performance
  • Alternate approaches – Neighbour, Manifold (theory)