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)
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