Advanced Machine Learning: From Linear Models to Neural Networks

Executive Overview

The field of Machine Learning (ML) has evolved beyond basic algorithms to encompass sophisticated models that can learn complex relationships from vast datasets. This 7-day corporate training program provides a comprehensive journey from traditional ML algorithms to advanced neural networks, bridging theory with hands-on implementation. Participants will learn the mathematical foundations, optimization techniques, and model design strategies that enable state-of-the-art performance in business applications. The course also focuses on interpretability, automation, and scalable ML deployment in enterprise environments.

Objectives of the Training

Master the principles behind advanced machine learning models and their optimization.
Learn to apply and tune algorithms such as ensemble methods, gradient boosting, and SVMs.
Understand the transition from traditional ML to deep neural networks.
Develop expertise in model evaluation, interpretability, and explainability.
Implement ML workflows for real-world enterprise use cases using Python and Scikit-learn.
Explore automation and deployment techniques using ML pipelines and cloud environments.

Prerequisites

Prior experience with Python programming and data analysis.
Working knowledge of basic ML concepts such as regression, classification, and clustering.
Familiarity with libraries like NumPy, Pandas, Matplotlib, and Scikit-learn.
Understanding of basic linear algebra, calculus, and probability theory.

What You Will Learn

Advanced supervised and unsupervised learning algorithms.
Ensemble learning, gradient boosting, and kernel-based methods.
Neural network foundations and optimization techniques.
Practical implementation of ML pipelines and hyperparameter tuning.
Model evaluation and interpretability (SHAP, LIME, feature importance).
Deploying ML systems in scalable, production-grade environments.

Target Audience

This training is designed for Data Scientists, ML Engineers, Quantitative Analysts, and AI Practitioners who wish to strengthen their expertise in advanced ML methodologies. It is also suitable for Technical Managers and Team Leads responsible for architecting AI solutions or optimizing machine learning workflows in enterprise contexts.

Detailed 7-Day Curriculum

Day 1 – Advanced ML Fundamentals & Linear Models (6 Hours)

Session 1: Revisiting Regression and Classification Fundamentals.
Session 2: Regularization Techniques – Ridge, Lasso, and ElasticNet.
Session 3: Gradient Descent, Loss Functions, and Optimization in ML.
Hands-on: Implementing Linear and Logistic Regression with Scikit-learn.

Day 2 – Tree-Based Models and Ensemble Learning (6 Hours)

Session 1: Decision Trees and their Variants (CART, ID3, C4.5).
Session 2: Ensemble Learning Concepts – Bagging, Boosting, and Stacking.
Session 3: Random Forests and Gradient Boosting Machines (GBMs).
Hands-on: Model Comparison and Hyperparameter Tuning using GridSearchCV.

Day 3 – Gradient Boosting and XGBoost (6 Hours)

Session 1: Understanding Gradient Boosting Mechanisms.
Session 2: XGBoost, LightGBM, and CatBoost Frameworks.
Session 3: Model Optimization and Feature Importance Interpretation.
Case Study: Predictive Analytics for Customer Retention using XGBoost.

Day 4 – Support Vector Machines and Kernel Methods (6 Hours)

Session 1: SVM Fundamentals and the Concept of Margins.
Session 2: Kernel Trick – Polynomial, RBF, and Sigmoid Kernels.
Session 3: Model Selection and Performance Evaluation.
Hands-on: Implementing SVMs for Classification of Financial Risk.

Day 5 – Introduction to Neural Networks (6 Hours)

Session 1: Neural Network Fundamentals – Perceptrons and Activation Functions.
Session 2: Backpropagation and Weight Optimization.
Session 3: Transition from Traditional ML to Deep Learning Architectures.
Hands-on: Building a Simple Neural Network from Scratch.

Day 6 – Model Evaluation, Interpretability & Automation (6 Hours)

Session 1: Advanced Model Evaluation Techniques (ROC, AUC, Precision-Recall).
Session 2: Model Interpretability Tools – SHAP, LIME, and Partial Dependence Plots.
Session 3: Automating ML Workflows using Pipelines and AutoML.
Workshop: Building Explainable ML Models for Regulatory Compliance.

Day 7 – Capstone Project & Enterprise Deployment (6 Hours)

Session 1: Designing an End-to-End ML Solution for a Business Challenge.
Session 2: Capstone Project Execution and Review.
Session 3: Deploying ML Models via APIs and Cloud Services (AWS, Azure).
Group Discussion: Trends in Automated Machine Learning (AutoML) and MLOps.

Capstone Project

The capstone project will require participants to implement an end-to-end ML pipeline on a real dataset. Participants may choose a project such as churn prediction, fraud detection, or demand forecasting. They will apply advanced ML techniques—ensemble methods, SVMs, and neural networks—to build, optimize, and interpret models while documenting their process and presenting insights.

Evaluation & Certification Framework

Completion of daily exercises and case studies (30%).
Participation in technical discussions and workshops (20%).
Final capstone project submission and presentation (50%).

Successful candidates will earn the ‘Advanced Machine Learning Specialist’ certification from Anika Technologies.

Future Trends in Advanced Machine Learning

As data grows in scale and complexity, the focus of machine learning is shifting toward automation, interpretability, and integration with deep learning. AutoML systems, federated learning, and reinforcement learning are paving the way for intelligent, self-optimizing systems. The convergence of ML with edge computing, generative AI, and explainable AI will define the next phase of enterprise innovation.