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EIEIO / ARTIFICIAL INTELLIGENCE

Machine Learning Intermediate Level

Course outline

8 days
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Duration

8 days

Course Overview

The "Overview of Machine Learning" course introduces participants to the fundamental concepts and techniques in machine learning. Covering both supervised and unsupervised learning methods, this course aims to equip learners with the knowledge required to understand and apply various machine learning algorithms effectively. Participants will also explore deep learning basics and practical applications in image processing and natural language processing.

Target Audience

This course is designed for:

  • Aspiring Data Scientists: Individuals looking to start a career in data science and machine learning.
  • Software Engineers: Professionals interested in expanding their skill set to include machine learning techniques.
  • Business Analysts: Those who want to leverage machine learning insights for data-driven decision-making.
  • Students: Learners in computer science or related fields who wish to gain foundational knowledge in machine learning.

Prerequisites

Participants should have:

  • Basic programming skills in Python, as it is commonly used in machine learning.
  • Familiarity with fundamental statistics and mathematics, particularly concepts related to probability and linear algebra.
  • No prior experience in machine learning is required, but a willingness to learn will enhance the experience.

Learning Objectives

By the end of this course, participants will be able to:

  • Differentiatebetween supervised and unsupervised learning techniques and understand their applications.
  • Implementsupervised learning algorithms for regression tasks, predicting continuous outcomes based on input features.
  • Applysupervised learning algorithms for classification tasks, categorizing data into discrete classes.
  • Exploreunsupervised learning techniques to discover patterns within datasets without labeled outputs.
  • Understandthe basics of deep learning, including the use of Convolutional Neural Networks (CNN) for image processing.
  • Learnabout Recurrent Neural Networks (RNN) and their applications in natural language processing (NLP).
  • Engagewith practical examples and case studies that illustrate the application of these techniques in real-world scenarios.

This structured curriculum ensures that learners not only grasp theoretical concepts but also develop practical skills applicable to various domains within machine learning.

Day 1

Overview of ML

  • Basic ML
  • Three Types of Machine Learning , Demos, ML LifeCycle
  • Use Cases of Machine Learning
  • ML Life Cycle

Create, train, evaluate & optimize the model

Model Evaluation Measures and Techniques

  • Accuracy , Sensitivity and ROC
  • R Square and Adjusted R-Square
  • KS

Overview of Sklearn package

Day 2

Unsupervised Learning with clustering

  • Basics of Clustering
  • K Means clustering
  • K means++
  • Distance Measures in Clustering

‒Euclidean, Manhattan

Hierarchical Clustering

Dendrogram

  • Inter Cluster Distance Models

Use Cases of Clustering

Day 3

Unsupervised Learning Continued

  • Association Rule Mining,
  • Recommendation Systems, and Collaborative Filtering
  • SVD and NMF based matrix factorization
  • Network Analysis

Python packages network

Day 4

Supervised Learning

  • Basics of Regression and Classification
  • Regression
  • Evaluation Measures for Regression
  • Linear Regression
  • Gradient Descent Concept
  • Regularization concept
  • Lasso Regresssion
  • Ridge Regression,
  • Gradient Descent Concept
  • Regression Trees

‒Entropy and Gini

Day 5

Supervised Learning - Regression

  • Lazy Regression Method KNN
  • Support Vector Regression

‒Kernels

  • Elastic-Net Regression

Classification

  • Logistic Regression
  • Metrics and Cross Validation

Day 6

Supervised Learning - Classification

  • Lazy Classification Method KNN
  • Support Vector Machine

‒Kernels

Ensemble Models

  • Random Forest
  • AdaBoost

Grid Search

  • Cross Validation
  • Pipelining of models

Day 7

Deep Learning

  • Intro to Neural Networks
  • Backpropagation
  • Basic Classification with Neural Networks

Basics of CNN

  • CNN Layers
  • CNN based Image Classification

MNIST classification with CNN

Day 8

NLP Basics

  • NLP Pipelines
  • Pre Processing for NLP
  • NLP for Named Entity Recognition
  • NLP based POS tagging

RNN

  • RNN basics
  • Vanishing Gradients Problem
  • LSTM Basics
  • LSTM based word prediction

MLOps

  • Basics
  • MLFlow based end to end ML Project Management

Programme scope, delivery arrangements and suitability are confirmed during enquiry.