Machine Learning For Researchers

Name: Machine Learning For Researchers
Rating: 4.3 (134 reviews)

Learn Research Methods & Machine Learning

Created byAcademy of Computing & Artificial Intelligence, Kaneeka Vidanage

Last updated 6/2020

English

English [Auto],Korean [Auto],

What you'll learn

Introduction to Research
Finding a research problem
Finalzing your objectives
Research Methodology
Introduction to Machine Learning:- What is Machine Learning ?,
Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google Collabs
Artificial Neural networks [Theory and practical sessions - hands-on sessions]
Support Vector Machines

Course content

2 sections • 13 lectures • 5h 35m total length

Getting Ready31:25
Identify realistic research problems by assessing existing knowledge, gaps, and domain relevance; learn to choose between basic and applied research, evaluate feasibility, scope, ethics, and mentor fit.
Finding a research problem32:04
Refine your research problem with your supervisor, narrow the scope, distinguish research from implementation, and outline a publishable contribution using current journals and conferences aligned to your domain.
Finalzing your objectives29:50
Define and finalize research problem in machine learning by narrowing scope with a lens and drafting a problem statement covering background, anchoring, general and specific problems, purpose, and smart objectives.
Positivism31:22
Explore positivism as a single reality, quantitative research approach, guiding literature reviews, hypothesis testing, and survey data collection while emphasizing knowledge gaps, contributions, and rigorous operationalization.
Interpretivism19:42

Downloading and Setting up Anaconda for Machine Learning4:13
Download and install Anaconda to set up a ready-to-use machine learning environment. Launch Jupyter or Spyder to run beginner-friendly machine learning examples and practice experimental setup.
Introduction to Google Colabs7:25
Support Vector Machines - Concept15:39
Explore how support vector machines use hyperplanes to separate data, compare kernel options, and tune gamma and regularization to maximize margin for clear class separation.
Support Vector Machines - Hands - On with Google Collabs48:38
Perform a hands-on SVM classifier on the iris dataset in google collabs, from data loading with pandas to train-test split, model training, and accuracy evaluation.
Trees Concept19:41
Explore decision trees as a classifier driven by entropy and information gain, compare with other methods, and introduce concepts like gini index and random forest.
Trees Hands On....25:48
Explore practical decision tree implementation in Google Colab, including data preprocessing, independent and dependent variables, label encoding, and training a classifier on a pandas dataset.
Random Forest - Hands - On with Google Collabs39:56
Explore a hands-on random forest session in Google Colab, building an ensemble of trees to classify handwritten digits with training, testing, accuracy metrics, and a confusion matrix heat map.
KERAS Tutorial : Developing an Artificial Neural Network in Python [Step by Step29:35
Develop a Keras-based neural network in Python, building a sequential model with eight inputs, relu hidden layers, a sigmoid output, for diabetes binary classification, trained with sgd.

Requirements

Internet connection & Basic python programming knowledge

Description

Indicative Module Content

Introduction to Research - This session will help you to start the wonderful journey of research. Research is useful for finding new opportunities , new discoveries and higher studies

Finding a research problem - Finding a research problem is the most important aspect of any research project . (Undergraduate, Postgraduate etc..)

Finalizing your objectives-

Research Methodology

Positivism

Interpretivism

Introduction to Machine Learning:- What is Machine Learning ?, -> in this session we will get an overview of machine learning

Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google Collabs -

(All the instructions will be provided to setup the environment successfully.)

Artificial Neural networks [Theory and practical sessions - hands-on sessions] - This practical hands-on session will guide you to create a comprehensive ANN project

Machine Learning Techniques : Support Vector Machines - Lecture & Hands - On with Google Collabs ,

Trees and Random Forest - Lecture & Hands-On with Google Collabs,

Who this course is for:

Beginners for Machine Learning & Research

Machine Learning For Researchers

What you'll learn

Explore related topics

Course content

Introduction to Research5 lectures • 2hr 24min

Introduction to Machine Learning8 lectures • 3hr 11min

Requirements

Description

Who this course is for: