• Post category:StudyBullet-13
  • Reading time:5 mins read


Fundamentals of machine learning to get you started in the field

What you will learn

Basic understanding of Machine Learning.

What machine learning is.

Why we use machine learning.

What are the types of the machine learning systems.

What are the challenges in machine learning.

Underfitting/Overfitting.

And a lot more.

Description

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to “self-learn” from training data and improve over time, without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions. In short, machine learning algorithms and models learn through experience.

In traditional programming, a computer engineer writes a series of directions that instruct a computer how to transform input data into a desired output.

Machine learning, on the other hand, is an automated process that enables machines to solve problems with little or no human input, and take actions based on past observations.


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In this course we are going to talk about the basics of the machine learning which will provide a strong foundation to the students who want to make a career in the field of data sciences and machine learning, we will go through each of the basic important thing that a beginner needs to know to get started with machine learning. We will be talking about what is the machine learning and why exactly we need to use the machine learning, then we will discuss the types of the machine learning system where we will be going in detail about all type and classification of the machine learning system. Then we will talk about the main problems that the data scientist face when they perform machine learning task or making a machine learning algorithm.

This course is introductory, do not expect high level coding and programming, this course is just to build a foundation on which a strong building shall stand.

English
language

Content

Introduction

Introduction
Intro to ML
Machine Learning Landscape
What is Machine Learning?
Why Machine Learning?
Machine Learning vs. Deep Learning vs. Neural Networks

Types of ML Systems

Types of ML system
Supervised ML
Unsupervised ML
Semi-Supervised ML
Reinforced ML
Batch Learning
Online Learning
Instance Based Learning
Modal Based Learning

Main Challenges of Machine Learning

Insufficient Quantity of data
Non Representative data
Poor Quality of data
Irrelevant features
Overfitting the training data
Underfitting the training data

Testing & Validation

Testing and validation
Hyperparameter Tuning & Modal selection
Hyperparameter tuning for machine learning models
Data mismatch
What are Data Mismatch and their potential solutions in Machine Learning?