Machine Learning with Python Tutorial
Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention.
This tutorial will be useful for graduates, postgraduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner. This tutorial has been prepared for the students as well as professionals to ramp up quickly. This tutorial is a stepping stone to your Machine Learning journey.
The reader must have basic knowledge of artificial intelligence. He/she should also be aware of Python, NumPy, Scikit-learn, Scipy, Matplotlib. If you are new to any of these concepts, we recommend you to take up tutorials concerning these topics, before you dig further into this tutorial.
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Table of Contents
Chapter 1 - Machine Learning with Python - Basics
Chapter 2 - Ecosystem
Chapter 3 - Methods
Chapter 4 - Data Loading for ML Projects
Chapter 5 - ML - Understanding Data with Statistics
Chapter 6 - ML - Understanding Data with Visualization
Chapter 7 - ML - Preparing Data
Chapter 8 - ML- Data Feature Selection
Chapter 9 - Classification Algorithms - Introduction
Chapter 10 - ML- Logistic Regression
Chapter 11 - ML - Support Vector Machine(SVM)
Chapter 12 - Classification Algorithms - Decision Tree
Chapter 13 - Classification Algorithms - Naïve Bayes
Chapter 14 - Classification Algorithms - Random Forest
Chapter 15 - Regression Algorithms - Overview
Chapter 16 - Regression Algorithms - Linear Regression
Chapter 17 - Clustering Algorithms - Overview
Chapter 18 - ML - Clustering K-Means Algorithm
Chapter 19 - ML - Clustering Mean Shift Algorithm
Chapter 20 - ML - Hierarchical Clustering
Chapter 21 - KNN Algorithm - Finding Nearest Neighbors
Chapter 22 - ML - Performance Metrics
Chapter 23 - Machine Learning - Automatic Workflows
Chapter 24 - Improving Performance of ML Models
Chapter 25 - Improving Performance of ML Model(Contd..)
Chapter 1 - Machine Learning with Python - Basics
We are living in the ‘age of data’ that is enriched with better computational power and more storage resources,. This data or information is increasing day by day, but the real challenge is to make sense of all the data. Businesses & organizations are trying to deal with it by building intelligent systems using the concepts and methodologies from Data science, Data Mining and Machine learning. Among them, machine learning is the most exciting field of computer science. It would not be wrong if we call machine learning the application and science of algorithms that provides sense to the data.
What is Machine Learning?
Machine Learning (ML) is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do.
In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention.
Need for Machine Learning
Human beings, at this moment, are the most intelligent and advanced species on earth because they can think, evaluate and solve complex problems. On the other side, AI is still in its initial stage and haven’t surpassed human intelligence in many aspects. Then the question is that what is the need to make machine learn? The most suitable reason for doing this is, “to make decisions, based on data, with efficiency and scale”.
Lately, organizations are investing heavily in newer technologies like Artificial Intelligence, Machine Learning and Deep Learning to get the key information from data to perform several real-world tasks and solve problems. We can call it data-driven decisions taken by machines, particularly to automate the process. These data-driven decisions can be used, instead of using programing logic, in the problems that cannot be programmed inherently. The fact is that we can’t do without human intelligence, but other aspect is that we all need to solve real-world problems with efficiency at a huge scale. That is why the need for machine learning arises.
Why & When to Make Machines Learn?
We have already discussed the need for machine learning, but another question arises that in what scenarios we must make the machine learn? There can be several circumstances where we need machines to take data-driven decisions with efficiency and at a huge scale. The followings are some of such circumstances where making machines learn would be more effective.
Lack of human expertise
The very first scenario in which we want a machine to learn and take data-driven decisions, can be the domain where there is a lack of human expertise. The examples can be navigations in unknown territories or spatial planets.
There are some scenarios which are dynamic in nature i.e. they keep changing over time. In case of these scenarios and behaviors, we want a machine to learn and take data-driven decisions. Some of the examples can be network connectivity and availability of infrastructure in an organization.
Difficulty in translating expertise into computational tasks
There can be various domains in which humans have their expertise,; however, they are unable to translate this expertise into computational tasks. In such circumstances we want machine learning. The examples can be the domains of speech recognition, cognitive tasks etc.
Machine Learning Model
Before discussing the machine learning model, we must need to understand the following formal definition of ML given by professor Mitchell −
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
The above definition is basically focusing on three parameters, also the main components of any learning algorithm, namely Task(T), Performance(P) and experience (E). In this context, we can simplify this definition as −
ML is a field of AI consisting of learning algorithms that −
Based on the above, the following diagram represents a Machine Learning Model −
Let us discuss them more in detail now −
From the perspective of problem, we may define the task T as the real-world problem to be solved. The problem can be anything like finding best house price in a specific location or to find best marketing strategy etc. On the other hand, if we talk about machine learning, the definition of task is different because it is difficult to solve ML based tasks by conventional programming
Publisher: BookRix GmbH & Co. KG
Publication Date: 03-26-2020
All Rights Reserved