1. Introduction

Goal of the Tutorial

This tutorial aims to provide a beginner-friendly introduction to the field of Machine Learning (ML). You will learn the basic concepts of ML, understand different types of ML, and see how they are applied in real-life scenarios.

Learning Outcomes

By the end of this tutorial, you will:
- Understand what Machine Learning is
- Be familiar with the types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Understand the basic process of preparing data, training a model, and testing a model
- Be able to implement simple Machine Learning models using Python

Prerequisites

It would be helpful to have a basic understanding of Python programming. Also, some knowledge of statistics and mathematics can be beneficial but is not mandatory.

2. Step-by-Step Guide

Understanding Machine Learning

Machine Learning is a subset of Artificial Intelligence (AI) that provides systems the ability to automatically learn from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves.

Types of Machine Learning

Supervised Learning: The algorithm learns from labeled data. After understanding patterns in data, it can predict outcomes for unforeseen data.

Unsupervised Learning: The algorithm learns from unlabeled data. Without prior training, it must find patterns in data.

Reinforcement Learning: The algorithm learns to perform an action from experience.

Machine Learning Process

1. Data Preparation: Includes cleaning data, handling missing data, normalization, and transformation.
2. Model Training: After preparing data, we train our Machine Learning model using an appropriate ML algorithm.
3. Model Testing: After training, we test our model with a new data set that it hasn't seen before to see how it performs.

3. Code Examples

Example 1: Simple Linear Regression with Python

Linear Regression is a simple Supervised Learning algorithm that is used to predict a numerical value.

# Importing Libraries
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import pandas as pd

# Load the data
dataset = pd.read_csv('data.csv')

# Prepare the data
X = dataset['Hours'].values.reshape(-1,1)
y = dataset['Scores'].values.reshape(-1,1)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Train the model
regressor = LinearRegression()  
regressor.fit(X_train, y_train)

# Test the model
y_pred = regressor.predict(X_test)

# Check the accuracy
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))

4. Summary

We've covered the basics of Machine Learning, including the different types and a simple example of creating a linear regression model. The next steps would be to delve deeper into different algorithms, such as Decision Trees, SVM, and clustering.

5. Practice Exercises

1. Try to predict a different numerical value from the dataset.
2. Use a different Machine Learning model on the same dataset and compare the results.
3. Find a different dataset and try to apply the same Machine Learning model.

Remember, practice is key when learning Machine Learning!