This Machine Learning (ML) Fundamentals course aims to explain the scikit-learn API, which is a package created to facilitate the process of building machine learning applications. By explaining the difference between supervised and unsupervised models, as well as by applying algorithms to real-life datasets, this course will help beginners to start programming machine learning algorithms.

As the use of machine learning algorithms becomes popular for solving problems in a number of industries, so does the development of new tools for optimizing the process of programming such algorithms.

Lesson 1: Introduction to scikit-learn

• scikit-learn
• Data Representation
• Data Preprocessing
• scikit-learn API
• Supervised and Unsupervised Learning

Lesson 2: Unsupervised Learning: Real-life Applications

• Clustering
• Exploring a Dataset: Wholesale Customers Dataset
• Data Visualization
• k-means Algorithm
• Mean-Shift Algorithm
• DBSCAN Algorithm
• Evaluating the Performance of Clusters

Lesson 3: Supervised Learning: Key Steps

• Model Validation and Testing
• Evaluation Metrics
• Error Analysis

Lesson 4: Supervised Learning Algorithms: Predict Annual Income

• Exploring the Dataset
• Naïve Bayes Algorithm
• Decision Tree Algorithm
• Support Vector Machine Algorithm
• Error Analysis

Lesson 5: Artificial Neural Networks: Predict Annual Income

• Artificial Neural Networks
• Applying an Artificial Neural Network
• Performance Analysis

Lesson 6: Building your own Program

• Program Definition
• Saving and Loading a Trained Model
• Interacting with a Trained Model

This Machine Learning Fundamentals course is perfect for beginners in the field of machine learning.

• No prior knowledge of the use of scikit-learn or machine learning algorithms is required.
• The students must have prior knowledge and experience of Python programming.

Hardware:

• Processor: Intel Core i5 or equivalent
• Memory: 4GB RAM or higher

Software:

• Sublime Text (latest version), Atom IDE (latest version), or other similar text editor applications.
• Python 3 installed
• The following Python libraries installed: NumPy, SciPy, scikit-learn, Matplotlib, Pandas, pickle, jupyter, and seaborn

Installation and Setup

• Before you start this course, you’ll need to install Python 3.6, pip, scikit-learn, and the other libraries used in this course. You will find the steps to install these here:

Installing Python

• Install Python 3.6 by following the instructions at this link: https://realpython.com/installing-python/.

Installing pip

• To install pip, go to the following link and download the get-pip.py file: https://pip.pypa.io/en/stable/installing/.
• Then, use the following command to install it: python get-pip.py

You might need to use the python3 get-pip.py command, due to previous versions of Python on your computer are already using use the python command.