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Machine Learning Fundamentals

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Machine Learning Fundamentals

Enroll Now
Duration
2 Days
Delivery
(Online and onsite)
Price
Price Upon Request
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.   See other courses available

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.

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.   See other courses available

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.

    Upcoming Dates
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