Description

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning. The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role. By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.

What You Will Learn

Become familiar with data science and machine learning terms
Distinguish between model parameters and hyperparameters
Distinguish between supervised and unsupervised learning
Discover how decision trees, bagging, and random forest works
Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning
Learn about neural networks and clustering techniques
Evaluate the performance of machine learning models

Audience

This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.

Approach

With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.

Key Features

Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using machine learning

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

Course Outline

1. Welcome and Introduction

1. Welcome

This video welcomes you to the course.

2. Introduction to Data Science

This video provides an introduction to data science.

2. Preliminary Requirements to Understand Data Science and Machine Learning

1. Understanding Data Science Terms

This video provides an overview of the terms used in data science.

2. Understanding Machine Learning

This video presents an overview of machine learning.

3. Types of Learning

This video explains the different types of learning.

4. Understanding Data Science Modeling

This video focuses on data science modeling.

5. Model parameters versus Hyperparameters

This video explains the difference between model parameters and hyperparameters.

3. Machine Learning Models

1. How Linear Regression Works

This video demonstrates how linear regression works.

2. How Decision Trees Work

This video demonstrates how decision trees work.

3. How Bagging and Random Forest Work

This video demonstrates the working of bagging and random forest.

4. How Support Vector Machine Works

This video demonstrates the working of support vector machines.

5. Neural Networks- Part 1

This video is the first part of a two-part video that explains neural networks.

6. Neural Networks- Part 2

This is the second part of the two-part video that explains neural networks.

7. How Logistic Regression Works

This video demonstrates the working of logistic regression.

8. How k-nearest neighbour (KNN) Works

This video focuses on KNN and explains how it works.

9. Clustering Techniques

This video explains the different clustering techniques.

4. Model Performance

1. Evaluating Models Performance

This video demonstrates how to evaluate the performance of the models.

5. Best Practices for a Data Scientist

1. Best Practices for a Data Scientist

This video explains the best practices used by data scientists.

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Mastering Data Science and Machine Learning Fundamentals

By Packt

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