The Ultimate Machine Learning Tutorial

Topics Covered

Here are the topics covered in the Machine Learning Tutorial:

• What is Machine Learning and How Does It Work
• Machine Learning Applications
• Supervised And Unsupervised Learning
• Linear Regression in Python
• Logistic Regression in Python
• Liner Regression vs. Logistic Regression
• Random Forest Algorithm
• Naive Bayes Classifier
• K-Means Clustering Algorithm
• KNN Algorithm
• Decision tree in Python
• Best Guide to Confusion Matrix
• Cost Function in Machine Learning
• How To Become A Machine Learning Engineer?
• Machine Learning Career Path
• Machine Learning Interview Questions

As businesses interact with customers and collect large volumes of data, they have started appreciating the importance of machine learning in their business. By collecting insights from the data, companies can work better and gain a competitive edge over others.

The Machine Learning tutorial will help you understand machine learning, its working principles, and how it can be used every day. And guess what? By the end of this machine learning tutorial, you will be trained in the skills that can help you become a Machine Learning Engineer faster and make a career in this field

Why Learn Machine Learning?

As an emerging field, Machine Learning offers immense opportunities for those looking at a highly impactful and satisfying career in IT. The increase in usage of Machine Learning is set to increase the demand for well qualified and professional Machine Learning Engineers by 60-percent. This Machine learning tutorial gives you a step by step learning curve to master this latest technology with practical examples. 

Applications of Machine Learning 

Machine learning is gaining immense popularity due to the range of applications that can be used across the world. It is now an integral and important part of both self-learning AI and robots. In the industrial sector, machine learning is helping in the development of technologies and tools that provide faster and cost-effective solutions to industries while also helping them predict demand and supply for their products and pricing models.  

The machine learning tutorial will cover numerous such applications impacting consumers directly with applications such as prediction engines to online TV live streaming, which are an integral part of our modern lifestyle. Some of the areas in which machine learning is already playing a significant role are:

1. Social Media

Social media platforms are particularly popular among the youth for their user-friendly features and the ability to connect easily with one's contacts. It is all possible through the use of algorithms designed in machine learning. For example, Facebook uses Machine Learning to observe and record different activities of users and even tracks their chats, likes, and comments, and the time individuals spend on various posts. Based on these observations and learning from the data collected, it suggests friends and pages you should follow.

2. Product Recommendations 

Being able to recommend the best and correct product is what most of the e-commerce websites aim for. This is where Machine Learning plays an important role since product recommendations are ideal for using machine learning techniques. Using both machine learning and AI, e-commerce websites can track your shopping pattern based on your previous purchase, the products you searched for, and the items in your cart. Based on these, it can make the right and best product recommendations for you. 

3. Image Recognition Technology

This is a very popular application of machine learning since it helps both private and government organizations to classify and sort images based on its particular features quickly. 

4. Sentiment Analysis

Understanding the message being conveyed and the thoughts behind a particular piece of communication are beneficial to both organizations and governments. This is where Machine Learning plays an important role with its capability for analyzing different sentiments, to get a better understanding of the emotion or thoughts of the speaker or the writer. In scenarios where an individual has written a review, email, or document, the algorithm will help users understand the intention of the text. 

5. Healthcare

Good quality and preventive health care are some of the primary objectives of the healthcare sectors and an area in which Machine Learning algorithms can play an important role. 

You will also learn more about practical implementation and case studies in each section of this Machine Learning Tutorial.

Who Should Learn Machine Learning Tutorials?

All those looking forward to establishing a career in forthcoming technologies and be a part of the latest developments in technology should consider Machine Learning as a career option. As an emerging field, it offers excellent opportunities for growth and career acceleration, along with an opportunity to work with some of the best companies and start-ups in the world.

Prerequisites to Get the Best out of Machine Learning Tutorial

While all people who want to make a career in the IT field can consider Machine Learning as a career option, those with an understanding of basic statistics and mathematics at the college level would be able to grasp the concepts faster and easier.  

A basic familiarity with Python programming would also be beneficial for those interested in Machine Learning. Ideally, you should understand the fundamental courses such as Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before starting the Machine Learning tutorial.

Enroll in the comprehensive Professional Certificate in AI and Machine Learning to gain in-demand skills that will help you scale up your career! 🎯

Machine Learning Tutorial: Table of Contents 

1. What is Machine Learning and How Does It Work?

Machine Learning is the process through which computers find and use insightful information without being told where to look. It can also be defined as the ability of computers and other technology-based devices to adapt to new data independently and through iterations. This happens through the ability of applications to learn from previous computations and transactions and use pattern recognition to produce reliable and informed results. The Machine Learning process starts with inputting training data into the selected algorithm. 

2. Real-World Machine Learning Applications That Will Blow Your Mind

Machine Learning can be used to analyze the data at individual, society, corporate, and even government levels for better predictability about future data based events. It could be used to predict the economy of both states and countries, while also forecasting a company's growth.

3. Supervised and Unsupervised Learning

In Supervised Learning, the machine learns under guidance and supervision. It is based on a model that can predict with the help of a labeled dataset, for which you already know the target answer. 

In Unsupervised Learning, the machine uses unlabeled data and learns on itself without any supervision. The smart machine and other technology-based devices try to find a pattern in the unlabeled data and present a response based on the same. 

4. Linear Regression in Python

Linear Regression can be used to determine the economic growth of a country or state along with the GDP of that country, the price of products in the future, housing sales, and even score predictions in matches.

Recommended Read: Python Tutorial for Beginners  ✔️

5. Random Forest Algorithm

Random Forest is a learning method that works on the principle of constructing multiple decision trees. The final decision is made based on the majority of the trees chosen by the random forest.

A few of the uses for random forest algorithm currently used today in remote sensing include:

• Object detection and multi-class object sorting, such as sorting out different vehicles, such as cars and buses, in traffic
• Kinect uses random forest algorithms as part of game consoles by tracking body movements and then recreating it in the game for entertainment. 

6. Understanding Naive Bayes Classifier

If you want to know how your email provider implements spam filtering, how online news channels perform news text classification, or how companies perform sentiment analysis of their audience on social media, then the key to all of them is the ML algorithm named the Naive Bayes classifier. 

This algorithm is responsible for classifying and sorting data as per the desired groups and activities. It has been named after Thomas Bayes from the 1700s who developed the principle of conditional probability, based on which the algorithm is now designed. 

7. K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases

K-means clustering is a part of the machine learning curriculum and has details about unsupervised algorithms, where you can find the input data which does not have a labeled response. Clustering is a form of unsupervised learning in which the data points are grouped into different sets based on their similarity. 

Clustering is of two categories:

• Hierarchical clustering
• Partitioning clustering 

From which, Hierarchical clustering can be subdivided into:

• Agglomerative clustering
• Divisive clustering

Even Partitioning clustering can be subdivided into:

• K-Means clustering 
• Fuzzy C-Means clustering 

8. How to Become a Machine Learning Engineer?

To become a Machine Learning Engineer, you'll want to take an AI and ML Certification that guides you through all the fundamental technical areas like classical machine learning, deep learning, and reinforcement learning, along with the more complex mathematical and statistical concepts that are used regularly within the field. You will need to have a basic understanding of these fundamentals, so acquiring this knowledge will be highly beneficial to you before becoming a Machine Learning Engineer.

9. Machine Learning Career Path

If you are considering a career in machine learning, you can look for opportunities in large and established companies and start-up businesses. There are also companies specializing in data science as their primary business and also have data science departments. 

Apart from these, machine learning engineers can look for opportunities in cloud-based companies that allow customers to upload data and logs for analytics and companies that offer APIs and other tools to allow customers to write their algorithms. 

10. Top Machine Learning Interview Questions and Answers

While approaching an organization or an employer for an opportunity in machine learning, one needs to be prepared in-depth on the probable questions they may ask during the interview to test your knowledge and skills. This tutorial will help you with top interview questions and their answers.

Did You Know? 🔍

The average annual salary for a Machine Learning Engineer in the US is around $109,143 to $131,000, with companies like Apple and Facebook offering up to $170,000 to $200,000. 🚀

Machine Learning Tutorial and the Next Step

Machine Learning is considered to be one of the best career options in the IT industry. It has huge potential for people looking to embark on a career with immense possibilities.

Start with "What is Machine Learning and How Does it Work" to get a better grasp of this subject and how it works, so that you can realize its true potential and also make a better-informed choice about it as a career path. Start learning now!