Lead the GenAI revolution. Future-proof your skills by understanding AI basics, machine learning, algorithms, ethical practices, and real-world applications. Stay ahead in tech leadership.

In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.
 
Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.
 
You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You'll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Grokking AI for Engineering & Product Managers

## Introduction
Machine learning algorithms can be broadly categorized into the following three groups:
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

Throughout this chapter, we discuss the nuances of each of these categories. However, this lesson focuses specifically on Supervised learning. 

## Supervised learning
In supervised learning, the training data provided as input to the algorithm includes the final solutions, called labels or class, because the algorithm learns by “looking” at the examples with correct answers. In other words, the algorithm has a ***supervisor***, or a ***teacher***, who provides it with all the answers first, like whether there’s a cat in the picture or not. Additionally, the machine uses these examples to learn one by one. The spam filter is also a good example of this.

Another typical task of a different type would be to predict a target numeric value, like housing prices from a set of features like size, location, number of bedrooms. To train the system, we again need to provide many correct examples of known housing prices, including both their features and their labels. 

While categorizing emails or identifying whether the picture is of a cat or a dog is a supervised learning algorithm of type **classification**, predicting housing prices is known as **regression**. What's the difference?

In regression, the output is a continuous value or a decimal number, like housing prices. In classification, the output is a label, like "spam or not-spam", and not a decimal number; the output only takes values like 0 or 1, where we could have 1 for “spam” and 0 for “non-spam.”
Basically, the **type of algorithm we choose (classification or regression) depends on the type of output we want**. 

Based on the examples so far, you can understand that this is an approach to be used **when**: 
- We know how to classify the input data.
- We know the type of behavior we want to predict.
- We need the algorithm to give us predictions for new unseen data.

**Examples of supervised learning algorithms:**

- Linear regression
- Logistic regression
- Decision trees 
- Random forests
- Naive Bayes
- Neural networks

In the next lesson, we will dive deeper into each of these algorithms.



# Introduction
Machine learning algorithms can be broadly categorized into the following three groups:
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

# Supervised learning
In supervised learning, the training data provided as input to the algorithm includes the final solutions, called labels or class, because the algorithm learns by “looking” at the examples with correct answers. In other words, the algorithm has a ***supervisor***, or a ***teacher***, who provides it with all the answers first, like whether there’s a cat in the picture or not. Additionally, the machine uses these examples to learn one by one. The spam filter is also a good example of this.

Another typical task of a different type would be to predict a target numeric value, like housing prices from a set of features like size, location, number of bedrooms. To train the system, we again need to provide many correct examples of known housing prices, including both their features and their labels. 

While categorizing emails or identifying whether the picture is of a cat or a dog is a supervised learning algorithm of type **classification**, predicting housing prices is known as **regression**. What's the difference?

In regression, the output is a continuous value or a decimal number, like housing prices. In classification, the output is a label, like "spam or not-spam", and not a decimal number; the output only takes values like 0 or 1, where we could have 1 for “spam” and 0 for “non-spam.”
Basically, the **type of algorithm we choose (classification or regression) depends on the type of output we want**. 

Based on the examples so far, you can understand that this is an approach to be used **when**: 
- We know how to classify the input data.
- We know the type of behavior we want to predict.
- We need the algorithm to give us predictions for new unseen data.

**Examples of supervised learning algorithms:**

- Linear regression
- Logistic regression
- Decision trees 
- Random forests
- Naive Bayes
- Neural networks

In the next lesson, we will dive deeper into each of these algorithms.



This lesson introduces supervised learning. 


The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Supervised Learning

Introduction

Supervised learning