Introduction to Machine Learning: Exploring the Basics

Table of Contents:-

– What is Machine Learning:
–Applications of Machine Learning:
– Types of Machine Learning Algorithms
1. Supervised Learning
2. Unupervised Learning
3. Semi-Supervised Learning
4. Reinforcement Learning
– Comparison between all algorithms
–Summary

What is Machine Learning:

To understand what is Machine Learning, we can look at it as the science of making computers to learn and act like a brain does or as humans do, and autonomously improve their learning over time by feeding them data

The basic building blocks of Machine Learning algorithms involve three important components: Representation, evaluation, and Optimization. While Representation is the first step of an ML algorithm’s implementation where we define a set of classifiers or we define finite automation that a computer can understand, Evaluation involves various scoring functions that can represent predictions of either future values or a future outcome and finally, Optimization which involves a Loss/Cost function that helps in minimizing faults and maximizing efficiency

The end goal of machine learning algorithms is to make use of the past data, implement each of the above three components and then successfully interpret any new or unseen data — thus proving its worth and might in solving the business problems.

Applications of Machine Learning:

1. Image Recognition
2. Speech Recognition
3. Traffic predictions
4. Product recommendations
5. Email Filtering
6. Self-driving cars

Types of Machine Learning Algorithms

1. Supervised Learning
2. Unsupervised Learning
3. Semi-Supervised Learning
4. Reinforcement Learning

1. Supervised Learning:

These algorithms learn from the past to predict the future. They use data that already has labels attached to it, which helps them understand how things are categorized. By analyzing this labeled data, the algorithms figure out the important factors that determine those labels.

Once they’ve learned from the past, these algorithms can be used on new data to make predictions. They compare their predictions with what they know is correct and make adjustments if they’re not quite right.

When they’re being trained, these algorithms study examples where the inputs are matched with their correct labels. This helps them find patterns and understand how things are supposed to be categorized. Then, when they’re used in real situations, they can take new inputs and decide which label those inputs belong to based on what they’ve learned.

The most widely used algorithms in Supervised Learning are Linear Regression, Logistic Regression, Decision Trees, Naïve Bayes, Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machines etc. Most of the supervised learning algorithms in Python are implemented using the Scikit Learn module and in R it is implemented via the caret package

2. Unupervised Learning:

Unsupervised learning is a type of machine learning where we analyze data that doesn’t have any labels or answers already attached to it. Unlike supervised learning, we don’t know the “correct” answers in advance. The main aim of unsupervised learning is to uncover hidden patterns and structures within the data. The algorithm explores the data and identifies different types of information and relationships that we might not have known existed. There are no “wrong” outputs or ways to measure errors in unsupervised learning since we don’t have labeled data to compare against.

The two major types of Unsupervised Learning are Clustering and Association Rules. And others include Anomaly Detection and Latent Variable Detection.

Cluster Analysis is like sorting toys into different groups based on how they are similar. We look at things like their colors, shapes, or sizes to decide which toys belong in the same group. We use special rules to compare the toys and put them together. For example, if two toys are similar in shape and color, we might say they belong in the same group.

The clusters are modeled using a similarity or a distance measure such as Euclidean Distance, Cosine Similarity or probabilistic distance. Common clustering algorithms are Hierarchical clustering, k-Means clustering

In association rules, we look for interesting relationships and connections in a large amount of data. It’s like finding patterns in how different things are related. For example, we might notice that people who buy bread also tend to buy butter or that patients who have a certain symptom also have another related symptom. These relationships help us understand how things are connected and can be useful for making decisions or predictions.

Majorly used association rule algorithms include the Apriori algorithm, FP-Growth, etc

3. Semi-Supervised Learning:

Semi-supervised learning is like a mix of supervised and unsupervised learning. It uses a small amount of labeled data (data with answers or labels) and a larger amount of unlabeled data (data without labels). This combination helps improve the performance and efficiency of unsupervised learning models.

By using the labeled data along with the unlabeled data, semi-supervised learning algorithms can leverage the benefits of both worlds. The labeled data provides some guidance and helps the model understand the patterns and relationships better. The unlabeled data, although without explicit labels, still carries valuable information that can be used to uncover hidden patterns and structures.

Overall, semi-supervised learning allows us to make the most of the available data by combining labeled and unlabeled examples, leading to more efficient and effective learning models.

An important aspect of semi-supervised learning algorithms is that they can be used to create proxy labels. Whenever we don’t have sufficient labeled data to perform supervised learning, we can add the unlabeled data to increase the training data size, get new labels, and then used the newly formed data for Supervised Learning. These include self-training, multi-view learning, and self-ensembling, and Pseudo-Label. This technology has been widely and famously used in Amazon’s Alexa

4. Reinforcement Learning:

In these algorithms, the learning process happens in a special way. The machine or software tries things out, learns from its mistakes, and adjusts its actions based on the feedback it gets. It interacts with its environment, taking actions and seeing the results, while also figuring out what works and what doesn’t.

The goal is to make the machine or software behave in the best possible way for a specific situation. It learns by making decisions step by step, instead of making each decision independently like in other learning methods.

So, in simple terms, these algorithms learn by trying, making mistakes, and getting better over time. They learn how to behave well in different situations by learning from the feedback they get.

To gain a better understanding of reinforcement learning, I recommend watching the following video for a comprehensive guide.

Reinforcement Learning is used in automation processes mostly — robotics, online games, interactive guided instructions or tours, text summarizations, etc.

Difference between Supervised Learning ,Unupervised Learning, Semi-Supervised Learning,Reinforcement Learning

Summary:

In summary, machine learning consists of four captivating types:

• Supervised learning: Leveraging labeled data for predictions.
• Unsupervised learning: Uncovering hidden patterns in unlabeled data.
• Semi-supervised learning: Harnessing the combined power of labeled and unlabeled data.
• Reinforcement learning: Acquiring wisdom through trial and error.

These distinct approaches empower us to:

• Make predictions and classifications.
• Unveil hidden patterns and structures.
• Enhance learning efficiency.
• Cultivate intelligent systems.

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