Components of ML

Machine learning is a complex field that involves two primary components: the ML life cycle and algorithms.

1. ML Life Cycle: Involves stages such as problem definition, data collection, preprocessing, model selection, training, evaluation, and deployment to develop and deploy machine learning models.

2. Algorithms: Comprise mathematical and statistical techniques like supervised, unsupervised, and reinforcement learning, used to create models that learn from data and solve various types of problems.

What is machine learning ?

Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” from data, without being explicitly programmed.

In conventional programming, we provide data and logic to the system, and it generates an output based on the logic or program we provided.

For example, if we create a program to add two numbers, and we provide two numbers to the system, it will generate the sum of "only two numbers." This is what we mean by "explicitly programmed."

In machine learning, however, we provide input data and output to the model, and it generates "logic" using certain "algorithms" (called machine learning algorithms). We don't need to explicitly program the system for the data.

For example, if we provide a dataset containing numbers and their sums in different columns as input data, and apply a relevant algorithm, it will generate a "logic or pattern" that recognizes "numbers are given and their sum." Now, whenever we provide some numbers, whether two, three, or any number, it knows it needs to perform "summation" on those numbers. Hence, no explicit programming is needed.

Unlike conventional programming, where a program for adding two numbers will only sum two numbers, in machine learning, it can sum any number of numbers.

When is ML Useful ?

There are several situations where machine learning is more useful than traditional programming. Here are a few examples :

1. You can't write programs for everything

For example : if we're building an email spam classifier to detect spam emails, you might create a program with conditions like: if an email contains "huge discount" more than three times, it's spam. But if by any chance, advertisers find out about this logic, they might change their emails to use words like "big" or "massive." Then, you'd have to update the code with new conditions, and this cycle continues.

In ML, if the data changes, the logic updates itself to detect spam. The beauty of ML is that you only need to write one algorithm, and it can adapt continuously.

2. When there are unlimited number of cases

For example : In image classification of dogs, you need to determine if a dog is present in the picture. There are hundreds of dog breeds, and we can't write code for each "feature" of a dog.

So, what we do is train our model with input data from hundreds of dog images. The model then generates the logic by itself and can classify dogs using the algorithm.

3. In data mining

For example, businesses can use data mining techniques to understand customer behavior, predict future trends, and make informed decisions. Machine learning algorithms can automatically adjust and improve over time as they process more data, leading to more accurate predictions and insights. This capability makes data mining an invaluable tool for industries such as finance, healthcare, and marketing, where understanding complex data relationships can lead to significant competitive advantages.

Evolution of Machine learning

• Initially, machine learning efforts were constrained by limited computational power and data availability. However, as technology advanced, improvements in hardware, faster processors, and increased storage capacities enabled the processing of larger datasets and more complex algorithms.

• The evolution of machine learning algorithms has been crucial. Early algorithms were simple, focusing on linear relationships and basic pattern recognition.

• Over time, more complex algorithms like neural networks, decision trees, and support vector machines were developed, allowing for better handling of non-linear relationships and sophisticated pattern recognition.

• The growth of the data science community and the sharing of knowledge through academic publications, open-source projects, and conferences have accelerated innovation in machine learning. Collaboration and competition have driven the creation of new techniques and methodologies.

Key milestones in machine learning include:

• 1950s: Alan Turing's concept of a "learning machine" and Frank Rosenblatt's perceptron algorithm.

• 1960s: Development of early algorithms like the nearest neighbor algorithm.

• 1970s: Stagnation due to limited computational power, with Kunihiko Fukushima proposing the neocognitron.

• 1980s: Resurgence with backpropagation for training neural networks.

• 1990s: Introduction of support vector machines and the random forest algorithm.

• 2000s: Rise of big data and deep belief networks.

• 2010s: Prominence of deep learning, AlexNet's success, GANs, and the transformer model.

• 2020s: Advancements in reinforcement learning, NLP, and explainable AI, with breakthroughs like GPT-3 and AlphaFold.

AI vs ML vs Neural networks vs DL

What is intelligence ?

• Human intelligence is a complex and multifaceted concept that encompasses various abilities, such as pattern recognition, problem-solving skills (often measured by IQ tests), and emotional intelligence, which includes understanding and managing emotions.

• However, in the realm of artificial intelligence (AI), we focus on a subset of human intelligence. AI primarily deals with "pattern recognition" because, at present, it is challenging to quantify emotions and creativity. For example, we cannot yet define an equation for love or creativity.

Symbolic AI, often referred to as expert systems, represents an early approach to AI.

• These systems contain a knowledge base and are designed to solve specific problems by following predefined rules.

• However, they have limitations, such as being effective only for particular tasks like playing chess. When faced with problems involving uncertainty or imprecision, such as identifying a dog in an image, these systems struggle.

• This limitation led to the evolution of AI into more advanced forms that can handle a broader range of tasks, incorporating techniques like fuzzy logic and machine learning to improve their capabilities.

Artificial Intelligence (AI), Machine Learning (ML), Neural Networks, and Deep Learning (DL) are interconnected fields that have evolved over time, each contributing uniquely to the development of intelligent systems

• AI is the overarching field that encompasses both ML and DL. It aims to create systems that can perform tasks that typically require human intelligence.

• ML is a subset of AI that focuses on developing algorithms that allow computers to learn from and make predictions based on data.

• Neural Networks are a type of model used in ML, inspired by the structure of the human brain, and are particularly effective for tasks involving pattern recognition.

• DL is a specialized area within neural networks that involves deep architectures, allowing for the processing of complex data inputs and achieving state-of-the-art results in various domains.Here's a detailed overview of their history, differences, and relationships:

History and Evolution

1. Artificial Intelligence (AI)

   • 1950s: The concept of AI was introduced, with Alan Turing proposing the Turing Test to measure a machine's ability to exhibit intelligent behavior.

   • 1960s-1970s: Early AI research focused on symbolic methods and problem-solving.

   • 1980s: Expert systems became popular, using rule-based systems to mimic human decision-making.

   • 1990s-2000s: AI research expanded into areas like robotics and natural language processing.

   • 2010s-Present: AI has seen significant advancements with the integration of ML and DL, leading to applications in various industries.

2. Machine Learning (ML)

   • 1950s-1960s: ML emerged as a subfield of AI, focusing on algorithms that enable computers to learn from data.

   • 1980s: The development of backpropagation algorithms for training neural networks marked a significant milestone.

   • 1990s: Introduction of algorithms like support vector machines and decision trees.

   • 2000s-Present: The rise of big data and computational power has propelled ML into mainstream applications, with techniques like ensemble learning and reinforcement learning gaining traction.

3. Neural Networks

   • 1950s: Frank Rosenblatt developed the perceptron, an early neural network model.

   • 1980s: The backpropagation algorithm enabled the training of multi-layer neural networks.

   • 1990s: Neural networks faced challenges due to limited computational resources and data.

   • 2010s-Present: Advances in hardware and data availability have led to the resurgence of neural networks, particularly deep neural networks.

4. Deep Learning (DL)

   • 2000s: Deep learning gained attention with the development of deep belief networks.

   • 2010s: Breakthroughs like AlexNet demonstrated the power of deep learning in image recognition tasks.

   • 2010s-Present: DL has become a dominant approach in AI, with applications in computer vision, natural language processing, and more.

Differences and Relationships