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ML Techniques and Methods

The Frick Chemistry Laboratory, Princeton University
(The Frick Chemistry Laboratory, Princeton University - Kimberly Chen)

 

 Machine Learning: Algorithms that parse data, learn from that data, 

and then apply what they’ve learned to make informed decisions

 
 

- How Does Machine Learning Work?

Machine intelligence is the last invention that humanity will ever need to make. If you could look back a couple of years ago at the state of AI and compare it with its current state, you would be shocked to find how exponentially it has grown over time. It has branched out into a variety of domains such as machine learning (ML), Expert Systems, natural language processing (NLP), and dozens more.  

While the idea behind AI is to build smarter systems that think and execute on their own, they still need to be trained. The ML domain of AI has been created for the very exact purpose by bringing several algorithms, allowing for smoother data processing and decision-making.

The “learning” in machine learning refers to a process in which machines review existing data and learn new skills and knowledge from that data. Machine learning systems use algorithms to find patterns in datasets, which might include structured data, unstructured textual data, numeric data, or even rich media like audio files, images and videos. Machine learning algorithms are computationally intensive, requiring specialized infrastructure to run at large scale. 

Suppose we have to train a model that can recognize the given data (image) as a cat or a dog. We will use tag (definition) input. We each took thousands of photos of cats and dogs. After that, we will do feature extraction. This means that we have to extract features (e.g. color, eyes, nose, ears, etc.) from the raw input that defines or can distinguish between cats and dogs.

 

- Machine Learning Algorithm and Model

Machine learning involves the use of machine learning algorithms and models. A machine learning algorithm, also called model, is a mathematical expression that represents data in the context of a ­­­problem, often a business problem. The aim is to go from data to insight. 

  • Machine learning algorithms are procedures that are implemented in code and are run on data.
  • Machine learning models are output by algorithms and are comprised of model data and a prediction algorithm.
  • Machine learning algorithms provide a type of automatic programming where machine learning models represent the program.

For example, if an online retailer wants to anticipate sales for the next quarter, they might use a machine learning algorithm that predicts those sales based on past sales and other relevant data. Similarly, a windmill manufacturer might visually monitor important equipment and feed the video data through algorithms trained to identify dangerous cracks.

 

- ML Techniques

The entire idea behind machine learning (ML) is to go from data to insight. From a given problem (by large business one) to an adequate solution. The machine learning algorithms help in predicting future trends, changes, and opportunities. However, large datasets are essential in this task. To harness them, data scientists use several machine learning techniques and methods.

Although the intention behind machine learning is to work without human assistance, to some extent, this assistance is indispensable. To put it in plain language, you have to teach your algorithm how it should work and what it ought to look for. This is exactly what the data scientists do. Does it sound familiar to you? It should! This is how humans learn–from experience. The machine learning algorithms use computational methods to “learn” information directly from available data. This is why it is crucial to input as much relevant data as it’s available. As the number of samples increases, the ML algorithm works more and more efficiently. 

Machine learning techniques can be divided into three foremost types: Supervised Machine Learning Methods, Unsupervised Machine Learning Methods, and Reinforcement Machine Learning Methods.

  • Supervised Machine Learning Methods: The supervised machine learning methods are used when you want to predict or explain the data you possess. The supervised machine learning techniques group and interpret data based only on input data. A supervised algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions. The most popular supervised techniques are classification and regression. For instance, the supervised ML techniques can be used to predict the number of new users who will sign up for the newsletter next month. 
  • Unsupervised Machine Learning Methods: The unsupervised machine learning methods find hidden patterns or intrinsic structures in data. They are used to draw inferences from datasets consisting of input data without labeled responses. The unsupervised algorithms group and interpret data solely on input information. The unsupervised ML techniques can be used to aggregate products with similar characteristics, for instance, to simplify the search process in your eCommerce business.
  • Reinforcement Machine Learning Methods: Reinforcement learning is the process of learning to map from conditions to behavior to optimize a scalar gain or a reinforcing alert. The learning system is not told what action to take, as with other ways of learning. Instead, it must figure out which actions produce the highest reward by attempting them. For the most fascinating and difficult situations, actions influence not just the instant reward but also the scenario that follows, and all future incentives arising from that. The trial-and-error search and delayed incentive are the two defining characteristics of reinforcement learning. Let’s take a deeper look into reinforcement learning and how it enables goal-driven systems.

 

- The Ten ML Methods

The following ten methods described below offer an overview -- and a foundation you can build on as you hone your machine learning knowledge and skill. The ten methods are the main disciplines in machine learning. Most machine learning algorithms fall into one of these categories:

  • Regression
  • Classification
  • Clustering
  • Dimensionality Reduction
  • Ensemble Methods
  • Neural Nets and Deep Learning
  • Transfer Learning
  • Reinforcement Learning
  • Natural Language Processing
  • Word Embeddings


Dinant_Belgium_Civil_Engineering_Discoveries_102620A
[Dinant, Belgium - Civil Engineering Discoveries]

- Top ML Algorithms

Machine Learning (ML) algorithms are the brains behind any model, allowing machines to learn, making them smarter. The way these algorithms work is, they’re provided with an initial batch of data, and with time, as algorithms develop their accuracy, additional data is introduced into the mix. This process of regularly exposing the algorithm to new data and experience improves the overall efficiency of the machine. ML algorithms are vital for a variety of tasks related to classification, predictive modeling, and analysis of data.

  • Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis
  • Classification and Regression Trees
  • Naive Bayes
  • K-Nearest Neighbors (KNN)
  • Learning Vector Quantization (LVQ)
  • Support Vector Machines (SVM)
  • Random Forest
  • Boosting
  • AdaBoost
  • SVM
  • K-means Clustering
  • Apriori Learning Algorithm
  • PCA (Principal Component Analysis)

 

 

[More to come ...]


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