The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns. Machine learning techniques leverage data mining to identify historic trends to inform future models.

The typical supervised machine learning algorithm consists of (roughly) three components:

  1. A decision process: A recipe of calculations or other steps that takes in the data and returns a “guess” at the kind of pattern in the data your algorithm is looking to find.
  2. An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
  3. An updating or optimization process: Where the algorithm looks at the miss and then updates how the decision process comes to the final decision so that the next time the miss won’t be as great.

For example, if you’re building a movie recommender, your algorithm’s decision process might look at how similar a given movie is to other movies you’ve watched and come up with a weighting system for different features.

During the training process, the algorithm goes through the movies you have watched and weights different properties. Is it a sci-fi movie? Is it funny? The algorithm then tests out whether it ends up recommending movies that you (or people like you) actually watched. If it gets it right, the weights it used stay the same; if it gets a movie wrong, the weights that led to the wrong decision get turned down so it doesn’t make that kind of mistake again.

Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention — providing the ability to uncover hidden insights without being specifically programmed to do so. 

How to get started with Machine Learning?

To get started with Machine Learning, let’s take a look at some of the important terminologies used in Machine Learning:

Some Terminology of Machine Learning
  1. Model: Also known as “hypothesis”, a machine learning model is the mathematical representation of a real-world process. A machine learning algorithm along with the training data builds a machine learning model.
  1. Feature: A feature is a measurable property or parameter of the data-set.
  1. Feature Vector: It is a set of multiple numeric features. We use it as an input to the machine learning model for training and prediction purposes.
  1. Training: An algorithm takes a set of data known as “training data” as input. The learning algorithm finds patterns in the input data and trains the model for expected results (target). The output of the training process is the machine learning model.
  1. Prediction: Once the machine learning model is ready, it can be fed with input data to provide a predicted output.
  1. Target (Label): The value that the machine learning model has to predict is called the target or label.
  1. Overfitting: When a massive amount of data trains a machine learning model, it tends to learn from the noise and inaccurate data entries. Here the model fails to characterise the data correctly.
  1. Underfitting: It is the scenario when the model fails to decipher the underlying trend in the input data. It destroys the accuracy of the machine learning model. In simple terms, the model or the algorithm does not fit the data well enough.

There are Seven Steps of Machine Learning

  • Gathering Data
  • Preparing that data
  • Choosing a model
  • Training
  • Evaluation
  • Hyperparameter Tuning
  • Prediction

It is mandatory to learn a programming language, preferably Python, along with the required analytical and mathematical knowledge. Here are the three mathematical areas that you need to brush up before jumping into solving Machine Learning problems:

  1. Linear algebra for data analysis: Scalars, Vectors, Matrices, and Tensors
  2. Mathematical Analysis: Derivatives and Gradients
  3. Probability theory and statistics
  4. Multivariate Calculus
  5. Algorithms and Complex Optimizations

What Are Some Machine Learning Methods?

Many machine learning models are defined by the presence or absence of human influence on raw data — whether a reward is offered, specific feedback is given or labels are used. 

  1. Supervised learning: The dataset being used has been pre-labeled and classified by users to allow the algorithm to see how accurate its performance is.
  2. Unsupervised learning: The raw dataset being used is unlabeled and an algorithm identifies patterns and relationships within the data without help from users.
  3. Semi Supervised learning: The dataset contains structured and unstructured data, which guide the algorithm on its way to making independent conclusions. The combination of the two data types in one training dataset allows machine learning algorithms to learn to label unlabeled data.
  4. Reinforcement learning: The dataset uses a “rewards/punishments” system, offering feedback to the algorithm to learn from its own experiences by trial and error.

Finally, there’s the concept of deep learning, which is a newer area of machine learning that automatically learns from datasets without introducing human rules or knowledge. This requires massive amounts of raw data for processing and the more data that is received, the more the predictive model improves.

Applications of Machine Learning

An ideal illustration of an female AI head in vivid gradient color
Facial recognition/Image recognition

The most common application of machine learning is Facial Recognition, and the simplest example of this application is the iPhone X. There are a lot of use-cases of facial recognition, mostly for security purposes like identifying criminals, searching for missing individuals, aid forensic investigations, etc. Intelligent marketing, diagnose diseases, track attendance in schools, are some other uses.

Automatic Speech Recognition

Abbreviated as ASR, automatic speech recognition is used to convert speech into digital text. Its applications lie in authenticating users based on their voice and performing tasks based on the human voice inputs. Speech patterns and vocabulary are fed into the system to train the model. Presently ASR systems find a wide variety of applications in the following domains:

  • Medical Assistance
  • Industrial Robotics
  • Forensic and Law enforcement
  • Defense & Aviation
  • Telecommunications Industry
  • Home Automation and Security Access Control
  • I.T. and Consumer Electronics
Recommendation Systems

Many businesses today use recommendation systems to effectively communicate with the users on their site. It can recommend relevant products, movies, web-series, songs, and much more. The most prominent use-cases of recommendation systems are e-commerce sites like Amazon, Flipkart, and many others, along with Spotify, Netflix, and other web-streaming channels.

The future scope of Machine Learning

To conclude, let us see how the future will turn up for Machine Learning. As per estimates, the Machine Learning market will grow to reach USD 8.81 billion by the year 2022.

That means that there is going to be a substantial requirement of skills around Machine Learning to drive this growth. The future looks promising for those planning a career in Machine Learning!

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