Guide About Machine Learning
Are you curious about the hype around What is Machine Learning ? Are you a Non-technical person who wants to learn about it but don’t have a clue where to start? Do you think machine learning is only meant for data scientists and engineers? If your answer is yes, then this post is for you. Machine Learning might seem intimidating, but it is not as complicated as you might think.
Machine Learning is a dominant force that has accelerated the progress in all the business verticals. It has dramatically altered and impacted almost every industry. The majority of individuals believe that ML is reserved only for the technical audience, I say it’s not entirely true.
In Layman terms, Machine learning is the branch of Artificial Intelligence which aims to find ways for computers to enhance their performance based on experience. Machines can learn automatically without being explicitly programmed.
Let’s dive into some basic concepts to build our ML knowledge.
Artificial intelligence is the simulation and the study of human intelligence processes by computers. These processes include learning, reasoning, and self-correction. Machine learning is the technology which utilizes the principles of AI to create statistical models. These models make predictions based on past data and discover patterns in the data.
Machine learning is still emerging. Many models for training a machine are already recognized, but more will be developed with time. Distinctive business problems need different models, hence, the use of ML is crucial.
A machine learning model should be explored and taught using data. More data in the system will lead to more accurate predictions and a better chance to accomplish the given task successfully.
Billions of data are generated every day because of the advent of the internet. Majority of this data is unstructured in the format of videos, audios, photos, documents etc. The human brain cannot comprehend all of the data.
Computing and finding patterns in this massive data is not an easy task. This is where Machine learning helps. The science of ML is not new, but it has gained momentum. When the machine learning models are exposed to new data, they adapt. They learn from past computations to produce consistent and reliable results.
How do machines learn? The answer is straightforward, they learn as our brains do. Machine Learning process follows some steps:
The first step is to collect the right data for solving the problem and achieving the goal.
Raw data is mostly unstructured, unreliable and noisy. Therefore, some preprocessing is vital to make the data ready.
1. Cleaning:Data can have lots of missing values and using them, will produce misleading results. It can be generated in different formats and from various sources. Data sets should be cleaned before further processing.
2. Splitting: The chosen data should be split into two sets: one for training the algorithm and the other to evaluate it.
3. Training the data:
It depends on what problem you want to solve and which dataset and algorithm you have chosen. The main aim is to find a suitable mathematical function or algorithm to accomplish the chosen goal accurately.
Trained algorithms are evaluated/tested by using some test datasets. If the test result is not satisfied, the training will need to be conducted again.
The model is ready to perform tasks and create values for you. It can be optimized for faster and easier integration within the desired platforms.
There are primarily three types of machine learning models:
In this type, the correct outcome for each data point is explicitly labelled to train the model. Therefore, instead of finding the answer, the model finds the relationship to classify or predict the unassigned data points.
In this case, unlabelled data is given to train the algorithm. The value of this algorithm lies in discovering patterns and correlations.
It is a blend of supervised and unsupervised learning. It is employed to solve more complex questions and involves interaction with the environment.
Machine learning has countless applications in almost all major fields. Few examples are listed below:
ML is automating the trading process as machines are learning from previous data and taking decisions to minimize loss and maximize profit.
The best example is Google DeepMind’s AlphaGo which was explicitly trained to play Go. It analyses the moves of the players and learns by practising against itself numerous times.
Machines can gather and access more data, e.g. IBM’s Green Horizon Project. It analyses environmental data from multiple sensors to produce accurate and reliable weather forecasts.
Machine learning models provide real-time insights. Combined with AI and deep learning, they are helping healthcare professionals to diagnose patients faster and more accurately. This model reduces diagnostic errors and predicts adverse reactions.
For example, fraud detection in credit card transactions. The machine learning algorithm is built and trained to detect fraudulent transactions. The training data is then improved for the desired output information like, which transactions appear fake and which do not.
State-of-the-art home security systems use machine learning. ML-powered smart homes have many features like notifying when your kids are back from school.
All the voice assistants like Alexa, Siri, Cortana etc. are powered by machine learning algorithms.
We are in the age of Artificial Intelligence and Machine Learning. There is a lot to explore, and we cannot ignore these domains. With ML and AI applications becoming more extensive and ubiquitous, individuals from non-technical backgrounds are also fascinated to learn more about them.
Anyone with non-technical knowledge like a CEO or marketing person can use Machine Learning to improve their business.
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