There is no denying that machine learning is the future. With the advent of Big Data, the machine learning boom has taken the tech industry by storm. However, machine learning is not very easy. You have to invest a lot of time to become an expert in machine learning. The best way to approach machine learning is by a step-by-step guide. It will help you deal with the subject slowly without getting too overwhelmed by it. Here are a few ways which can make you a machine learning expert:
- Understanding the basics
Before diving into machine learning, you need to know what you are getting into. Just knowing a few basics will not help – you have to be aware of the finer details in machine learning. Learn what analytics, Big Data, Artificial Intelligence, Data Science are and how they are related to one another.
- Learning basic statistics
When you research on the basics of machine learning, you will often come across many statistical applications. So, what should be your next step? Brush up your statistics. You don’t have to be an expert in statistics, but you need to learn a few topics in statistics. It will be essential in machine learning. A few topics you should work on are sampling, data structures, linear and multiple regression, logistic regression, probability, etc.
- Learning a programming language
While researching machine learning, you will learn about the different programming languages which support machine learning. When you learn these programming languages, you become familiar with many applications of machine learning like data preparation, data cleaning, quality analysis, data manipulation, and data visualization.
- Taking up an Exploratory Data Analysis project
Exploratory Data Analysis means analyzing data sets and then explaining or showing that summary presented by that data set, mostly in a visual format. In this project, charts, diagrams, or other visual representations can be used to display the data. A few topics that need to be covered here are Single variable explorations, visualization, pair-wise, and multi-variable explorations.
- Creating unsupervised learning models
Unsupervised learning model is a machine learning technique where you do not need to supervise the model. It will discover information on its own and work on it. For example, if you give the basic parameters of several countries like population, income distribution, demographics, etc., unsupervised learning models can help you find out which countries are most similar. It uses unsupervised machine learning algorithms. It can be grouped into two kinds of problems: Clustering and Association. Two Unsupervised learning algorithms are k-means for clustering problems or the Apriori algorithm for association rule learning problems.
- Creating supervised learning models
Supervised learning models are a kind of learning where you teach and train the machine to use labelled data to arrive at the right conclusion. After training the machine with the labelled data, you have to provide some training examples to see if it produces the right outcome. For example, if you provide the specific descriptions of an apple (Red, Rounded) and a banana (Yellow, long curving cylinder) to the machine, then it can separate the two fruits and put them in their respective categories. Logistic regression and Classification trees are a few topics you need to cover here.
- Understanding Big Data Technologies
The machine learning models being used today were there in the past too. However, we can make full use of them now because nowadays, we have access to large amounts of data. Big data systems stores and control the vast amounts of data that are used in machine learning. So, if you are making your way to be an expert in machine learning, you should research and understand Big Data Technologies.
- Exploring Deep Learning Models
Top tech companies like Google and Apple are working with deep learning models to make Google Assistant and Siri better. Deep learning models help machines listen, write, read, and speak. Even vehicle tests are now conducted using deep learning models. Learn about topics like Artificial Neural Networks, Natural Language Processing, etc. Start by making your model differentiate between a fruit and a flower. That’s a great start and will set a pattern for future learning.
- Completing a data project
Finally, find a data project and work on it. You can search for a data project on the internet. Work on it and showcase your skills. There’s nothing for fulfilling and educative as the proper application of machine-learning.
Benefits of Machine Learning
Machine learning is one of the most innovative technologies which is being used by top companies like Amazon, Apple, and Google. Now, the question is: what are the benefits of Machine learning? Here are a few benefits of machine learning:
- Identifying trends and patterns
Machine learning can review large sets of data and identify trends and patterns based on it. For example, Amazon can direct notifications to buyers based on their purchasing and browsing history of a user.
- Constant Improvement
Machine learning algorithms improve over time. With the increase of data input, machine learning will be more accurate and help in making better predictions.
- No human intervention
With machine learning, machine algorithms learn by themselves and improve themselves automatically. So, you don’t have to invest all your time in it.
- Different kinds of data
Machine Learning algorithms can handle multi-dimensional and multi-variety data easily and is thus, very efficient in handling large data sets.
- Many Applications
The applications of machine learning are expanding. From being used software like Siri to even driverless vehicle testing, machine learning is becoming the future in many industries. It is also being included in healthcare industries. Machine learning applications are far and wide.
Job Prospects of Machine Learning
Machine Learning is one of the hottest careers in the market right now. Top tech firms like Amazon, Google, and Apple, are integrating machine learning with their software. According to Gartner, AI will be creating 2.3 million jobs in 2020. These jobs will require research and developing algorithms. Machine learning scientists will have to extract patterns from Big Data too. Some hot career positions are:
- Machine Learning Engineer
- Machine Learning Analyst
- Data Sciences Lead
- Machine Learning Scientist
- NLP Data Scientist