Planning for a career in Machine Learning applications?

In recent times, Machine Learning is the most sought-after technology of the year. The next decade 2021-2030 is going to see a steep increase in the development of machine learning applications as most of the companies have invested a lot into ML Algorithmic Integration to their innovations cum products. Undoubtedly, Machine Learning is one of the most sought after technology.

In case you are planning to go for a career in Machine Learning or intend to develop applications in Machine Learning, there are some prerequisites that you need to know before diving deep into the world of Machine Learning.

Before getting started, it’s important that you understand different concepts and types of machine learning algorithms that are basic mandates in this field.

What is Machine Learning?

Machine learning Process

Applications of Machine Learning

1. Fraud Detection

2. Medical Diagnosis

3. Financial Sector

Prerequisite for Machine Learning

1. Basic Functions & Maths

2. Programming Knowledge

3. Machine Learning Algorithms

What is Machine Learning?

Machine Learning in simple words is “ Providing Intelligence to Machines”. To be diplomatic, Machine Learning is a subset of Artificial Intelligence and is also the scientific study of algorithms and statistical models used by computer systems. The machines or the computers use further to perform a specific task with the help of patterns and inference of data.

To provide a better understanding, here are some real-life scenarios.

Home Security Manager: Imagine a Machine that recognizes visitors to your home via their Photo and voice, check with your appointment schedules, also check for any criminal records and by default alert you in case of any discrepancies?

Travel Time & Alternate route Predictor: Can a Machine predict, inform and suggest an alternative route to your destination, by informing you that the earlier route planned would take more travel time, as there is an accident occurred before or by using the prediction data that the route consumes more time?

The above scenarios are perfect examples of ML Applications in real-world problems. Hence Machine Learning in simple can be stated as the concept to allow computers or machines to learn automatically, with no human intervention or assistance.

It should also be able to adjust and adapt to actions accordingly. Constant up-gradation and use of existing database and predictions from Machine Learning Algorithms. The best Machine Learning algorithms are, the best is the predictional analysis.

Machine learning Process

Machine learning is a continuous updating process. The process of Machine Learning can be categorized broadly in seven steps as follows

Applications of Machine Learning

1. Fraud Detection

For years, fraud has been a major issue in sectors like banking, medical, insurance, and also many others. Due to the increase in online transactions through different payment options, such as credit/debit cards, PhonePe, G-Pay, Paytm, etc., fraudulent activities have also increases.

Moreover, fraudsters or criminals have become very skilled in finding escapes so that they can loot more. Since no system is perfect and there is always a loophole, it has become a challenging task to make a secure system for authentication and also preventing customers from fraud. So, Fraud detection algorithms are very useful for preventing fraud.

2. Medical Diagnosis

It can be also used in techniques and tools that are going to help in the diagnosis of diseases. With the help of analysis of clinical parameters, prediction of disease progression is made. From here, you can have a medical opinion in terms of the therapy planning of the patient, along with monitoring.

3. Financial Sector

Machine Learning is the driving force for the popularity of services that the financial sector provides. It helps banks and other institutions to make smarter decisions. With the help of Machine Learning, you can also predict an account closure beforehand.

Click to read more about the Machine learning Projects.

Prerequisite for Machine Learning

Having said that what are the prerequisites to acquaint yourself for a successful career in Machine learning?

When it comes to prerequisites to learn Machine Learning, this is high up on the list, as it does involve some basic maths. This lays down the core foundation of how information can be extracted from the data at hand.

1. Basic Functions & Maths

Data forms the base of any Machine Learning Applications. Data in simple can be of any form. Any excel data of sensor outputs, PLC Time Inputs, Weather data, or any sort. Manipulation of data in the simplest form is data validation and it also requires a basic interest in mathematical equations and problem-solving skills. Calculus plays a vital role in building a model of machine learning.

2. Programming Knowledge

Being able to write code is one of the most important things when it comes to Machine Learning. You need to know languages such as Python and R to implement the process.

Basic functions such as:

• Defining and calling functions
• Lists, sets, and dictionaries (assessing, iterating, and creating)
• for loops with multiple variable iterators
• if/else conditional expressions
• String formatting
• Pass statement – for syntax

You could do a course in Python, to be specific. This will not only ease your process of learning this subject but also give a better understanding of data modeling. Python Basic Programs for Learning.

3. Machine Learning Algorithms

Efficient Algorithms make efficient ML Models and hence accurate predictions. Therefore simple understanding of the ML Algorithms, in general, will ease your effort in the domain of Machine Learning algorithm development. Here are the three different learning styles in machine learning algorithms:

a. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is preparing through a training process in which it is requiring to make predictions and make corrections when those predictions are wrong. The training process continues until the model achieves a desiring level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include Logistic Regression and the Back Propagation Neural Network.

b. Unsupervised Learning

Input data is not labeled and also it does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction, and also association rule learning.

Example algorithms include the Apriority algorithm and K-Means.

c. Semi-Supervised Learning

Input data is a mixture of labeled and unlabeled examples.

There is a prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are also extensions to other flexible methods that make assumptions about how to model the unlabelled data.

Software Download links for Machine Learning Application Developments

Now that you have opted for ML Career, have shown some subtle interest in Machine Learning Application Development, or intend to develop some Machine Learning projects, software, or packages that are necessary for the same are listed below. Download links for the same are also provided.

Online IDLE: Google Colab [Cloud Based ]

Offline IDLE: Anaconda Navigator

Python IDLE: https://www.python.org/downloads/

Package’s

• NUMPY
• PANDAS
• MATPLOTLIB
• SEABORN
• NLTK
• Sci-Kit Learn