Introduction

 

With the increase in the demand for Data Scientists and a significant rise in Data Science as a career, more and more professionals are willing to learn the concepts of Machine Learning (ML) and stay updated with the latest technologies and trends. There are numerous easy to learn Machine Learning and one such is taking up online machine learning training. This blog focuses on the best ways to learn online machine learning. Let us get started by first knowing more about ML!

 

What do you mean by Machine Learning?

 

Without being particularly programmed for a task, robots can learn from experience with the help of artificial intelligence through a process called machine learning. (In a nutshell, without human assistance, machines learn autonomously. Giving them high-quality data at the outset, followed by training the machines by creating various machine learning models based on the data and various algorithms, is how this process works. Depending on the type of data we have and the task we are trying to automate, we will choose the appropriate algorithms.

 

Significance of Machine Learning

 

The availability of high-speed internet and the rate at which the digital world is developing are generating enormous amounts of data every single minute. This is a key component in creating automated systems that can accurately use various algorithms for large data sets to handle data at such a level. The use of this method by businesses of all sizes today helps them manage costs, reduce risk, and also raises the calibre of their goods and services. In many businesses today, this technology is widely used, and soon it will play a significant role in our daily life. (which is already in progress)

 

How to Learn Machine Learning?

 

The roadmap to online machine learning is not linear and has many ups and downs. It demands complete focus and dedication to learn the concepts and ensure a firm grasp of the underlying principles. There are multiple milestones in the roadmap, the first one being starting with the basics and understanding the essential concepts.

 

Start with the Basics

 

You need to be familiar with Linear Algebra, Multivariate Calculus, Statistics, and Python as prerequisites to begin with the online machine learning 

 

  • Gain knowledge of multivariate calculus and linear algebra -  Calculus with multiple variables and linear algebra are both essential in machine learning. Your responsibilities as a data scientist will determine how much you will require them. You won't be as highly focused on maths if you are more interested in machine learning that is significantly applied.
  • Learn statistics -  Machine learning is heavily reliant on data. Actually, gathering and organising data will take up about 80% of your time as an ML specialist. Data collection, analysis, and presentation are all responsibilities of the discipline of statistics. Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, and other essential topics in statistics are among the most crucial.
  • Python is another prerequisite that you cannot miss. While you can also use R, Scala, and other languages for machine learning. The language of choice for ML right now is Python. In reality, there are numerous Python libraries, like Keras, TensorFlow, Scikit-learn, and others, that are particularly helpful for artificial intelligence and machine learning.

 

Hence to learn machine learning, you must be well-aware of the above mentioned topics.  

 

Move Towards Learning the Concepts of ML

 

With the understanding of the rpe-requisites, it becomes easier to grasp the topics of Machine Learning. However, there are a few terminologies that you must know before sliding into more advanced and complicated stuff. These are:-

 

  • Machine learning terminology: A model is a particular representation that is discovered from data by using a machine learning method. One can also refer to a model as a hypothesis.
  • Features are distinct, quantifiable characteristics of the data. A feature vector makes it easy to describe a collection of numerical features. The model receives feature vectors as input data. 
  • The value that our model is expected to predict is known as the target variable or label.
  • Training - The idea is to provide a set of inputs (features) and their anticipated outputs (labels), so that after training, we will have a model (hypothesis) that would subsequently track the data to one of the categories trained on.
  • Once our model is complete, we can feed it a set of inputs and get a projected result in return (label).

 

The Various Types of Machine Learning

 

During online machine learning, you will come across the numerous types of machine learning and the role they play. Each kind has its own implementation and is essential to gain knowledge about them. The types of machine learning are;

 

  • Supervised Learning - Classification and regression models are used in supervised learning to help students learn from a training dataset of labelled data. This process of learning doesn't stop until the desired level of performance is attained.
  • Unsupervised Learning - It entails using unlabeled data to uncover the underlying structure, which may then be used with factor and cluster analysis models to uncover more and more information about the data itself.
  • Semi-Supervised Learning - Using unlabeled data, similar to unsupervised learning, along with a limited quantity of labelled data is known as semi-supervised learning. Labelled data significantly improves learning accuracy and is more economical than supervised learning.
  • Reinforcement Learning - It refers to learning the best course of action through trial and error. Learning behaviours that are based on the current state are therefore used to choose the next course of action.

 

Best Online Courses for Machine Learning 

 

  • Machine Learning by The IoT Academy - The IoT Academy in collaboration with the experts and mentors at IIT Guwahati has curated the perfect online machine learning training course that is suitable for all levels and is completely beginner friendly. The course features live-instructor-led sessions, doubt clearing classes, flexible timings, access to top-notch study resources, projects to practice skills, and a highly recognized certificate at the completion of the course. 
  • By Coursera - Professor Andrew Ng, a co-founder of Google Brain, Coursera, and the vice president who expanded Baidu's AI team to include thousands of scientists, is the instructor and creator of this introductory course. Ng is also a Stanford professor. The course employs the open-source programming language Octave rather than Python or R. The course material is both comprehensive and naturally intuitive. In addition to some calculus explanations and a review of linear algebra, the maths necessary to comprehend each algorithm is fully explained.
  • Machine Learning Crash Course by Google - This online machine learning course is offered by Google AI Education, a platform with a mixture of written, visual, and interactive content that is totally free. The concepts required to resolve ML issues as soon as feasible are covered in the Machine Learning Crash Course. Python is the programming language of choice, and TensorFlow is introduced, just like in the prior course. Google Colab-hosted interactive Jupyter notebooks are available for each of the curriculum's major subject areas.

 

Ways to Practice ML

 

Data gathering, integration, cleaning, and preparation are the ML tasks that take the longest to complete. Practice with this because you need high-quality data, but a lot of it is usually unclean and you want to practise with it. Thus, this is where you'll spend the majority of your time! Study different models, then practise on actual datasets. You can develop your intuition about the kinds of models that are appropriate in certain scenarios with the help of this. The ability to interpret the outcomes of employing various models is just as crucial as these other processes. Understanding the varied tuning parameters and regularisation techniques used with distinct models would make this task simpler.

 

Conclusion

 

There are numerous online machine learning trainings to learn about this booming topic.You need to be diligent in the process and practise the gained knowledge to gain mastery in Machine Learning. Before beginning with the ML, there are some basic concepts that you must be familiar with such as Linear Algebra, Multivariate Calculus, Statistics, and Python.