Machine learning (ML) is a branch of computer science that provides the capability to the computers to learn without the requirement of any clearly formulated programming. Machine Learning offers great career opportunities and is increasingly...
Machine learning (ML) is a branch of computer science that provides the capability to the computers to learn without the requirement of any clearly formulated programming.
Machine Learning offers great career opportunities and is increasingly being used in almost all aspects of our knowledge domain – be it business, science, technology or even medicine and space research.
In order to start a career in machine learning, you need to have the passion to learn new things, as learning and the ability to solve problems on a day to day basis is the success mantra of this field. Before you set the foot in this segment, it is imperative to have a basic understanding of what machine learning is all about including the mathematical logic, alternative technologies used and hands-on-experience required.
This article is dedicated to all those professionals and students, who want to explore a career option in machine learning.
Machine learning in recent years has evolved rapidly with the adoption of new technologies, frameworks, business models and techniques. So, at the preliminary stage, you need to get curious about all these aspects and be a constant learner.
Machine learning is a logical field and is best suited for aspirants who have a logical blend of mind. It integrates several disciplines such as mathematics, technology, and business analysis, making it an interdisciplinary job. Apart from a strong technological focus, you also need to need to be open to understand the business problems and possess the ability to interpret those problems into a machine learning paradigms, thereby adding value to the product or the project that you work upon.
Machine learning is more about working as part of a team, rather than being an isolated fragment in the big organizational picture. One of the success stories of being an excellent machine learning expert is to be proactive to work in a team, assimilate their ideas and put your thoughts into action. So, it’s necessary that you be a good team player.
Data is the new oil for any industry or technological segment. Hence, data analysts are the most suited professionals to make a perfect transition into a machine learning career as their next best career move. Of course, if you are not a data analyst, you need not worry; develop an analytical mindset and set your focus on data analysis and interpretation. This means you need to play with the data – dig the data, comprehend where it is most suited and infer the net outcomes from the data. Bottom line is you need to share the information in a prudent way, generate good visualization, and integrate information that can be easily understood by all stakeholders.
The best programming language that is most suited for machine learning is Python. Apart from that, you also need to learn how to use machine learning libraries. If you feel daunted about how to get started, there are a host of institutes and courses online that teaches you the concepts of Python, apart from customized courses dedicated only for machine learning. It’s always better to connect with the experts to get going in this area.
As mentioned in my earlier tip, you can take up some online courses, apart from participating in learning competitions (kaggle.com is one such website) to gain good knowledge and showcase it to others. There are several online customized courses offered by learning platforms such as Udemy, Udacity, Coursera, which are dedicated only for machine learning. Hence, enroll in some good online course, read new articles on emerging technologies and connect with technology experts on various social media platforms to trigger a career push in machine learning.
You need to understand that every organization has specific and unique goals. Hence, it is better to do thorough research about the industry, where you want to work. For instance, it may take a couple of months to understand a financial product of a specific business segment, but the crux is to research and learn about it as quickly as possible to leverage your knowledge in this domain. Also, remember that you need not be an expert, but gaining some preliminary knowledge about the domain or the product that you are going to work really goes a long way in building a good career in machine learning.
If you are interested to begin your career in machine learning, it’s always advisable to start small. Hence, don’t target big companies such as Amazon, Google, etc as they look for experienced candidates. Try to target startup companies, where you have immense opportunities to learn and apply your knowledge.
One aspect which helps a potential employer to take note of your skill sets is to showcase some good projects. Take the advice of your seniors; connect with experts and try creating and uploading a project in Github. This way, you could definitely gain an edge over other potential candidates, eying a career in machine learning.
Piece of Advice
Similar to any other career option, you need to have the passion, interest to try new things and above all the genuine aptitude of learning to make a mark in machine learning. Keeping aside the technical and analytical skills, you need to get motivated about solving new challenges, day in and day out and always proactive to learn new technologies to keep yourself updated. That is the key to success.
This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.
Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial
It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.
Below topics are explained in this Machine Learning course for beginners:
Basics of Machine Learning - 01:46
Why Machine Learning - 09:18
What is Machine Learning - 13:25
Types of Machine Learning - 18:32
Supervised Learning - 18:44
Reinforcement Learning - 21:06
Supervised VS Unsupervised - 22:26
Linear Regression - 23:38
Introduction to Machine Learning - 25:08
Application of Linear Regression - 26:40
Understanding Linear Regression - 27:19
Regression Equation - 28:00
Multiple Linear Regression - 35:57
Logistic Regression - 55:45
What is Logistic Regression - 56:04
What is Linear Regression - 59:35
Comparing Linear & Logistic Regression - 01:05:28
What is K-Means Clustering - 01:26:20
How does K-Means Clustering work - 01:38:00
What is Decision Tree - 02:15:15
How does Decision Tree work - 02:25:15
Random Forest Tutorial - 02:39:56
Why Random Forest - 02:41:52
What is Random Forest - 02:43:21
How does Decision Tree work- 02:52:02
K-Nearest Neighbors Algorithm Tutorial - 03:22:02
Why KNN - 03:24:11
What is KNN - 03:24:24
How do we choose 'K' - 03:25:38
When do we use KNN - 03:27:37
Applications of Support Vector Machine - 03:48:31
Why Support Vector Machine - 03:48:55
What Support Vector Machine - 03:50:34
Advantages of Support Vector Machine - 03:54:54
What is Naive Bayes - 04:13:06
Where is Naive Bayes used - 04:17:45
Top 10 Application of Machine Learning - 04:54:48
How to become a Machine Learning Engineer - 04:59:46
Machine Learning Interview Questions - 05:09:03
This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.
Mastering the Machine Learning Course would easily develop one's career. This is the reason why studying Machine Learning Tutorial becomes so important in the career of a particular student.
Making a part of the machine learning course would enact and studying the Machine Learning Tutorial would make one carve out a new niche.
Machine learning is changing the dimensions of business in many industries. A report projects that the value added by machine learning systems shall reach up to $3.9 Trillion by 2022.Machine lear...
Machine learning is proving it's worth in many industries like manufacturing, financial services, healthcare, and retail, to name a few. We hope that we have dispelled some of the myths associated with Machine Learning. It wouldn't be MLan incorrect to say that we have both overestimated and underestimated the potential of Machine learning systems.