9 Tips to Trigger a Great Career in Machine Learning

9 Tips to Trigger a Great Career in Machine Learning

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 (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.

1. Be a Constant Learner

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.

2. Develop a Logical Blend of Mind

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.

3. Integrate with the Team you Work

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.

4. Gain a Good Grounding in Data Analysis

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.

5. Learn the Right Programming Language to Get Started

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.

6. Online Courses can Help you a Great Extent

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 UdemyUdacityCoursera, 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.

7. Research about the Industry where you want to Work

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.

8. Look for Small Companies

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.

9. Showcase Some toy Projects

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.

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

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:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

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.

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5...

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5 machine learning frameworks that are cutting edge technology in your hands.

Through the machine learning frameworks, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.

As we know that ML is considered as a subset of Artificial Intelligence (AI). The scientific study of statistical models and algorithms help a computing system to accomplish designated tasks efficiently. Now, as a mobile app developer, when you are planning to choose machine learning frameworks you must keep the following things in mind.

The framework should be performance-oriented
The grasping and coding should be quick
It allows to distribute the computational process, the framework must have parallelization
It should consist of a facility to create models and provide a developer-friendly tool
Let’s learn about the top five machine learning frameworks to make the right choice for your next ML application development project. Before we dive deeper into these mentioned frameworks, know the different types of ML frameworks that are available on the web. Here are some ML frameworks:

Mathematical oriented
Neural networks-based
Linear algebra tools
Statistical tools
Now, let’s have an insight into ML frameworks that will help you in selecting the right framework for your ML application.

Don’t Miss Out on These 5 Machine Learning Frameworks of 2019
#1 TensorFlow
TensorFlow is an open-source software library for data-based programming across multiple tasks. The framework is based on computational graphs which is essentially a network of codes. Each node represents a mathematical operation that runs some function as simple or as complex as multivariate analysis. This framework is said to be best among all the ML libraries as it supports regressions, classifications, and neural networks like complicated tasks and algorithms.

machine learning frameworks
This machine learning library demands additional efforts while learning TensorFlow Python framework. Your job becomes easy in the n-dimensional array of the framework when you have grasped the Python frameworks and libraries.

The benefits of this framework are flexibility. TensorFlow allows non-automatic migration to newer versions. It runs on the GPU, CPU, servers, desktops, and mobile devices. It provides auto differentiation and performance. There are a few goliaths like Airbus, Twitter, IBM, who have innovatively used the TensorFlow frameworks.

#2 FireBase ML Kit
Firebase machine learning framework is a library that allows effortless, minimal code, with highly accurate, pre-trained deep models. We at Space-O Technologies use this machine learning technology for image classification and object detection. The Firebase framework offers models both locally and on the Google Cloud.

machine learning frameworks
This is one of our ML tutorials to make you understand the Firebase frameworks. First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted into the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and patterns of the object placed in front of it.

The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
The other photo that we targeted was a half water glass. The core of the machine learning app is to assemble data and to manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
The last one is a full glass recognition image.
Note: For correct recognition, there has to be 1 label that carries at least 100 images of a particular object.

#3 CAFFE (Convolutional Architecture for Fast Feature Embedding)
CAFFE framework is the fastest way to apply deep neural networks. It is the best machine learning framework known for its model-Zoo a pre-trained ML model that is capable of performing a great variety of tasks. Image classification, machine vision, recommender system are some of the tasks performed easily through this ML library.

machine learning frameworks
This framework is majorly written in CPP. It can run on multiple hardware and can switch between CPU and GPU with the use of a single flag. It has systematically organized the structure of Mat lab and python interface.

Now, if you have to make a machine learning app development, then it is mainly used in academic research projects and to design startups prototypes. It is the aptest machine learning technology for research experiments and industry deployment. At a time this framework can manage 60 million pictures every day with a solitary Nvidia K40 GPU.

#4 Apache Spark
The Apache Spark machine learning is a cluster-computing framework written in different languages like Java, Scala, R, and Python. Spark’s machine learning library, MLlib is considered as foundational for the Spark’s success. Building MLlib on top of Spark makes it possible to tackle the distinct needs of a single tool instead of many disjointed ones.

machine learning frameworks
The advantages of such ML library lower learning curves, less complex development and production environments, which ultimately results in a shorter time to deliver high-performing models. The key benefit of MLlib is that it allows data scientists to solve multiple data problems in addition to their machine learning problems.

It can easily solve graph computations (via GraphX), streaming (real-time calculations), and real-time interactive query processing with Spark SQL and DataFrames. The data professionals can focus on solving the data problems instead of learning and maintaining a different tool for each scenario.

#5 Scikit-Learn
Scikit-learn is said to be one of the greatest feats of Python community. This machine learning framework efficiently handles data mining and supports multiple practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework is known for supervised & unsupervised learning algorithms as well as cross-validation. The Scikit learn is largely written in Python with some core algorithms in Cython to achieve performance.

machine learning frameworks
The machine learning framework can work on multiple tasks without compromising on speed. There are some remarkable machine learning apps using this framework like Spotify, Evernote, AWeber, Inria.

With the help of machine learning to build iOS apps, Android apps powered by ML have become quite an easy process. With this emerging technology trend varieties of available data, computational processing has become cheaper and more powerful, and affordable data storage. So being an app developer or having an idea for machine learning apps should definitely dive into the niche.

Conclusion
Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, ML algorithms from scratch, how this technology is helpful for your business? Just fill our contact us form. Our sales representatives will get back to you shortly and resolve your queries. The consultation is absolutely free of cost.

Author Bio: This blog is written with the help of Jigar Mistry, who has over 13 years of experience in the web and mobile app development industry. He has guided to develop over 200 mobile apps and has special expertise in different mobile app categories like Uber like apps, Health and Fitness apps, On-Demand apps and Machine Learning apps. So, we took his help to write this complete guide on machine learning technology and machine app development areas.