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Starting A Career in Machine Learning: Everything You Need To Know

Learning Machine Learning

Introduction

Machine Learning has become the buzzword in recent years. This can be attributed to high data production by applications, the growing computation power, and the development of better algorithms.

Machine learning has found applications everywhere, from automating simple tasks to providing intelligent insights. Businesses in every sector are benefiting from machine learning today. And chances are you are using a device that is utilizing this technology, such as Google Home or Fitbit.

So, what is machine learning?

Simply put, Machine Learning involves the implementation of Artificial Intelligence to enable machines execute tasks from experience without specifically programming them about those specific tasks. In other words, the machine learns how to automatically perform a task without human contact. It all begins with feeding these machines with good quality data before training them to by building machine learning models using data in different algorithms. The choice of algorithms depends on the kind of data you have as well as the nature of tasks you are looking to automate.

Difference between Machine Learning and Artificial Intelligence

Artificial Intelligence (AI) is the broad science that mimics human abilities. Machine Learning, on the other hand, is a specific subset of Artificial Intelligence that trains a machine how to learn.

Who is using Machine Learning?

Machine Learning Applications

Simply put, Machine Learning involves the implementation of Artificial Intelligence to enable machines execute tasks from experience without specifically programming them about those specific tasks. In other words, the machine learns how to automatically perform a task without human contact. It all begins with feeding these machines with good quality data before training them to by building machine learning models using data in different algorithms. The choice of algorithms depends on the kind of data you have as well as the nature of tasks you are looking to automate.

Difference between Machine Learning and Artificial Intelligence

Artificial Intelligence (AI) is the broad science that mimics human abilities. Machine Learning, on the other hand, is a specific subset of Artificial Intelligence that trains a machine how to learn.

So, who is using Machine Learning?

Businesses working with large amounts of data are beginning to appreciate the importance of machine Learning Technology. By obtaining real-time insights from data, businesses are able to work more efficiently and gain competitive advantage. Here are some of the industries that are actively using Machine Learning technology today:

Financial sector

Businesses in the financial sector like banks are implementing Machine Learning for two important purposes: to prevent data, and gain important data insights. These insights can include investment opportunities, or help investors make trade decisions. Data mining can also help banks identify high-risk clients or pinpoint warning signs of fraud.

Governments

Government agencies like the public safety and utilities have strong interest in Machine Learning since they have multiple data sources that can be mined for insights. By analyzing census data, governments can figure out how to allocate resources and increase efficiency. Machine Learning is also implemented by crime investigations and prevention agencies.

The Healthcare sector

Machine Learning is fast gaining traction in the healthcare sector thanks to the advent of wearable sensors and devices that rely on data to provide real-time assessment of the patient’s health. Machine Learning is also helping medical experts analyze data with the goal of determining trends or red flags that may result in more accurate diagnosis and better treatment.

The Retail sector

Platforms like Amazon rely on Machine Learning to recommend items you might like based on your previous purchase decisions. They do this by capturing your data, analyzing it, and using it to personalize your shopping experience, carry out a marketing campaign, gain customer insights, optimize prices, and merchandize supply planning.

Oil and gas sector

Machine Learning is largely implemented in the oil and gas sector for exploring new energy sources, analyzing minerals, detector refinery sensor failures, and streamline oil distribution.

Transportation sector

Data analysis and modeling aspects of Machine Learning are crucial to identify trends and patterns is crucial to the transportation sector, which relies on making routes more efficient and predicting potential challenges to boost profitability.

Popular Machine Learning methods

Two of the most widely implemented methods of Machine Learning are supervised learning and unsupervised learning. However, there are other Machine Learning methods too. Here is a brief overview of them:

Supervised Learning

In this Machine Learning method, labeled examples are used to train algorithms, such as an input where the intended output is already known. For instance, machine could have data points labeled as either “F” for failed and “R” for runs. Thus, the learning algorithm receives a set of inputs along with the corresponding accurate outputs to detect errors. It them works to modify the model as desired. Supervised learning utilizes methods like classification, prediction, regression, and gradient boosting to predict the values of the label on a set of unlabeled data. It is commonly implemented in instances where historical data predicts the likely outcome of future events. For instance, it can be implemented to anticipate when a credit card transaction is likely to be fraudulent.

Unsupervised Learning

This is implemented against data that does not have any historical labels. The machine is not informed of the “right answer” upfront. Rather, the algorithm is let on its own to figure out what is being shown. The goal of unsupervised learning is to explore the given data in order to find out some sort of structure. Unsupervised data is often implemented in transactional data. For instance, it can identify customer segments with identical attributes who can them be used in marketing campaigns. Unsupervised data can find main attributes that identify specific customer segments. Common unsupervised learning techniques include nearest-neighbor mapping, self-organizing maps, k-means clustering, and singular value decomposition. Unsupervised learning algorithms can also be used to recommend items, segment topics, and identify data outliners.

Semi-Supervised learning

This Machine Learning method is used for the same applications as supervised learning. However, it is both labeled and unlabeled data for training – ideally combining a small amount of labeled data with a large chunk of unlabeled data. Semi-supervised learning can be used with methods like regression, classification, and prediction. It is helpful when the cost associated with labeling is quite high to allow for a fully labeled training process. An example of a semi-supervised learning is identifying a person’s face via a web cam.

Reinforcement Learning

This Machine Learning is commonly used in gaming, robotics, and navigation. Here, the algorithm works through trial and error to determine the actions that yield the best possible rewards. Reinforcement Learning has three key components: the agent (decision maker or learner), the environment (what the agent interacts with), and the actions (what the agent is capable of doing). The agent chooses the actions that optimize the expected reward over a time period. To reach the goal faster, the agent must follow best practices. Thus, the ultimate goal in reinforcement learning is to learn the best possible policy.

How to get started with Machine Learning

The world is evolving quite fast, and you have to evolve with it in every given sense. The idea is to be part of the future. Here is a step-by-step guide to getting started with Machine Learning:

Step 1: Get to know the prerequisites

You can get started with Machine Learning immediately. However, there are some prerequisites that you need to know. These include Multivariate Calculus, Linear Algebra, Statistics, and Python. You do not need a master’s degree in any of these topics to get started.

Learning Linear Algebra and Multivariate Calculus

Both Multivariate Calculus and Linear Algebra are crucial skills in Machine Learning. However, the extent to which you will need these skills depend on your role as a Machine Learning expert. If you are more inclined on application learning, then you will not need to focus more on mathematics as there are a number of libraries available. However, if you want to specialize in R&D in Machine Learning, then you need to master Multivariate Calculus and Linear Algebra since you will have to implement many Machine Learning algorithms right from scratch.

Learn Statistics

Data plays a fundamental role in Machine Learning. In fact, you will spend around 80 percent of your time as a Machine Learning expert collecting and cleaning data. And statistics is a field of mathematics that deals with data collection, analysis, and presentation. Some of the most important components of statistics that you need to master include Statistical Significance, Hypothesis Regression, Probability Distribution, and Regression. Bayesian Thinking is also a crucial part of Machine Learning that deals with a wide array of concepts like Priors, Conditional Probability, Maximum Likelihood, and Posteriors.

Learn Python

Some people prefer skipping Multivariate Calculus, Linear Algebra and Statistics and learn them as they move along. However, the one thing you cannot skip learning is Python. While you can use other languages like R and Scala for Machine Learning, Python remains the most popular Machine Learning language. In fact, there are several Python libraries out there that are specifically designed for Machine Learning and Artificial Intelligence. Some of these include TensorFlow, Keras, and Scikit-learn.

So, if you truly want to master Machine Learning, you better start off by learning Python. You can sign up for various online Python courses to master this skill.

Step 2: Learn the Different Machine Learning Concepts

Once you are through with the prerequisites, it is time to move on to the actual learning of Machine Learning. You are better off starting with the basics before moving on to more complex stuff. Here are some of the Machine Learning basic concepts:

  • Machine Learning terminologies
  1. Model – Also referred to as a hypothesis, this is a specific representation that you learn from data by applying specified Machine Learning algorithm.
  2. Feature – This is the data’s individual measurable property. A set of numeric features is conveniently described by a feature vector. Feature vectors are fed into the model as inputs. For instance, features like smell, taste, and color can be used to predict a fruit.
  • Target label – A target label or variable is the value that the model is designed to predict. For instance, a fruit can be labeled by name like orange, pineapple, or grape.
  1. Training – The idea is to feed in a set of inputs (features) and expect an output (labels), so after training, there is going to be a model (hypothesis) that maps available data to any of the categories trained on.
  2. Prediction – Once the model is ready, it will be fed a set of inputs to which it will be expected to provide a predictable output (label).

Career paths in Machine Learning

Businesses are increasingly adopting Machine Learning as it reduces human input while it boosts machine performance by empowering machines to execute tasks by themselves. Consequently, there are several Machine Learning career paths that both popular and well-paying such as Data Scientist, Machine Learning, and Natural Language Processing (NLP) among others.

Machine Learning Engineer

Machine Learning engineer specializes in using programming languages like Java, Python, and Scala to run various machine learning experiments in the appropriate libraries. Some of the major skills required for this profession include Probability, Programming, Statistics, Data Modeling, System Design, and Machine Learning Algorithms.

People often ask the question: How does a Machine Learning engineer differ from a Data Scientist?

Well, a Data Scientist specializes in analyzing data for purposes of producing actionable insights. The produced insights are then used for making business decisions. A Machine Learning engineer, on the other hand, analyzes data with the goal of creating machine learning algorithms that automatically run with minimal human input. Simply put, a Data Scientist creates input for human consumption while a Machine Learning engineer creates input for machine consumption.

Data Scientist

A Harvard Business review article described Data Science as the sexiest career in the 21st century – and that is an excellent incentive right there if you want to become a Data Scientist. As a Data Scientist, you will be using advanced analytics technologies like Predictive Modeling and Machine Learning for collecting, analyzing, and interpreting large datasets in order to produce actionable insights that will then be used for making business decisions

As you can see, Machine Learning is a very important skill for Data Scientist alongside other skills like knowledge of statistical research techniques and data mining. You will also need to master big data platforms and tools like Pig, Hadoop, Spark, and Hive. Additionally, you need to master programming languages like Python, SQL, Scala, and Perl.

Natural Language Processing (NLP) Scientist

Natural Language Processing involves training machines to understand the human language with the goal of enabling machines to communicate within humans in our own language. As a NLP scientist, you will work on creating a machine that is capable of learning speech patterns and translating spoken words into other languages. This calls for fluency in syntax, spelling, and grammar of at least one language in addition to machine learning so that you can transfer these skills to the machine you are working with.

Business Intelligence Developer

A Business Intelligence Developer specializes in combining Machine Learning with Data Analytics to collect, analyze, and interpret large sets of data with the goal of producing actionable insights that can be applied for making business decisions. Simply put, using data to make smart business decisions.

In order to do this, a Business Intelligence Developer should be knowledgeable in both relational as well as multidimensional databases as well as programming languages like Python, SQL, Perl, and Scala. Knowledge of various business analytics services like Power BI is also recommended. 

Human Centered Machine Learning

As the name suggests, this refers to Machine Learning algorithms that focus on humans. An example of this would be Netflix that provides users with movie recommendations based on their viewing behavior with the goal of creating a “smart” viewing experience. Human Centered Machine Learning Designer builds systems based on pattern recognition and information processing. This allows the machine to “master” human preferences without the need for cumbersome programs that manually tracks conceivable user scenario.

Getting Started with Your Career in Machine Learning: Tips for Success

Machine Learning, as already indicated, plays an important role in Artificial Intelligence. The process of training a machine to learn revolves around building programs and algorithms. This is achieved by the machine collecting data from numerous sources and “studying” it in order to execute a task. The collected data is meticulously studied to build a programming system.

So, how do you start a career in this exciting field?

Well, like with everything else, start small. Look out for low-hanging fruits and celebrate those little achievements. The most important thing is to keep on learning, not just for a couple of months but over the years. Here are a few things you can start doing right now to position yourself for a future career in machine learning.

Start off by understanding what machine learning really is

While this may seem straightforward, most people tend to ignore it. Before starting out your career, it is important that you have a clear understanding of what you are getting into. It is also important to self-evaluate. Do you enjoy learning with hands-on projects? Are you self-motivated? Can you set goals and bring them to fruition? If your answer is yes to these questions, you will definitely enjoy learning and pursuing a career in Machine Learning.

Be the curious type

Machine Learning and Artificial Intelligence are emerging technologies that are expected to continue evolving in the years to come. As such, a healthy sense of curiosity and passion for learning new stuff are crucial if you want to build a career in this field.

Master how to translate business challenges into mathematical terms

Machine Learning is designed for logical and cerebral minds with sound analytical and reasoning skills. As a machine learning engineer, you will blend math, technology, and business skills into one job. As such, you need to focus on technology a lot and develop a healthy intellectual curiosity. You should also possess openness toward business challenges and be able to interpret business problems into mathematical terms that make sense into end.

Learn teamwork

“I am great at managing products, not people, “is a remark commonly made by most team leaders. Well, this is flat wrong as far as Machine Learning go. While the term “Machine Learning” may convey the image of a lone geek surrounded by computers and machines, the truth is this is one of the fields that involve a lot of collaboration.

Find a mentor

One of the most effective, yet overlooked, ways of learning a new skill is through mentorships. Find a strong mentor from your network or platform that are learning ML from. To establish a stable career, you will need guidance and direction from an experienced-professional. This is one area where Udacity online learning platform excels in. Never overlook the importance of learning under the guidance of a knowledgeable and insightful  mentor. You will be pleasantly surprised by your success.

Have Data Analysis Skills

Data analytics is the building block of Machine Learning. Both Machine Learning and Data Analysis utilize somewhat identical skills like computational programming, data modeling, and mathematical statistics. In fact, studies have shown that data analysts make a perfect transition into career in machine learning. Remember, a Machine Learning role requires an analytical mindset that is capable of comprehending what works and what doesn’t.

Master Python and how to use Machine Learning Libraries

You do not need to be a programming guru to build a successful career in Machine Learning. For a successful learning journey, however, you need to identify the right coding language from the word go. It is important that you think strategically can set the right priority. According to industry experts, Python is the perfect coding language of choice for beginners in Machine Learning.

This minimalistic, yet intuitive programming language, comes with a full-featured library (also known as frameworks) that significantly cuts down the time needed to get your first results.

After mastering Python, you will comfortably launch into Machine Learning libraries. Most experts recommend TensorFlow and Scikit-lean libraries.

Sign up for an online course or attend a data science boot camp

Your ultimate goal is to broaden your Machine Learning related skill as much as possible. Some people have secured jobs after completing online courses like Machine Learning Nanodegree program from Udacity. To boost your opportunities and prospects, you need to move a step further. Taking up additional courses in Data Analytics, Data Science, Big Data, Python Programming, or Hadoop can go a long way in bolstering your Machine Learning knowledge and expertise levels. Moreover, consider participating in machine learning projects as well as online ML competitions can also strengthen your experience and practical skills.

Machine Learning engineer salary

Over to money matters. So, how much does a Machine Learning engineer earn? Well, a Machine Learning engineer’s salary typically falls between $106,500 and $130,700. The income ranges vary depending on multiple factors such as education, certifications, additional skills, as well as years of experience.

Conclusion

A Machine Learning career presents you with two hats: one as a data scientist and the other as a machine learning engineer. Acquiring competency in both fields can make you a hot commodity in the ever evolving digital market. It means you can collect and analyze tons of data, make sense of it, and use this information to train machine learning models that deliver accurate results. Machine Learning engineers often work in partnership with data scientists for better product synchronization.

What next?

Are you ready to learn Machine Learning and start your dream career at a top-notch organization? Well, get started with your learning journey today. We have put together several Machine Learning and related courses to help you get started without much hassle. Most of these online machine learning courses have been created in collaboration with top Universities so you can be sure that you will be getting value for your money. Furthemore, some of these courses have great discounts to ensure that you get the skills you need without breaking the bank! Browse through and get started with your learning today. 

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