How to Become a Machine Learning Expert: 2024 Guide

To learn Machine Learning step-by-step, and secure a job in Machine Learning, follow these easy steps below:

• Consider the prerequisites
• Understand Machine Learning concepts
• Work on projects
• Familiarize yourself with Machine Learning(ML) tools
• Enroll in Machine Learning certifications
• Apply for internships
• Secure a job
   

Consider the Prerequisites

Before getting started with the Machine Learning journey, it is important to start learning the essential concepts. The essential prerequisites of Machine Learning include

• Statistics and Probability: Statistics and Probability is an important aspect of Machine Learning as it is focused on data and its predictions. 
   
• Programming: Machine learning algorithms are implemented in code, which is why programming languages like Python, R, Java, etc., are some of the most important programming languages that are also essential prerequisites. 
   
• Mathematics: Mathematics skills are important as Machine Learning heavily relies on both linear algebra and calculus. Fundamental algorithms for handling datasets are constructed upon linear algebra's basis, encompassing vector spaces, matrices, linear transforms, tensor concepts, and matrix multiplication.
   
• Data Tackling: Proficiency in data manipulation can significantly enhance your code's efficiency in terms of both memory usage and processing time. Prior to modeling data analysis, data cleaning (wrangling) and assessing the overall dataset structure are essential steps to undertake.

Understand Essential Machine Learning Concepts

After the Machine Learning prerequisites, you can start learning the Machine Learning concepts. The Machine Learning concepts include:

• Machine Learning terminologies, which include model, feature, target, training, prediction, etc.
• Types of Machine Learning include supervised learning, unsupervised learning, semi-supervised and reinforcement learning.

Familiarize Yourself with Machine Learning(ML) Tools

The current market offers many machine learning tools and packages, covering tasks from data collection to model deployment. For beginners, it's wise to start with established and reliable software known for their capabilities. While tool selection can vary based on your specific model and goals, many proven tools excel across most use cases. Familiarizing yourself with these tools is a part of becoming a Machine Learning Expert.

Enroll in Machine Learning Certifications

Machine Learning certifications are one of the best ways to understand everything from scratch. Even when you want to progress in your career, advanced level Machine Learning certifications will help you develop all the essential skills. Some industry-recognized certifications offer internship and job opportunities after completion, including a boost to your resume.

Work on Projects

Engaging in personal projects is essential for every aspiring Machine Learning Engineer. In the competitive realm of machine learning, tangible experience is crucial to distinguish yourself from numerous inexperienced candidates. These projects will later add value to your resume as prospective employers seek individuals who can showcase hands-on proficiency in Machine Learning tools and applications.
 

Apply for Internships

While pursuing Machine Learning certifications, you can simultaneously seek out internships to apply your learning in practical settings. Internships offer a valuable avenue for gaining experience and putting your skills into action as you acquire them. Employers frequently prefer freshers with practical experience, making internships an excellent way to fulfill this expectation.

Secure a Job

You can look for entry-level data science jobs to start your career in this field. As you gain more experience and skills, you can apply for companies that are you most interested in. Make sure you demonstrate your skills and experience in the best way possible for the recruiters.

Machine Learning is becoming increasingly widespread and is used in almost every field, such as medicine, cybersecurity, automobiles, etc. ML engineering integrates software engineering principles with analytical and data science knowledge to make an ML model. Thus, having a slate of data scientist skills and software engineering is important. Gaining industry-relevant machine-learning skills and learning machine-learning techniques is a wise career move. 

When it comes to gaining new skills and upskilling, the best accessible method is to go for online courses. To pick the right option among so many available courses on the internet, you can leverage Careervira to compare different courses based on various factors and then pick the one suitable for you.

Below-mentioned is the top technical machine learning skills needed to become a ML Engineer or an expert:

• Programming Skills
  You should be well versed in programming languages such as Python and R for Machine Learning and statistics, Apache Spark and Hadoop for distributed computing, SQL for database management, Apache Kafka for data pre-processing, etc.
  • Python is one commonly used programming language in many fields; machine learning engineers mostly prefer it. It supports machine learning libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.
  • R programming language is developed for statistical computing and data mining, and these features make it a perfect fit for machine learning tasks.
  • C/C++ is used to program the interface and develop machine learning mechanisms. It is mostly used to develop embedded systems. There are specialized online programming courses that help learn it from scratch and even help learners gain programming skills centric to machine learning.
     
• Statistics
  Its related branches, including analysis of variance and hypothesis testing, are critical for building algorithms. So it is necessary to gain knowledge of statistical tools to advance your machine learning career.
   
• Data Modeling
  The critical task of machine learning is to analyze unstructured data models, which requires the science of data modeling. Having in-depth knowledge of data modeling concepts helps in creating efficient algorithms.
   
• Machine Learning Algorithm
  Mostly, ML algorithms are divided into - supervised, unsupervised, and reinforcement Machine Learning Algorithms. Some common ones include Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc. So it’s good to know all these algorithms.
   
• Additional Skills
  Many machine learning professionals are acquainted with deep learning, data visualization, neural network architectures, natural language processing (NLP), reinforcement learning, advanced signal processing techniques, etc. To constantly upgrade their skills and be aligned with professional development, learners can enroll in niche specializations such as online deep learning courses, NLP certification courses, etc.

Below-mentioned are the essential soft skills required to excel in a machine learning career:

• Communication Skills
  If you are good at communication skills, it can improve your career trajectory. Because while you may understand the data and the insights, it is equally significant to communicate these insights to non-technical members, shareholders, or clients.
   
• Problem-Solving Skills
  ML focuses on solving real-time challenges, so thinking critically and creatively about issues that arise and developing solutions is a foundational skill.
   
• Time Management
  ML handles demands from different stakeholders while performing research, organizing and planning projects, designing software, and testing it rigorously. Managing one’s time is key to making valuable contributions to the team.
   
• Teamwork
  The ability to collaborate and contribute is a skill many managers look for when hiring machine learning experts.
   
• Learning Ability
  Whether it’s learning the latest programming languages, mastering new tools or programs, or studying the latest breakthrough techniques and technologies, the most effective machine learning engineer jobs require constant refreshing of toolkits and the ability to learn new skills.

If you want to pursue your goal of becoming a machine learning expert or specialist. There are two alternative paths you can take:

E-Learning Path

This can be achieved by taking machine learning boot camp, ML online certification courses, or opting for free crash courses that will equip you with relevant skills. 

• Advantages:
  • Online courses are especially beneficial for those who do not have degrees in Computer Science, Statistics, Mathematics, or fields related to data science and technology but wish to dive deeper into this field.
  • Offers flexible schedule facilitating learning for working professionals
  • Virtual learning provides the option to complete the course from anywhere in the world.
  • More affordable option
     
• Disadvantages:
  • Course structure is less flexible than the regular classroom, since learners may not be able to clear doubts in real-time.
  • As it is a quant-heavy field, the instructor may have to explain a topic again for your understanding. However, this is not as easy online.
  • A flexible schedule makes it important to have the self-discipline to finish the course and practice the skills you learn.
  • No competition, so cannot compare skills or verify knowledge level.

Traditional Path

The offline approach is perfect for people who are looking to gain a master’s or bachelor’s degree in Data Science and Machine Learning and want a complete college experience.

• Advantages:
  • Face-to-face interaction provides the option to ask the instructor questions in real-time and make sure you are not facing trouble with coding.
  • The timings and schedules are rigid, so it is important to finish the course within an externally set deadline.
  • It allows to work on interpersonal skills as well as assess skills in a competitive environment.
     
• Disadvantages:
  • Requires studying at a university or institute physically, which will not give the flexibility to attend classes from anywhere in the world.
  • It arises more expenses related to transportation and accommodation amongst other things, making this economically unfeasible for many.
  • Since the timings cannot be tailored to suit your needs, you may have to shift your schedule to make the most out of offline classes.

We suggest going for an online machine learning course or a series of online classes since the advantages of ML outweigh the disadvantages. It is more convenient, cheaper, and less time-consuming and equips you with the same skills an offline course will. Moreover, you can pursue one of the bachelor’s or master’s degrees online for the same online too. 

Yes, machine learning can be easy to learn with the help of various online course options available. But it can be overwhelming to pick the best one for you among so many course providers. So to ease this process, we have analyzed different courses offered across different platforms.

Here are the top 5 picks available for machine learning courses at Careervira:

• Machine Learning With Python by Coursera
  It is offered by Coursera in collaboration with IBM, which focuses on the purpose of machine learning, and how it applies to the real world. Learners gain insight into common topics such as supervised and unsupervised learning, how to use powerful ML algorithms, and what’s involved in “model evaluation”. To enroll in this course, you do not need any existing machine learning knowledge, but you will need some experience in data science.
   
• Data Science: Machine Learning by edX
  This course provides a hands-on learning experience to learners who want to build their ML algorithms. It covers all of the basics of machine learning algorithms and applications. To enroll in this course, you don’t need a lot of substantial prior knowledge in machine learning. Also, it is part of a comprehensive Professional Certificate Program in data science.
   
• Machine Learning with Python by One education
  This course helps learners gain an in-depth understanding of implementing ML models in Python. Whether you are an existing practitioner or an aspiring expert, this course enhances your expertise and boosts your resume with key skills. To enroll in this course, no formal entry requirements are there. All you need is a good understanding of the English language, numeracy and IT.
   
• Intro to Machine Learning with PyTorch by Udacity
  This course helps learners understand foundational machine-learning techniques, including data manipulation, creating products and deploying machine-learning models to a production environment, such as a web application. It teaches both the theoretical and the practical aspects of machine learning.
   
• Machine Learning Specialization by Coursera
  This course helps learners develop a strong foundation in basic concepts such as Regression algorithms, Clustering algorithms, Classification algorithms, and Information Retrieval. It helps them prepare for the machine learning scientist or engineer role through hands-on experience with open-source tools such as TensorFlow, Sci-kit-learn, and Keras.

The time taken for learning Machine Learning can vary, and it takes anywhere from 3 months(basics) to 6 years to become an expert in ML. Machine Learning is a constantly evolving field, which means there is no end to learning.

Here are some of the factors that affect the time taken for learning Machine Learning:

• Previous experience and knowledge
  If you have any experience or previous knowledge of this field - including data science and artificial intelligence - then understanding or getting started with Machine Learning will be comparatively easier and hence faster learning.
   
• Amount of time you spend learning
  If you are learning Machine Learning on your own or you are choosing a self-paced course, then it depends upon how many hours you spend daily on learning or building skills. It will eventually also depend on your retaining power.
   
• Learning style
  Machine Learning is better learned with projects and hands-on experience. If you are doing internships or entry-level jobs during your studying, you might have a better chance of understanding and grasping complex concepts.

There are multiple different machine learning jobs or career paths within the machine learning field that can lead to a fulfilling career. Machine learning online courses and certifications are two ways learners can develop the skills to succeed in this field. With the right machine learning courses, learners develop their understanding of all kinds of complex ML concepts, demonstrate their understanding of machine learning languages like Python, develop skills with common ML and AI algorithms, build machine learning projects or develop a strong portfolio for future career opportunities, etc.

Apart from mastering the foundations of machine learning, it is also important to stay updated with Machine Learning trends. As the field evolves, so do the methodologies and techniques driving it forward. Exploring emerging trends such as federated learning, explainable AI, and ethical considerations in AI development can provide learners with a competitive edge and prepare them for the future job prospects.