AI &#038; Machine Learning Learning Path: A Definitive Guide
Artificial intelligence is currently one of the hottest buzzwords in tech — with good reason. In the last few years, we have seen several technologies previously in the realm of science fiction transform into reality. Experts look at artificial intelligence as a factor of production, that has the potential to introduce new sources of growth and change the way work is done across industries. In fact, AI technologies could increase labour productivity by 40% or more by 2035, according to a recent report by Accenture. This could double economic growth in 12 developed nations that continue to draw talented and experienced professionals to work in this field. According to Gartner’s 2019 CIO Agenda survey, the percentage of organizations adopting AI jumped from four to 14% between 2018 and 2019. Given the benefits that AI and machine learning (ML) enable in business analysis, risk assessment, and R&D — and, the resulting cost-savings — AI implementation will continue to rise in 2020. However, many organizations that adopt AI and machine learning don’t fully understand these technologies. In fact, Forbes points out that 40% of the European companies claiming to be ‘AI startups’ don’t use the technology at all. While the benefits of AI and ML are becoming more evident, businesses need to step up and hire people with the right skills to implement these technologies. Some are well on their way. KPMG’s recent survey of Global 500 companies shows that most of those surveyed expect their investment in AI-related talent to increase by 50 to 100% over the next three years. Why Pursue AI and Machine Learning Courses? As data science and AI industries continue to expand, more people are beginning to understand just how valuable it is to have a qualified AI Engineer or data scientist on their team. As a matter of fact, Indeed.com revealed that job postings for data scientists and AI rose over 29% between May 2018 and May 2019. Many people who want to get into this field, typically start with YouTube videos or other free online courses. This approach is definitely good to get your feet wet, but you can’t make a career jump based on these alone. What you need is to get a handle on the fundamentals of data science is experience through hands-on projects, where you get guidance from experts. These opportunities are not generally available in the workplace, especially if your current role does not involve data science. However, there are some excellent, comprehensive courses that you can enrol in, which will provide you with all of the above. Courses like Simplilearn’s Artificial Intelligence Engineer program enable you to learn, practice, and interact with expert instructors and peer, in live, online sessions. You don’t even have to travel. If you’re looking for a course that keeps students up to date on the latest trends in AI and machine learning through practical projects and industry expert-led instruction, Simplilearn’s AI Engineer and Machine Learning Certification courses are excellent options. There is no better time than now to get started, especially if you want to get ahead of your peers. Learning Path: How to Get an AI and Machine Learning Career Started Choosing a learning path for AI and machine learning training can be overwhelming due to all the options out there, but it’s ideal to choose a program that best suits your needs and goals. Successful data scientists usually have a thorough comprehension of various tools and programming languages. They also understand what their roles are in the grand scheme of things. With these skills, you can easily stand out from the competition with potential employers. Some of the programming languages include SAS, R, and Python. What you’ll need to know depends on different variables, such as the specific project you’re working on or the company you’re working for. In order to be a well-rounded candidate that can take on any type of project, it’s critical to know all three of these programming languages. Beyond that, it’s also helpful for data scientists to learn about AI and machine learning. When you enrol in an accredited data science learning program, you’ll get comprehensive training in the field. Let’s dig into some suggested learning paths for AI and machine learning to give you a better idea of what’s available and what to expect. Artificial Intelligence Engineer Master’s Program Simplilearn’s Artificial Intelligence Master’s Program, co-developed with IBM, is a blend of artificial intelligence, data science, machine learning, and deep learning — facilitating the real-world implementation of advanced tools and techniques. The program is designed to give you in-depth knowledge of AI concepts including the essentials of statistics (required for data science), Python programming, and machine learning. Through these courses, you will learn how to use Python libraries like NumPY, SciPy, Scikit; as well as essential machine learning techniques, such as supervised and unsupervised learning, advanced concepts covering artificial neural networks, layers of data abstraction, and the basics of TensorFlow. Next, let’s look at the courses that are included in this program, which can also be taken separately. Data Science with Python The Data Science with Python course provides students with all-around data science instruction that includes data visualization, machine learning, data analysis, and natural language processing using Python. As a data scientist, it’s crucial to add Python to your skillset, as more and more professionals in the industry are mastering this programming language. In fact, it has been reported that with seven million people now using Python, surpassing Java as the top programing language. This course is not only suited for those wishing to pursue a career as a data scientist but can also be beneficial for anyone looking to work in data analytics or software development. Machine Learning As a data scientist, mastering machine learning is often a requirement, and the best way to do so is by enrolling in an accredited learning program and earning a machine learning certification. Although there are free online learning sources and tutorials, such as blogs and YouTube videos, these unstructured learning methods don’t always cover all aspects of ML. Also, self-learners may not be able to stay up-to-date on industry changes or receive certifications. Through Simplilearn’s machine learning course, students are introduced to various techniques and concepts, such as mathematical and heuristic aspects, supervised and unsupervised learning, algorithm development, and hands-on modelling. This course is ideal for those who want to add to their skill set as a data scientist, or for those who wish to pursue a career as a machine learning engineer. Deep Learning with TensorFlow Deep learning is one of the most exciting and promising segments of artificial intelligence and machine learning technologies. Simplilearn’s Deep Learning with TensorFlow and Keras course is designed to help you master key deep learning techniques. You’ll learn how to build deep learning models using TensorFlow, the open-source software library developed by Google to conduct machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are already showing up in smartphone applications and efficient power grids. The technology is also driving innovations in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks, and how to interpret the results. Natural Language Processing (NLP) Simplilearn’s NLP course gives you a detailed look at the science of applying machine learning algorithms to process large amounts of natural language data. You will learn the concepts of statistical machine translation and neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, deep reinforcement learning techniques, neural models applied in image captioning, and visual question answering using Python’s Natural Language Toolkit (NLTK). AI Capstone Project Simplilearn’s Artificial Intelligence (AI) Capstone project gives you the opportunity to implement the skills you learned in the AI Engineer Master’s program. With dedicated mentoring sessions, you’ll learn how to solve a real industry problem. You’ll also learn various AI-based supervised and unsupervised techniques like regression, multinomial Naïve Bayes, SVM, tree-based algorithms, NLP, etc. The project is the final step in the learning path and will help you to showcase your expertise to potential employers. Bottom Line There is no denying that the job market is competitive. In fact, the Bureau of Labor Statistics recently released a report that reveals how the job market is tightening. If you’re looking for a stable industry that isn’t going anywhere anytime soon, AI and machine learning are excellent choices. However, choosing a growing and successful industry is only half the battle when it comes to job security. There is also a competition to consider — oftentimes, many qualified candidates are vying for the same job opening. One of the best ways to ensure you stand out to recruiters and employers is to have the right credentials. Earning your certifications in AI and machine learning, or other relevant fields is a surefire way to get your resume noticed by the right people. Get started today!
Artificial intelligence is currently one of the hottest buzzwords in tech — with good reason. In the last few years, we have seen several technologies previously in the realm of science fiction transform into reality. Experts look at artificial intelligence as a factor of production, that has the potential to introduce new sources of growth […]
AI is a factor of production that has the potential to introduce new sources of growth and change the way work is done across industries.
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