Remember the cool filters that turn you into a cat on Snapchat? Or the art generator Dall-E that creates images based on simple input? AI is behind those too.

Maybe you’ve heard these stories and thought “AI sounds amazing, but it’s WAY too complicated for me.” Here’s a secret: you don’t need to be a tech wiz to understand AI.

With the right guidance, anyone can embark on an AI learning journey, starting from scratch. Imagine going from “AI? Huh?” to using it to solve real-world problems in Nepal. This is possible with the Fusemachines AI Fellowship Program.

This blog is your invitation to explore AI, overcome your doubts, and discover how you can turn your curiosity into valuable skills. So, put on your learning hat, get ready for a fun ride, and let’s begin your transformation from beginner to expert!

From Curious Newbie to Foundational Fighter

Remember feeling nervous and excited on your first day of school? Imagine starting an exciting new adventure, but instead of textbooks and classrooms, you’re surrounded by supportive peers and industry experts, all geared towards one goal: unlocking the world of AI.

That’s exactly what the initial phase of the Fusemachines AI Fellowship feels like. We know diving into AI can be overwhelming, so we start with building a strong foundation. Think of it as climbing a ladder, but each step equips you with the knowledge and skills to confidently take the next.

Here’s what “beginner’s steps” look like:

Building the basics: Remember building Lego castles as a kid? We start similarly, but instead of colorful bricks, we work with the building blocks of AI: programming basics (if needed), essential math concepts, and the core principles of machine learning. Don’t worry, even if you’re new to these terms, our experts will guide you patiently, step-by-step.

Learning by doing: Memorizing facts is cool, but applying them is even cooler! That’s why the program integrates hands-on projects from the very beginning. Imagine building your own mini AI program or analyzing real-world data—all while having experts by your side to answer your questions and celebrate your progress.

A supportive community: Remember those nervous first-day jitters? Well, forget them! You’ll be surrounded by fellow AI enthusiasts, just like you, creating a supportive learning environment. Ask questions, share ideas, and learn from each other.

This initial phase might seem basic, but trust us, it’s crucial. You’ll be amazed at how quickly you progress from a complete beginner to someone confidently navigating the fascinating world of AI. And that’s just the beginning of your incredible journey!

From Foundations to Future-Ready: Shaping Your AI Journey

The next step in your AI journey takes you beyond fundamentals as you begin crafting your own personalized skillset, tailored to address Nepal’s unique needs and opportunities.

This phase empowers you to:

Dive deeper into AI concepts: Explore areas that fascinate you, be it natural language processing, computer vision, or machine learning algorithms.

Shape your learning path: Collaborate with mentors and peers to design projects that challenge and inspire you, applying your skills to problems you find meaningful.

Develop a Nepal-focused perspective: Gain insights into the country’s unique challenges and opportunities, ensuring your AI knowledge has direct relevance and impact.

Engage in hands-on projects: Tackle real-world challenges alongside organizations and communities in Nepal, gaining practical experience and making a difference.

Receive expert guidance: Learn from industry professionals who share their knowledge and help you navigate the world of AI.

Build a supportive network: Connect with like-minded individuals and experts, fostering collaboration and ongoing learning within the AI community.

By the end of this phase, you won’t just be an AI enthusiast; you’ll be a future-ready AI practitioner, equipped with the tools, skills, and Nepal-focused perspective to:

• Contribute to innovative solutions for Nepal’s challenges.
• Become a leader in shaping the future of AI in your community.
• Embark on a fulfilling career that makes a lasting impact.

Transformation and Expertise: Your AI Journey Starts Now

Ready to transform your curiosity into AI expertise and contribute to a brighter future for Nepal? Imagine yourself joining a supportive community of aspiring AI professionals, guided by industry experts, and equipped with the skills to tackle real-world challenges. This is the transformative power of the Fusemachines AI Fellowship Program.

More than just learning AI:

The program goes beyond teaching technical skills. It fosters:

Problem-solving mindset: Identify and analyze real-world challenges unique to Nepal, developing innovative AI solutions that make a tangible impact.

Critical thinking and innovation: Push the boundaries of AI applications, exploring creative solutions tailored to your community’s needs.

Growth mindset: Embrace continuous learning and adapt to the ever-evolving world of AI, becoming a lifelong learner and leader in the field.

Your transformation story can begin today. Applications for the next cohort of the Fusemachines AI Fellowship Program are now open! Apply now!

Here’s your chance to:

Learn from the best: Gain mentorship from industry experts and renowned faculty, acquiring practical knowledge and industry insights.

Collaborate with peers: Join a vibrant community of aspiring AI professionals, fostering learning, support, and lifelong connections.

Tackle real-world projects: Apply your skills to hands-on projects, addressing Nepal-specific challenges and making a difference in your community.

Bottom line

Don’t just read about transformation, embrace it. Join the Fusemachines AI Fellowship and unlock your potential to impact Nepal’s future. Apply now, applications close on [date]. 

The post From Beginner to Expert: Your AI Learning Journey appeared first on Fuse AI.

Whether you’re a beginner looking to grasp the fundamentals or an experienced practitioner aiming to specialize in a specific domain, there are AI courses tailored to meet your needs. From introductory courses covering machine learning and algorithms to advanced programs focusing on natural language processing and computer vision, our programs uncover the diverse range of options available.

Fusemachines offers job-ready AI training, including live sessions and hands-on projects, while building an understanding of AI at a conceptual and technical level. You will also learn to evaluate the benefits and risks of the technology and strategies for mitigating risks and maximizing benefits.

Types of AI courses that can help develop your AI skills

Fundamental Courses: These courses provide a solid foundation in AI concepts and principles. They cover introductory topics such as basic machine learning algorithms, data analysis, and AI frameworks. Fundamental courses are suitable for beginners who want to grasp the fundamental concepts of AI before diving into more technical aspects.

Technical Courses: Technical AI courses dive deeper into advanced algorithms, models, and techniques. They focus on specific areas such as deep learning, natural language processing (NLP), computer vision, and reinforcement learning. These courses are geared toward individuals who already have a basic understanding of AI and want to develop specialized technical skills.

Business and Integration Courses: These courses bridge the gap between AI and business. They explore how AI can be integrated into various industries and sectors, providing insights into AI strategy, implementation, and ethical considerations. Business and integration courses are designed for professionals who want to understand the practical implications of AI and its impact on organizational decision-making.

Specialization Courses: Specialization courses allow individuals to dive deep into a particular aspect of AI. They focus on specific applications, such as AI for healthcare, finance, robotics, or autonomous vehicles. These courses enable learners to gain in-depth knowledge and expertise in a specialized domain of AI, making them suitable for individuals who want to specialize and excel in a specific field.

Courses offered by Fusemachines

Some of the popular courses offered by Fusemachines are AI2Go, Foundations in AI Program, AI for Professionals, Microdegree, and Microdegree Specializations. If you want to be a part of comprehensive programming combining these all Fusemachines AI Fellowship Program might just be for you.

Introducing Fusemachines AI Fellowship Program 2023

The AI Fellowship Program is a transformative educational initiative offered by Fusemachines currently in Latin America, designed to equip individuals with the knowledge, skills, and practical experience needed to thrive in the field of AI. With a curriculum designed by industry experts, participants will delve into various AI concepts, including machine learning, generative AI, natural language processing, computer vision and more.

The program offers a full scholarship, allowing students to access high-quality education without any financial burden. Through hands-on projects and mentorship from AI professionals, fellows will acquire the necessary skills to excel in this rapidly evolving industry. Additionally, networking opportunities and potential job placements further enhance the career prospects of program graduates.

Join the AI Fellowship Program and unlock a world of exciting new career opportunities in AI. Apply Now. 

Why learn and develop AI skills?

AI has a proven history of impact in almost every industry, with results that deliver a competitive advantage. AI adds value to current processes and provides insights on how to innovate and create new processes. 

An introductory course in AI will give students an overview of the essential components of AI. AI jobs are lucrative and there are many great opportunities to choose from. Courses will give you the latest knowledge on AI research and development. You can learn examples of AI in use today, such as self-driving cars, facial recognition, military drones, and advanced natural language processors.

Growth in AI-related job sectors is versatile. The average salary ranges from $100,000 to $160,000 in the United States. AI improves user experiences that bolster ROI. AI has many applications in industries, from farming to healthcare and emergency relief.

Join the Fusemachines AI Fellowship Program for Latin America and unlock a world of exciting new career opportunities in AI. Apply Now. 

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Data refers to machine-readable information in computing and business. Due to large amounts of user-generated data, also known as “big data,” traditional data management technology is incapable of storing and managing it. As such, big data is complex and comes in different forms, such as structured, unstructured, and semi-structured. 

Because regular data warehouses aren’t capable of processing and analyzing big data, platforms such as Spark, Hadoop, NoSQL databases, have emerged to help enable businesses to collect and set up data ponds as repositories. However, simply collecting and managing data directories isn’t enough to gain business value, and conventional data analytics don’t tap into all the benefits of big data.     

This is where Machine Learning (ML) comes in. Able to spot patterns and manage large amounts of data, Machine Learning takes data analytics to the next level, allowing organizations to extract more value from their data. 

As you plan your career, it is important to understand both the differences between big data and machine learning and where they converge. 

Defining Big Data 

Big Data is information or statistics acquired by large ventures and organizations. What qualifies as “big data” however, varies depending on the skills and tools of those analyzing it. Additionally, due to its magnitude, it is difficult to compute big data manually, and data analysts and scientists tend to categorize it into “columns” based on type and source.

Similarly, data analysts use big data to extract information systematically, identify trends, patterns, and human behavior to make decisions. In order to make good decisions, one has to not only make the best guess about what is going on, but also the best estimate of what will happen in the future. We do this all the time when we predict what other people will do in certain situations, often by identifying repeated behavior patterns.

Likewise, data with many columns offer greater statistical power but is prone to false discovery rates. To boot, expanding capabilities also make big data a moving target. In other words, raw data is constantly being produced, expanding the volume of big data, thus making it harder to make concrete predictions as a result.  

Furthermore, the availability of user-generated data has also grown exponentially with the use of smartphones, Internet of Things (IoT) devices, software logs, cameras, microphones, radio-frequency identification (RFID), and wireless sensor networks. International Data Group Inc. (IDC) predicted that the global data volume would grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. IDC also predicts that by 2025, there will be 163 zettabytes of data.  

Defining Machine Learning 

A subset of Artificial Intelligence, ML extracts knowledge from data and improves and learns from experience without intervention. In other words, through algorithms and training, ML models process data and deliver predictions. Additionally, many applications also use ML, from medicine and e-mail filtering to speech recognition and Computer Vision (CV).

Now, Artificial Intelligence and Machine Learning are often used interchangeably but are not the same. To read more about the subsets of Machine Learning and how it differs from Artificial Intelligence, read our blog: AI vs. ML – Difference Between Artificial Intelligence and Machine Learning.  

How are Machine Learning and Big Data Related? 

Machine Learning vs Big Data aren’t competing concepts. What’s more, they are not mutually exclusive either. In fact, their combination provides impressive results. On one hand, data analysts feed ML algorithms big data, and the algorithm analyses its potential value. On the other, ML tools use such data-driven algorithms and statistical models to put together data sets. The ML model then draws inferences from identified patterns and makes predictions based on these patterns.  

Comprising ample amounts of raw data, big data correspondingly gives ML systems plenty of materials to derive insights from. In like manner, effective big data management also consequently improves Machine Learning as large quantities of high-quality, relevant data make ML models successful. At the same time, data scientists who create these ML models simultaneously provide a way to manage big data.  

A good example is Netflix’s ML algorithms that understand individual viewing preferences to provide recommendations. Similarly, Google also uses Machine Learning to provide personalized experiences, not only for its search function but also for predictive text in Gmail. Google Maps too, uses ML to give users the best directions. 

How are Machine Learning and Big Data Different? 

The primary focus of data science is data visualization and better presentation. Machine Learning, on the other hand, focuses on learning algorithms and from real-time experience. Thus, for data science, data is the main focus, and for Machine Learning, learning is the main focus. And this is where the difference lies. Given below are key differences between ML and Big Data: 

Which should you choose?

When it comes to Machine Learning vs Big Data, both go hand-in-hand. Hence, familiarity with both is ideal. Comparatively, both fields offer competitive job opportunities and are in high demand. Moreover, professionals in both fields also enjoy similar remuneration packages. Thus, if you have skills in both areas, you will be an essential asset.  

To summarize, choosing Machine Learning vs Big Data depends on your interests. Basically, user-generated data is growing at a fast pace and will continue to grow as time goes on. As a result, the need for data scientists, ML engineers, and other data management and analytics professionals will also increase as more companies opt into big data, Machine Learning, and data visualization tools. Conversely, companies that don’t combine big data and Machine Learning will be left behind. 

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field. 

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“AI is a concept bigger than ML, used to create intelligent machines capable of simulating human thinking capability and behavior. Machine Learning, on the other hand, is an application or subset of AI that enables machines to learn from data without being explicitly programmed. In other words, AI is the all-encompassing concept that initially erupted, followed by ML that thrived after.” 

AI vs ML – Major Differences (and Overview)

Artificial Intelligence

Artificial Intelligence studies methods to build intelligent programs and machines to creatively solve problems. It has always been considered the human prerogative.

As such, AI aims to build computer systems that mimic human intelligence. The term “Artificial Intelligence”, thus, refers to the ability of a computer or a machine to imitate intelligent behavior and perform human-like tasks.

Likewise, these tasks include actions such as thinking, reasoning, learning from experience, and most importantly, making decisions. AI systems do not require pre-programming. Rather, they use algorithms.

There are many well-known examples of AI, such as Siri, Google’s AlphaGo, and AI in Chess playing, among many others. 

AI Classification

 AI is classified into three types based on capabilities: Weak AI, General AI, and Strong AI.   

Artificial Narrow Intelligence (ANI) or Weak AI 

Weak, or Narrow AI, performs particular tasks but is incapable of passing as a human outside its defined capacities. Most AI in use today is categorized as Weak AI. It is widely used in science, business, and healthcare.

One of the earliest examples of Weak AI is Deep Blue, the first computer to defeat a human in chess. (Not just any human either: Deep Blue defeated Garry Kasparov in 1996). Another good example of Weak AI is bots. 

Bots are software capable of running simple, repetitive, and automated tasks, such as providing answers to questions such as, “How is the weather?” or “What are some good burger restaurants near me?” Bots pull data from larger systems, such as weather sites or restaurant recommendation engines, and deliver the answer. 

Artificial General Intelligence (AGI) 

Artificial General Intelligence systems perform tasks that humans can with higher efficacy, but only for a particular/single assigned function. They are incapable of doing tasks not assigned to them.

Thus, AGI systems can make decisions and learn without human input. Engineers program AGI machines to produce emotional verbal reactions in response to various stimuli. Examples include chatbots and virtual assistants capable of maintaining a conversation. 

Artificial Super Intelligence (ASI) or Strong AI

Strong Artificial Intelligence is the theoretical next step after General AI, perhaps more intelligent than humans. Right now, AI can perform tasks, but they are not capable of interacting with people emotionally.

Additionally, if you want to know more about AI and its subsets, you can check this blog: What is AI? Artificial Intelligence and its Subsets.

Machine Learning (ML)

Machine Learning is a subset of Artificial Intelligence that deals with extracting knowledge from data to provide systems the ability to automatically learn and improve from experience without being programmed. In other words, ML is the study of algorithms and computer models machines use to perform given tasks. 

There are different types of algorithms in ML, such as neural networks, that help solve problems. These algorithms are capable of training models, evaluating performance and accuracy, and making predictions. Furthermore, ML algorithms use structured and semi-structured data. They also learn on their own using historical data. 

The “learning” in ML refers to a machine’s ability to learn based on data. You can say that ML is a method of creating AI. Additionally, ML systems also recognize patterns and make profitable predictions.

Many fields use Machine Learning, such as the Online Recommender System, the Google Search Algorithm, Email Spam Filters, and Facebook Auto Friend Tagging Suggestion.

Components of Machine Learning 

Datasets: ML Engineers train systems on special collections of samples called datasets. The samples can include texts, images, numbers, or any other kind of data. Usually, it takes a lot of time and effort to create a good dataset.

Features: Features are important pieces of data that function as key components to the solution of tasks. Features demonstrate to the machine what to pay attention to.

Algorithm: Machine Learning algorithms are programs, like math or logic. An algorithm can adjust itself to better performance when exposed to more data. It is a procedure that runs on data to create a machine learning model. Essentially, they perform pattern recognition. Similarly, the accuracy or speed of getting results differs from one ML model to the next depending on the algorithm. 

When it comes to performing specific tasks, software that uses ML is more independent than ones that follow manually encoded instructions. An ML-powered system can be better at tasks than humans when fed a high-quality dataset and the right features. 

Types of Machine Learning 

Supervised Learning

In Supervised Learning, an ML Engineer supervises the program throughout the training process using a labeled training dataset. This type of learning is commonly used for regression and classification. 

Examples of Supervised ML include Decision Trees, Logistic Regression, Naive Bayes, Support Vector Machines, K-Nearest Neighbours, Linear, and Polynomial Regression. 

Hence, Supervised ML is commonly used for language detection, spam filtering, computer vision, search, and classification. 

Semi-Supervised Learning

Semi-Supervised Learning uses a mixture of labeled and unlabeled samples of input data. In this process, the programmers include the desired prediction outcome. The ML model must then find patterns to structure the data and make predictions.

Unsupervised Learning

In Unsupervised Learning, engineers and programmers don’t provide features. Rather, the model searches for patterns independently. Therefore, this type of ML is good for insightful data analytics. The program can recognize patterns humans would miss because of our inability to process large amounts of numerical data. 

That being so, UL can be used to analyze customer preferences based on search history, find fraudulent transactions, and forecast sales and discounts. Examples include K-Means Clustering, Mean-Shift, Singular Value Decomposition (SVD), DBSCAN, Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis, and FP-Growth. 

Accordingly, engineers commonly use them for data segmentation, anomaly detection, recommendation systems, risk management systems, and fake images analysis.

Reinforcement Learning

Finally, Reinforcement Learning is an ML training method formulated on rewarding desired behaviors, and/or punishing undesired ones. This is very similar to how humans learn: through trial. A reinforcement learning model is capable of perceiving and interpreting its environment, taking actions, and learning through trial and error. 

Furthermore, RL allows engineers and programmers to step away from training on static datasets. Instead, the computer is capable of learning in dynamic environments, such as in video games and the real world. Reinforcement learning works well in in-game research as they provide data-rich environments. 

Some examples include Q-Learning, SARSA, Genetic algorithm, DQN, and A3C. As such, engineers and programmers commonly use them for self-driving cars, games, robots, and resource management.

AI vs ML – Key Differences

We use AI to resolve tasks that require human intelligence. ML, on the other hand, is a subset of AI that solves specific tasks by learning from data and making predictions. For this reason, you can say that all Machine Learning is AI, but not all AI is Machine Learning.

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. Likewise, the proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these help engineers become leading AI industry experts, aiding them in achieving a fulfilling and ever-growing career in the field.

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How to Become an AI Engineer

There are many ways to become an AI developer. Most, if not all, AI jobs require an analytical thought process. A key characteristic is the ability to solve problems with cost-effective and efficient solutions. AI Engineers should be aware that technological innovations need to be able to translate to state-of-the-art programs. 

To elaborate, let’s dive into innovations. It is apparent, initial product innovation first needs an introduction of a new product. You cannot, however, introduce just any new product. The new product must solve an existing problem in an exciting and modern way. Or the product needs to solve a completely new problem that has come to light.

For example, at the 2016 Consumer Electronics Show, the electronics company LG introduced a new type of flexible screen that you can roll up like a newspaper. When you first hear it, it sounds like science fiction. But it’s not in the realm of fiction anymore, it’s an actual real product that could become readily available to everybody in a short time. So what’s innovative about this, you ask? This innovative product solves the portability problem- instead of large, unwieldy screens, people can instead show videos on screens they can fold up when they are done and put in their bags. 

New innovative products can introduce new technologies or new ways to do something. And this is what AI Engineers need to be aware of- translating technological innovations into tangible practical products. 

The following topics provide an understanding of the prerequisites that you must obtain to get the AI job of your dreams.  You can also check this blog that elaborates on what AI Engineering is- What is AI Engineering. If you are hoping to begin a career as an AI Engineer, here are the requirements. 

Education Prerequisites Required to be an AI Engineer 

You first need to earn a bachelor’s degree to become an AI Engineer. You can earn a degree in the following subjects:

• Computer Science 
• Mathematics 
• Information Technology 
• Statistics 
• Finance 
• Economics 
• Data Science
• Cognitive Science

You can also take additional courses and pursue relevant certifications. There are many online courses and certifications created to further your AI knowledge and skills. For example, you might consider taking the AI2GO programming course by Fusemachines, an online course designed for individuals to learn about AI and its applications with no computer background knowledge necessary. 

Technical Skills Required to Become an AI Engineer

You will need in-depth knowledge of various technical skills to be a successful AI Engineer. You must know and use various software development techniques and practices, along with programming skills. As an AI Engineer, you will have a set of responsibilities within your workplace. To know more about the responsibilities of an AI Engineer, you can check out this article- What are the Responsibilities of an AI Engineer? 

Make sure you have a firm grasp of the following topics: 

• Programming Languages
• Statistical Knowledge
• Applied Maths and Algorithms
• Natural Language Processing
• Deep Learning and Neural Networks

Programming Skills 

The first skill you need to become an AI Engineer is programming. Learning programming languages such as Python, R, Java, and C++ is a must to build and implement models. It is also good to know about classes and data structures. In addition, you might encounter projects where you need to leverage hardware knowledge. Thus, you must be familiar with basic algorithms, classes, memory management, and linking. 

Applied Math and Statistical Skills

Technical and statistical skills include matrices, vectors, and matrix multiplication. To understand and implement AI models, you must know Linear Algebra, Probability, and Statistics. A good understanding of derivatives and integrals is necessary as well. Statistics are empirical, and tend to come up a lot. Make sure you are familiar with Gaussian Distributions, Means, and Standard Deviations as they are the fundamentals of AI Engineering. You must also have a firm understanding of Probability as this can help you understand models such as:

• Gaussian Mixture Models
• Naive Bayes
• Hidden Markov Models

The next technical skill you need is in-depth knowledge of algorithm theory and how algorithms work. To completely grasp and understand AI, you will need to know subjects such as the Gradient Descent, Quadratic Programming, Partial Differential Equations, Lagrange, and so on. Unlike front-end development, Machine Learning and Artificial Intelligence are much more math-intensive.  

Natural Language Processing Skills 

NLP incorporates two major fragments of Machine Learning and Artificial Intelligence: Linguistics and Computer Science. As an AI Engineer, the possibility of working with either text, audio, or video is highly probable. Therefore, it is important to have a good grasp of libraries such as Gensim, NLTK, and techniques such as word2vec, Sentimental Analysis, and Summarization. 

Deep Learning and Neural Networks Skills 

While working as an AI Engineer, you might need Machine Learning for assignments that are too complex for humans to code directly. This is where Neural Networks come in. Neural Networks are inspired by the human brain and identify numerical patterns based on sensory data. 

Artificial Intelligence has advanced from single-layer Neural Networks to Deep Learning Neural Networks. Data is passed through manifolds for complex pattern recognition in Deep Learning Neural Networks. They are the most accurate way of approaching complex problems such as Translation, Speech Recognition, and Image Classification. 

Aside from technical skills, AI Engineers also require business and non-technical skills to successfully navigate within companies and organizations.

Conclusion 

When it comes to the best jobs for the future, only a few industries stand out as much as Artificial Intelligence and AI Engineering. The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field.  

The post Skills Needed to Become an AI Engineer | A Guide to Technical Skills you need to become an AI Engineer appeared first on Fuse AI.