But here’s the thing: many experts reckon AI might snatch up some jobs, but it’s also gonna create new gigs in AI development, data science, robotics, and AI ethics and governance. Additionally, AI is expected to enhance productivity and efficiency in existing industries, leading to job growth in those sectors as well.

Ever since Microsoft Copilot was introduced at my workplace, my productivity and learning have definitely gone up, and I have gotten more done in the workday than I have in the past.

What some people are also missing is how incredible AI is for learning, development, and as a tutoring resource.

Currently, AI is becoming the most in-demand technical skill as well. With companies scrambling for workers in these roles and having trouble filling them. AI developer’s, engineers, and consultants are seeing a lot of job opportunities even at companies outside the traditional tech world. Al related jobs also don’t always require engineering or coding skills

While there is a AI goldrush in Tech right now, a lot of tech workers don’t know where to get started with AI, and where to gain the skill set for it. Because lets be real, diving into AI can be daunting.

One of the hardest courses I took as an information system major during my undergrad was Artificial Intelligence, and building machine learning projects in my college internships were the hardest projects I have done still so far in my career. So its definitely not an easy subject to master off the bat.

But the million dollar is how do we start to learn and understand AI? Because it’s not impossible, and anybody can learn it. You just gotta start somewhere.

Like what is Machine Learning? What is Deep Learning? What is Natural Language processing? How do we understand Machine Learning Models? And how do they all fit into AI?

I know trying to learn machine learning and AI concepts can be intimidating at first glance when you see stuff like this:

I felt exactly the same, but learning and getting started with Machine Learning does not have to be intimidating, and that’s why I wanted to start this series of articles. Because during my journey on revisiting machine learning topics and progressing my skills in AI, I want to document my sharing’s as well.

To start let’s give the basic gist of Machine Learning, because it’s pretty much the heart of AI.

Machine learning is like teaching a computer to learn from examples, which in our case would be data.

An simple water down example would be imagine you’re learning to recognize different animals. At first, someone might show you pictures of a dog, a cat, and a bird, and they tell you what each one is. You look at the pictures and remember what makes each animal different. Then, when you see a new animal, you can guess what it is based on what you’ve learned from those examples.

Machine learning works kind of like that. Instead of you, it’s a computer that’s learning, and instead of animals, it might be learning to recognize things like pictures of cats, dogs, or even handwritten numbers. But instead of someone telling the computer what each thing is, it’s given a bunch of examples and figures out patterns on its own.

So, just like you get better at recognizing animals the more examples you see, a computer gets better at recognizing things the more examples it’s given. That’s the basic idea so far of machine learning, and well get into the more in depth parts later, but basically Machine Learning algorithms enable AI systems to learn from data, identify patterns, make predictions, and improve over time without being explicitly programmed. This ability to learn from data is crucial for many AI applications.

While machine learning is a central aspect of AI, it’s important to note that AI encompasses a broader range of techniques beyond just machine learning. These techniques include symbolic reasoning, expert systems, knowledge representation, planning, and more. Machine learning, however, has become increasingly prominent due to its effectiveness in handling complex tasks and large datasets, leading to significant advancements in AI capabilities.

While Artificial Intelligence (AI) and Machine Learning (ML) are related concepts, they are not the same thing. AI is a broad field of computer science that aims to create machines or systems that can perform tasks that typically require human intelligence. This includes things like understanding natural language, recognizing objects in images, making decisions, and solving problems. AI encompasses a wide range of techniques and approaches, including machine learning.

Machine Learning, on the other hand, is a subset of AI. It’s a specific approach to achieving AI, where systems learn from data instead of being explicitly programmed to perform a task.

So, while all machine learning is a form of AI, not all AI systems use machine learning. AI is this big umbrella term for smart computer stuff, while ML is a specific way computers learn, mainly from data.

While this is just a brief introduction to Machine Learning moving forward, we’ll cover these topics of machine learning in the coming articles as well.

• Type of Machine Learning: Clustering, Unsupervised Learning, Supervised Learning, Regression, Classification, and Reinforcement Learning
• How Machine Learning Models works
• How Machine Learning Algorithm’s work
• Basic Data Exploration
• Making your first ML Model
• Model Validation
• Underfitting & Overfitting
• Random Forests (Python & R)
• Different Machine Learning Libraries
• Machine Learning Pipelines
• Cross Validation
• Handling Data in Machine Learning: Missing Values, Categorical Variable’s, Data Leakage

These coming months I’ll start releasing my notes from learning and reviewing these different Machine Learning and Artificial intelligence topics. Stick around as I share my learning journey with you!