Beginner’s Guide to AI/Machine Learning in 2023

The way we live, work, and communicate has been changed by artificial intelligence (AI). Technology has integrated into our daily lives and will undoubtedly change industries in the years to come. As a beginner, understanding the basics of AI is crucial to staying ahead of the curve. We will examine the fundamentals of artificial intelligence and how it will impact our future in this introduction to AI in 2023.

One of the most In-Demand skills one can learn in 2023 is Artificial Intelligence or Machine Learning. With advancements in Artificial Intelligence and Machine Learning, and with every industry looking to apply Artificial Intelligence in their business, acquiring a skill like Machine Learning will open a whole new world of opportunities.

So, the question comes, where should I start? Or rather, how should I start?

In this article, I will tell you step by step “How To” get started with Machine Learning or Artificial Intelligence and how to get to the next level.

First off, Let’s start with how artificial intelligence can be defined.

Artificial intelligence is the capacity of machines to simulate human intelligence and carry out operations like speech recognition, decision-making, and visual perception that often call for human intelligence. It combines robots, natural language processing, and machine learning.

Algorithms enable machines to learn from data and enhance their performance over time, which is how AI is made possible.

Artificial intelligence (AI) Types:

Artificial intelligence can be divided into two primary categories: specific or weak AI and general or strong AI. Narrow AI is created to carry out a single task or collection of related activities, like Siri or Alexa or facial recognition. General AI, on the other hand, is created to carry out any intellectual work that a human can.

Artificial intelligence applications:

Across industries, artificial intelligence offers a wide range of useful applications. AI is being utilized in healthcare to diagnose illnesses and find the most effective treatments. AI is used in finance to spot fraud and decide which investments to make.

AI technology is being used to create self-driving vehicles for use in transportation.

Artificial intelligence’s future:

The potential of artificial intelligence is intriguing, and technological developments have the potential to fundamentally alter how we live and work. Healthcare, banking, and education are just a few of the many sectors that AI will continue to affect. AI will develop into a more sophisticated and widely available technology as more businesses engage in its research and development.

Here are five steps that you can follow to get good at Artificial Intelligence or Machine Learning:

Step 1: Define Your Problem

Step 2: Data Preparation (Pre-processing)

Step 3: Selecting a Machine Learning Algorithm

Step 4: Evaluation of The Machine Learning Algorithm

Step 5: Predicting the Response

Step 1: Define Your Problem.

• This might look straightforward but is more than you think.
• This step might be one of the most crucial steps. Most people make this mistake, where they wrongly define their problem. If your problem statement is wrongly defined, the predictions will be incorrect, no matter how complex or accurate your model is.
• Describe the problem to yourself, as if you are explaining it to a friend.
• Now, make it a one-sentence description. Once you have that, you can use formalism. There is something called Tom Mitchell’s Machine Learning formalism. You can look it up if you are interested.
• Define T(Task), E(Experience), and P(Performance/Accuracy) for your problem.

Step 2: Data Preparation (Pre-processing).

Real-world data or Raw data is, most of the time, inconsistent and incomplete, and it cannot be sent through a Machine Learning model. This is where Pre-processing comes in. It is a technique that transforms raw data into much more consistent, structured, and formatted data.

• Select the data you want to work on. It can be from sites like Kaggle, or it can be a dataset that you gathered.
• Once you have selected the desired data, now it’s time to pre-process it.

There are three steps for Data Preprocessing:

• formatting the data: The data you have selected may not be in formatting suitable for you to work with. Hence, you must format the data that is suitable for the given model.
• Cleaning the data: Cleaning the data step aims to identify and remove errors and duplicate data to improve the quality and reliability of the dataset.
• Sampling the data: Sometimes, there is far more data than necessary for initial model building. This can make the run time longer, which is undesirable. What you can do instead is to take a smaller sample of the same data for initial model building and testing before considering the whole dataset.

Step 3: Selecting a Machine Learning Algorithm.

Selecting a Machine Learning Algorithm is a vast topic, and it depends on data, problems, and its purpose. But let’s discuss it. There are different types of problems in Machine Learning, Supervised Learning problems, and Unsupervised Learning problems.

• Classification and Regression are types of Supervised Learning, while Clustering is a type of Unsupervised Learning.
• If it is a Regression problem, you can use Linear regression, Decision Trees, Random Forest, KNN, etc.
• If it is a Classification problem, you can use Logistic Regression, Random Forest, SVM, etc.
• If it is Unsupervised learning, then you can use Clustering algorithms like K-means Algorithm.

Step 4: Evaluation of the Machine Learning Algorithm.

Many a time, what we see is that after selecting an algorithm and building a model, the model fails to give an accurate prediction. Sometimes the accuracy falls between the range of 70–80%. How can we check if this result is actually accurate, and if it is, then how can we increase the accuracy of our model?

This is where Model Evaluation comes into play. There are different techniques to evaluate a Machine Learning model.

For Supervised Learning, there is Classification and Regression type. Classification (in which the response is categorical) and Regression (in which the response is ordered and continuous). For Classification, there are different evaluation metrics, such as accuracy score. But we cannot use Classification’s evaluation metrics for Regression models as the values in them are continuous. So instead, we use evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error(MSE), or Root Mean Squared Error(RMSE) for Regression.

Train-Test Split can also help us to choose between features. If a feature appears to have a weak correlation with the response, we can use a train-test split to evaluate the model. If the accuracy increases, then we can neglect that feature.

There is also something called K-fold Cross-Validation.

• Which is to split the dataset into equal partitions.
• Use fold one as the testing set and the union of the other folds as the training set.
• Calculating the testing accuracy.
• Repeat points 2 and 3, K times, using a different fold as the testing set each time.
• Using the average testing accuracy as the estimate of out-of-sample accuracy.

These are very vast topics that I summarized to get all the steps together, from start to finish, in one place. Once you get these steps, go in-depth with each step, and understand it thoroughly.

I’ll be posting In-depth AI projects with code so you can check that out to start your AI journey. It will be from beginner level to advance level.

Step 5: Predicting the Response of our Machine Learning Model.

• After we defined our problem, pre-processed the data, selected the Machine Learning Model, and evaluated our Machine Learning Model, it is time to Predict the Response of our Machine Learning Model.
• This is the step where we will try to input new observations or out-of-sample data to check the response and accuracy of our Machine Learning Model. If it is a classification problem, then we will observe the category of the label. If it’s a regression problem, then we will check the real-time value of the response.

To sum up, artificial intelligence is a fascinating field that has the potential to completely transform how we live and work. To keep ahead of the curve as a beginning, it is essential to comprehend the fundamentals of AI. Keeping up with the most recent trends and innovations in the industry is crucial given the constant advancements in technology.

We hope that this introduction to artificial intelligence has given you a solid foundation on which to build when you explore the field more deeply in 2023.

Hope you learned something from this article and are ready to start your AI/ML journey!