Build Your First Machine Learning Model Step by Step

Table of Content

• What is a Machine Learning model and why does it matter?
  
• Step 1: Define The Problem Clearly
  
• Step 2: Acquire and understand your data
  
• Step 3: Prepare and Clean the Data
  
• Step 4: Train the Model
  
• Step 5: Evaluate the Model
  
• Step 6: Refine and iterate
  
• Conclusion
• FAQs

Machine learning is one of those subjects that appears daunting in concept, but once someone is in a position to take a working example apart and rebuild it, it starts to feel like common sense. Fundamentally machine learning involves teaching a machine to recognize patterns in data and using the patterns identified to make predictions relevant to the task. The machine is never explicitly programmed with rules; it instead "learns" how to recognize patterns by observing examples and by itself refining how it finds patterns each time and gets progressively better at the task. The thousands of iterations through actual data produce the effect people refer to as "smart" software.

The good news is that it takes very little time (measured in hours) for someone who understands programming fundamentals, has a basic familiarity with data, and some time to read a bit, to build and evaluate an actual machine learning model. This tutorial describes the steps one takes, in chronological order of how they actually occur, with explanations for each in the overall context of ai and machine learning.

What is a Machine Learning model and why does it matter?

Before diving into the workflow, it's important to define what exactly is meant by a machine learning model. The model itself is basically a mathematical construction that has learned to return some output from some input that it's never experienced before. It is very much like the human experience, as children need to see many different instances of cats to know that all cats are, in fact, cats. The model does this with tens or hundreds of thousands of data points in just minutes.

Why does it matter? This model construction is important as every step taken when constructing a model relates back to this definition. It is not some predefined sequence of actions.

Step 1: Define The Problem Clearly

The very first thing in any machine learning project is to be clear about what you are trying to solve. This may seem obvious, but most beginners either overlook this first stage or take it too lightly, and it leads to confusion down the road. Whether you're designing a predictive tool or ai robots, a good problem statement is the foundation of an intelligent agent.

●     What am I expecting the model to predict/ classify?

●     In what format will the answer be needed? Category/ classification? Number/ regression? Cluster?

●     Which data sources do I have available, and will they be sufficient to build a reasonably performing model?

●     What will count as a "success" for the model, and what does "good" look like?

As an example, for a model intended to predict whether a bank customer will default on a loan, the problem statement would be a classification problem, and the model would have to output either a 'yes' or 'no' decision.

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Step 2: Acquire and understand your data

Data is the raw ingredient in machine learning, and if there isn't enough or if it's not the correct kind, then the algorithm just won't be able to do what you want it to. Gathering data involves collecting all the data that you will need to put the algorithm to good use, in the correct format and with correct labels/representatives for the real-world situation. Some things which need to be looked into when a new dataset is first explored are:

●     How many rows & columns are there?

●     Is there missing data & how much is it?

●     What type of data is it (text, numeric, dates)?

●     Are there any highly correlated features?

●     Is there a class imbalance?

●     Finding new datasets can be made quite easy with Python using something like Pandas.

If the data is loaded into Pandas and a few summary functions are applied, a good deal can be learned about the structure and the potential issues. This process is usually termed as exploratory data analysis, and seasoned experts tend to dedicate a lot of time to this process before attempting any algorithm, which tends to result in poor performance from models. This is especially true in deep AI learning, where the volume and complexity of data make thorough exploration even more critical.

Step 3: Prepare and Clean the Data

Raw data often needs to be cleaned. This involves things like ensuring no missing data, incorrect formatting, duplicated rows, and any values that seem to distort the results. Data cleaning is one of the most time-consuming steps of the entire process, and one of the most important ones at that.

Following a step by step approach to data preparation helps avoid the common mistake of rushing straight into model training.

Some of the most frequent data preparation steps include:

●      Dealing with missing data. It may mean deleting rows that have many missing values, using imputation (such as the mean or median of the column), or using more sophisticated imputation algorithms.

●     Encoding categorical variables. Usually, text-based categorical features need to be converted into numerical features to be learned by the algorithms. Categorical features red, blue, and green must be transformed into a number for the model to learn.

●      Scaling numerical features. One common task is to bring variables measured in significantly different ranges to a similar scale, as otherwise higher-ranging values will be disproportionately weighted in the model. An income column in the hundreds of thousands of dollars should be on a comparable scale to a column that contains the age of the individual in tens.

●     Removing duplicate data to prevent deep learning ai models from learning from similar instances numerous times, which could give them undue confidence in particular data points.

●     Splitting the data into training and testing sets. It's common to have a 80% training, 20% testing split, although this depends heavily on the size of the dataset in question.

The model learns from the training data. The testing data is only revealed to the model when determining the model's effectiveness after training.

Step 4: Train the Model

This is the step at which the algorithm learns the patterns in the training data. To learn machine learning concepts in practice, this consists of making the algorithm "learn" or fit itself to the training set, such that it learns how to map the inputs to outputs based on that training data.

In Python using scikit-learn, this will typically involve writing three lines of code, which are importing the algorithm, instantiating the algorithm with certain parameters, and fitting the algorithm on the training set:

The syntax is deceptively simple, though. Within these three lines of code, the algorithm is performing many hundreds or thousands of calculations to work out the optimum parameters for it internally.

A couple of considerations with regard to training:

●     Hyperparameters are settings that define how the algorithm learns and thus what the resultant model 'learns'. For example, a decision tree has the max_depth hyperparameter, which defines how deep the tree will grow, but altering this will change the model significantly.

●     Overfitting occurs when a model learns the patterns and, more crucially, the noises/deviations from the pattern in the training data, but then fails on new data. This is a particularly common problem in deep learning. If some of the training data is held out to use for testing during the training stage, this will be detected earlier.

●     Underfitting occurs when the model is simply too basic to learn any useful patterns in the data. If accuracy during the training phase is low, the model will be underfitting the training data.

Step 5: Evaluate the Model

After training, the performance of the model has to be evaluated on the test set. Here is where keeping aside part of the data becomes really useful; evaluating the model on data that it has not previously seen will truly reflect its performance in the real world.

Key evaluation metrics to look out for in machine learning:

●     Accuracy – Fraction of predictions that were correct. It is quite simple to understand, but misleading when you have a highly uneven class distribution.

●     Precision – The measure of how many of the positive predictions made by the model were correct.

●     Recall – The measure of how many of the actual positives were identified by the model correctly.

●     F1 Score – It is a harmonic mean of Precision and Recall. This will give you a better result if both Precision and Recall are important.

●     Confusion Matrix – It is a table in which all the results have been tabulated, including TP, TN, FP, and FN.

The accuracy of a model for a dataset having 95% sample belong to one class will not teach us anything, and in order to facilitate this, you can always consider looking beyond the accuracy.

Step 6: Refine and iterate

Having said that, it is unlikely that the model you just created is the final model you will create. Once you have completed the evaluation stage, the next thing you want to do is question: 'How can I make this better? This may involve going back to the data preparation stage, including additional features in the data, or choosing a different algorithm in artificial intelligence and machine learning, possibly by tuning parameters of the algorithm that has been used.

Some ways in which model performance can be increased are:

●     This is feature engineering, where the original features are combined in new ways to potentially add value for the algorithm.

●     Use of cross-validation, as opposed to just train-test splits.

●     Comparing different types of models like decision tree, Random Forest, Gradient Boosting type algorithms, or SVM.

●     If the data available is insufficient, perhaps it is best to collect more data before pursuing further improvements with algorithms.

The cycle of build, evaluate, and refine is how machine learning projects tend to work in the real world, and not usually how you would obtain the correct result on the first try.

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Conclusion

This is a general framework to start you with the first ML model. Be patient and learn the details on every step. The order that we described-define the problem, prepare data, choose the model, train model, evaluate model, improve model- is a correct methodology for any ML problem, whether it is complicated or simple. The main thing is you don't have to be perfect in the first step, but you do have to know what you are doing. If the results are not good, then don't be afraid to go back one or more step(s).

Anyone willing to truly learn machine learning should build their first machine learning model using a real dataset, no matter how small and how simple the problem is. All the theory becomes more solid when there's actual code to run, real bugs to debug, and real outcomes to interpret. When starting with ML, it should always be from a small and manageable step, and your understanding will be built up over time with every model you complete.

FAQs

1. How much data do I need to create a useful machine learning model?

 For classical machine learning algorithms, just a few thousand "clean", labeled examples might suffice to begin with. Deep learning models, however, would require data in the tens or hundreds of thousands of examples, or more.

2. Do I require more advanced mathematics to build a machine learning model?

Some statistics and algebra are needed right at the beginning, although a good knowledge of multivariate calculus only becomes relevant when we dive into the area of deep learning, and it's less important when developing standard models in a beginner course.

3. What is the difference between training data and test data?

"Training data" is simply the data the machine learning algorithm learns from. The "test data" is a set of data that is held back until after training, so that we can find out how well the model works on data that it has never encountered before.

4. What is overfitting and how do I prevent my model from doing so?

An "overfitted" model has learned the training data too well and will not work on unknown data. This is prevented by using a simpler model, and using many examples for training, and a variety of techniques, such as 'regularisation'.

5. Is machine learning equivalent to artificial intelligence?

Artificial Intelligence is a broader topic, and one of the sub-fields of AI is machine learning, in which computers learn from data without the need for explicitly programmed rules.