Complete Guide to Mastering Machine Learning | Beginner to Pro.

One of the most Demanded skills in 2021 is Machine Learning. Considering the advancements in Artificial Intelligence(AI) and Machine Learning(ML), 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.

Every Aspirant always have this one question ringing in his mind.

Where should I start? Or rather, How should I start?
In this post, I will tell you step by step how to get started with Machine Learning and how to get to the next level.

Complete Guide to Mastering Machine Learning | Beginner to Pro.

WHAT IS MACHINE LEARNING?

Machine Learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Artificial Intelligence in a broader perspective refers to systems or machines that mimic human intelligence.

Machine learning is famous in internet search engines,
email filters to sort out spam, websites to make personalized
recommendations, banking software to detect unusual transactions, and
lots of apps on our phones such as voice recognition.

There are three types of machine learning thus

Supervised machine learning,

Unsupervised learning, and

Reinforcement learning.

ADVANTAGES OF MACHINE LEARNING

They
have the ability to easily identify the patterns and other common
elements in groups.
Automation of tasks is possible with machine learning.
It is a faster method of
performing tasks.
Easy solution to complicated problems and complex datasets.
Wide range of applications.

The downside of machine learning is that sometimes machine learning tasks will require large amounts of data to
produce the desired results. Hence, it is slightly expensive to collect
all the required results.

APPLICATIONS OF MACHINE LEARNING

Image Recognition.
Speech Recognition.
Traffic prediction.
Product recommendations
Self-driving cars.
Email Spam and Malware Filtering.
Virtual Personal Assistant.
Online Fraud Detection.

THE PREREQUISITES FOR MACHINE LEARNING

To Master machine learning, it is very important that one knows his/her way around in the following areas.

Math – Mathematics is undeniably the most significant aspect of machine
learning. Learning Math is crucial to be able to understand some of the integral
coding aspects that are involved in machine learning. The main focus for any beginner starting with machine learning is to
pay attention to specific topics in Math such as Algebra, Calculus, Probability and Statistics.
Programming – Programming is another essential feature required to master machine learning.While mathematics is extremely important for understanding the concepts, coding skills are required to interpret this information into a form that can be viewed and utilized by others.
Algorithms – Some of the basic algorithms that beginner machine learning enthusiasts
must start working on and mastering are some of the basic supervised
algorithms like logistic regression, linear regression, support vector
machines and naïve Bayes algorithm.

HOW TO GET STARTED IN MASTERING MACHINE LEARNING

Believe that you can practice and apply machine learning.
Use a systemic process to work through problems. (Discussed below)
Select a tool for your level and map it onto your process. See Top 10 Programming Languages
Select datasets to work on and practice the process.
Gather results and demonstrate your skills.

5 PROCESSES TO MASTERING MACHINE LEARNING

1. Identify and Establish your Problem

Describe the problem as though you were describing it to a friend or colleague.

Use formalism like TEP to define this problem. T(Task), E(Experience), P(Performance).

Question Everything.

Make Assumptions.

Consider your motivation for solving the problem, the benefits a solution provides and how the solution will be used.

2. Prepare and Process your Data

Raw data is, most of the time, inconsistent and incomplete, and it cannot be sent through a Machine Learning model. This is where Preprocessing comes in.

Select your Data

Format the data you selected in a way that will be suitable to work with.

Clean your data by adding missing information or removing unnecessary ones.

Sample your data by testing a smaller portion of the selected data before considering the whole dataset.

Transform your data lastly by scaling, decomposing and aggregating them into a single feature that would be more meaningful to the problem you are trying to solve.

3. Select 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.

4. Evaluate the Selected Algorithm

Now that you have established your problem, prepared your data and selected desired algorithms, you need to apply and evaluate the machine learning algorithms to the data in order to solve your problem.

This step is targeted at improving the accuracy of your model which usually falls between 70 – 80 %.

There are different techniques to evaluating a machine leaning model.

For Supervised Learning, there is Classification and Regression type.

Classification(in which the response is categorical) make use of different evaluation metrics, such as accuracy_score which we cannot use for Regression models as their values are continuous. Regression(in which the response is ordered and continuous) make use of evaluation metrics such as Mean Absolute Error(MAE), Mean Squared Error(MSE), or Root Mean Squared Error(RMSE) instead.

Train-Test Split is another evaluation tool that will help you 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. Whereas we can neglect this feature if there is an increase in accuracy.

K-fold Cross-Validation is another tool you need to have knowledge of. It features;

Splitting the dataset into equal partitions.
Folding 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 broad topics that can’t be captured on one article however I summarized it to get all the steps together, from start to finish, in one place. Once you get these steps, study extensively with each step and understand it thoroughly.

5. Presenting the Response of our Machine Learning Model.

After we defined our problem, preprocessed 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 present the response results, I recommend you use the template below

Context

Problem

Solution

Findings

Limitations

Conclutions

CONCLUSION

Machine learning in the modern world is a crucial aspect of development.

In this article, we have covered,

The meaning of Machine Learning
The advantages of Machine Learning
The applications of Machine Learning
The essential requirements to master Machine Learning
How to get started and the 5 processes to mastering Machine Learning

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