Demystifying Machine Learning: An Introduction to Techniques

Introduction:

Have you wondered about how e-commerce websites suggest your products?

Have you wondered about how virtual assistants like google assistant, and Siri works based on your commands?

This is all happening with the working of Artificial Intelligence(AI) which recognizes patterns, learns from your mistakes and tries to mimic your brain so that it will be perfect for you.

Now in this article let's try to discuss about the key player of Artificial Intelligence to work properly. You have guessed it right it's Machine Learning (ML).

What is Machine learning?

Machine Learning is a field of Computer Science branch of Artificial Intelligence (AI). Machine Learning deals with building models and developing algorithms in different areas like Natural Language Processing (NLP), Deep Learning (DL).

What are the types of Machine Learning?

There are different types of Machine Learning available to discuss among which are classified into 3 major parts. They are:

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Working Process of Machine Learning

The working of this Machine Learning is simple. Now let's try to outline this working process. We'll try to cover these steps in further articles.

1. Data Collection

The first and foremost step in the working process of Machine Learning is Data Collection. We need to collect the data for the business problem we are trying to solve.

2. Data PreProcessing

The second step involves data preprocessing, in which we need to preprocess the data by cleaning the data, and understanding of data.

3. Feature Selection

After the preprocessing, we need to look after the features of the data through which we need to decide which features to work with to gain useful outcomes.

4. Model Selection

We need to select the best suitable model for our problem statement. We can iteratively look upon this Model selection if we are not satisfied with the selected model.

5. Training

Data Training is done after selecting the Model. This training data is some part of the total data i.e., we need to split the data into 80% for the training data and 20% for the testing of the data. Among these, we need to train 80% of the total data and the rest is used for accuracy checking. Note: In the above example I have taken 80% train and 20% test data which you can use according to your convenience.

6. Testing(Evaluation)

We need to check for the accuracy of the model by testing out the model by using the rest of the data with the help of the evaluation metrics like root mean square error, accuracy, classification report etc.

7. Model tuning

We need to tune the model with the hyperparameter tuning which means we need to make sure to pass the best suitable parameters for the model.

8. Deployment

After satisfying with the model performance we can deploy the model into production so that we can further work on the new data by maintaining the model.

Conclusion

This is the Introduction of Machine Learning where we have seen the basic definition of Machine Learning and its simple working of it. In the further articles, we will see a much more detailed explanation of every part.

Machine Learning Boot Camp

#Day-1

Table of contents

Introduction:

What is Machine learning?

What are the types of Machine Learning?

Working Process of Machine Learning

Conclusion

Thank You!

Machine Learning Boot Camp

#Day-1

Table of contents

Introduction:

What is Machine learning?

What are the types of Machine Learning?

Working Process of Machine Learning

Conclusion

Thank You!

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