There are three types of machine learning:

Supervised Learning

In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.

Unsupervised Learning

In unsupervised learning, we don’t have labeled data. A model can identify patterns, anomalies, and relationships in the input data.

Reinforcement Learning

Using reinforcement learning, the model can learn based on the rewards it received for its previous action.

Consider an environment where an agent is working. The agent is given a target to achieve. Every time the agent takes some action toward the target, it is given positive feedback. And, if the action taken is going away from the goal, the agent is given negative feedback. 

The Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data. 

When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.

There are multiple ways of avoiding overfitting, such as:

• Regularization. It involves a cost term for the features involved with the objective function
• Making a simple model. With lesser variables and parameters, the variance can be reduced 
• Cross-validation methods like k-folds can also be used
• If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters

There is a three-step process followed to create a model:

1. Train the model
2. Test the model 
3. Deploy the model

Consider a case where you have labeled data for 1,000 records. One way to train the model is to expose all 1,000 records during the training process. Then you take a small set of the same data to test the model, which would give good results in this case.

But, this is not an accurate way of testing. So, we set aside a portion of that data called the ‘test set’ before starting the training process. The remaining data is called the ‘training set’ that we use for training the model. The training set passes through the model multiple times until the accuracy is high, and errors are minimized.

Now, we pass the test data to check if the model can accurately predict the values and determine if training is effective. If you get errors, you either need to change your model or retrain it with more data.

Regarding the question of how to split the data into a training set and test set, there is no fixed rule, and the ratio can vary based on individual preferences. 

One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.

There are two useful methods in Pandas:

• IsNull() and dropna() will help to find the columns/rows with missing data and drop them
• Fillna() will replace the wrong values with a placeholder value

When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit. 

For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.

A confusion matrix (or error matrix) is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it’s called the matching matrix.

The confusion matrix has two parameters:

• Actual
• Predicted 

It also has identical sets of features in both of these dimensions.

Consider a confusion matrix (binary matrix) shown below:

Here,

For actual values:

Total Yes = 12+1 = 13

Total No = 3+9 = 12 

Similarly, for predicted values:

Total Yes = 12+3 = 15

Total No = 1+9 = 10 

For a model to be accurate, the values across the diagonals should be high. The total sum of all the values in the matrix equals the total observations in the test data set. 

For the above matrix, total observations = 12+3+1+9 = 25

Now, accuracy = sum of the values across the diagonal/total dataset

= (12+9) / 25

= 21 / 25

= 84%

False positives are those cases that wrongly get classified as True but are False. 

False negatives are those cases that wrongly get classified as False but are True.

In the term ‘False Positive,’ the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative. 

So, looking at the confusion matrix, we get:

False-positive = 3

True positive = 12

Similarly, in the term ‘False Negative,’ the word ‘Negative’ refers to the ‘No’ row of the predicted value in the confusion matrix. And the complete term indicates that the system has predicted it as negative, but the actual value is positive.

So, looking at the confusion matrix, we get:

False Negative = 1

True Negative = 9

The three stages of building a machine learning model are:

• Model Building

  Choose a suitable algorithm for the model and train it according to the requirement 

• Model Testing

  Check the accuracy of the model through the test data 

• Applying the Model

  Make the required changes after testing and use the final model for real-time projects

Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be modified to make sure that it is up-to-date.

The Deep learning is a subset of machine learning that involves systems that think and learn like humans using artificial neural networks. The term ‘deep’ comes from the fact that you can have several layers of neural networks. 

One of the primary differences between machine learning and deep learning is that feature engineering is done manually in machine learning. In the case of deep learning, the model consisting of neural networks will automatically determine which features to use (and which not to use). 

This is a commonly asked question asked in both Machine Learning Interviews as well as Deep Learning Interview Questions