Python deletes unwanted objects (built-in types or class instances) automatically to free the memory space. The process by which Python periodically frees and reclaims blocks of memory that no longer are in use is called Garbage Collection.

Python’s garbage collector runs during program execution and is triggered when an object’s reference count reaches zero. An object’s reference count changes as the number of aliases that point to it changes.

An object’s reference count increases when it is assigned a new name or placed in a container (list, tuple, or dictionary). The object’s reference count decreases when it’s deleted with del, its reference is reassigned, or its reference goes out of scope. When an object’s reference count reaches zero, Python collects it automatically

• Supervised Learning is a machine learning approach that’s defined by its use of labeled datasets. Unsupervised Learning uses machine learning algorithms to analyze and cluster unlabeled data sets. 
• Training the model to predict output when a new data is provided is the objective of Supervised Learning. Finding useful insights, hidden patterns from the unknown dataset is the objective of the unsupervised learning.
• Supervised Learning can be used for 2 different types of problems i.e. regression and classification. Unsupervised Learning can be used for 3 different types of problems i.e. clustering, association and dimensionality reduction.
• Supervised Learning will use off-line analysis. Unsupervised Learning uses real time analysis of data. 
• To assess whether right output is being predicted, direct feedback is accepted by the Supervised Learning Model. No feedback will be taken by the unsupervised learning model.
• Computational Complexity is very complex in Supervised Learning compared to Unsupervised Learning.
• Accurate results are produced using a supervised learning model. The accuracy of results produced are less in unsupervised learning models.

• The central limit theorem states that the sampling distribution of the mean approaches a normal distribution, as the sample size increases.
• As the sample size increases, the distribution of frequencies approximates a bell-shaped curve (i.e. normal distribution curve). The main objective of the Central Limit Theorem is that the average of your sample means will be the population mean.
• This is useful, as the research never knows which mean in the sampling distribution is the same as the population mean, but by selecting many random samples from a population the sample means will cluster together, allowing the research to make a very good estimate of the population mean.
• A sufficiently large sample can predict the parameters of a population such as the mean and standard deviation.
• Sample sizes equal to or greater than 30 are required for the central limit theorem to hold true.
• The Central Limit theorem plays a big role in hypothesis testing.