Use the append() method, e.g., my_list.append(item).

Insert the item using my_list.insert(item).

Use the add() method, e.g., my_list.add(item).

Combine lists with my_list += [item].

Tuples are mutable; lists are immutable.

Lists are mutable; tuples are immutable.

Lists are faster than tuples in all cases.

Lists can contain only numbers; tuples can contain any data type.

list(my_list)

array(my_list)

tuple(my_list)

convert(my_list)

numpy.array()

numpy.list()

numpy.create_array()

array.numpy()

array.size

array.dimensions

array.shape

array.length

numpy.mean()

numpy.median()

numpy.sum()

numpy.average()

Use array.change_shape(new_shape) to modify the shape of a NumPy array.

Use array.reshape(new_shape) to reshape a NumPy array.

Call reshape_array(array, new_shape) to reshape a NumPy array.

Use array.resize(new_shape) to change the size of a NumPy array.

A DataFrame is a type of database in Pandas.

A DataFrame is a two-dimensional labeled data structure in Pandas.

A DataFrame is a one-dimensional array in Pandas.

A DataFrame is a graphical representation of data in Pandas.

Use `pd.read_csv('file_path.csv')` to read a CSV file into a DataFrame.

Use `pd.load_csv('file_path.csv')` to read a CSV file into a DataFrame.

Read the CSV file with `pd.import_csv('file_path.csv')` into a DataFrame.

You can load a CSV file using `pd.open_csv('file_path.csv')` to create a DataFrame.

top()

slice()

head()

firstRows()

Apply the `map()` method

Use the `filter()` function

Sort the DataFrame by index

Use boolean indexing or the `query()` method.

To manage databases in Python.

To perform web scraping tasks.

To write and execute Python scripts.

The purpose of Matplotlib in Python is to create visualizations and plots of data.

import matplotlib.pyplot as plt; plt.draw(x, y); plt.render()

import matplotlib as mpl; mpl.plot(x, y); mpl.show()

import matplotlib.pyplot as plt; plt.line(x, y); plt.display()

import matplotlib.pyplot as plt; x = [1, 2, 3, 4]; y = [10, 20, 25, 30]; plt.plot(x, y); plt.show()

Classification requires more data than regression.

Classification is used for time series; regression is for image data.

Classification predicts numerical values; regression predicts categories.

Classification predicts categories; regression predicts continuous values.

Linear Regression

K-Means Clustering

Decision Tree

Support Vector Machine

The purpose of the training set in machine learning is to train the model.

To store the final model parameters

To provide real-time predictions

To validate the model's performance

Identify stakeholders, Develop software, Conduct training

Define the problem, Collect data, Prepare data, Choose a model, Train the model, Evaluate the model, Tune the model, Deploy the model, Monitor and maintain the model.

Select features, Validate data, Present findings

Analyze results, Collect feedback, Implement changes

Use metrics like MAE, MSE, RMSE, and R-squared to evaluate performance.

Evaluate based on the number of features used in the model.

Use only visual inspection of the model's predictions.

Check the training time of the model as the main metric.

Overfitting is when a model is trained on too little data, leading to poor performance.

Overfitting is when a model performs well on unseen data but poorly on training data.

Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.