Python for Data Science & Machine Learning: Zero to Hero

Review the core data science libraries and module structure for this course, noting that Anaconda provides the essential libraries so no additional installation is needed for now.

Discover Seaborn as a powerful data visualization library built on top of Matplotlib, extending its functionality with additional graph types and simplified syntax.

Learn to create NumPy arrays from Python lists and tuples using np.array, build 1d, 2d, and 3d arrays, and verify their dimensions with the .ndim attribute.

Learn to index numpy arrays in Python by using multi-dimensional indexing with two indices for 2d arrays and three indices for 3d arrays, demonstrated in a Jupyter notebook.

Explain how to read numpy array shape with the .shape attribute, returning a tuple where the first value is number of dimensions and the second is elements per dimension.

Explore iterating over numpy arrays with for loops, from 2d arrays using nested loops to 3d arrays with three levels, printing rows and elements in a Jupyter notebook.

Use numpy's ones() to create an array of ten ones. The resulting array defaults to floats, then cast to an integer array.

Divide an array by a scalar using / for float division and // for integer results, while a Python list cannot be divided and raises an error.

Raise each element of an array to a power using the double star operator, and contrast with a Python list that errors, illustrating element operations and list limitations.

Explore element-wise addition in NumPy by adding two arrays with the plus operator, and note that Python lists concatenate rather than sum element-wise.

Explore the two main pandas data structures: dataframe and series, noting that a dataframe is two-dimensional with labeled rows and columns, and that labels can be numbers or strings.

Learn how a pandas series is a one-dimensional data structure with a single column, index 0 to 9, and its name and data type.

Use the described function to obtain a statistical summary of the entire dataframe, DF, via the describe method, producing a data frame that presents per-column statistics.

Slice dataframe rows with the bracket operator by specifying a start index and an exclusive end, using raw positions not labels such as 1 to 3.

Use the bracket operator to index one or more dataframe columns by labels; when indexing multiple columns, supply their labels as a list, returning a subset while preserving original dataframe.

Apply a condition on the calories column to filter a DataFrame, using the bracket operator with a boolean list to return rows where calories are greater than 70.

Add a row with the lock method using a label and values matching the dataframe, then delete rows and columns with drop and bracket notation (axis 0 and axis 1).

Learn to sort the values of a data frame by a column using the sort_values function, with numbers sorted numerically and words alphabetically, as shown for the calories column.

Improve data quality by applying data cleaning techniques to ensure data is fit for operations, decision making, and planning, enabling better decisions.

Apply z-score based anomaly detection to identify outliers by measuring how far values lie from the mean in units of standard deviation, using a threshold of 1.5.

Explore detecting anomalies with the interquartile range by computing Q1, Q2, and Q3 from data, using NumPy percentile brackets, and the 1.5 IQR rule in Python.

Learn how to handle missing values in data cleaning using pandas: remove rows with missing data or fill with mean, median, or mode to preserve information and analyze datasets.

Explore regular expressions in Python to match patterns, locate digits, test string starts, and replace text, using the re module methods like findall, search, and sub for data cleaning tasks.

Perform univariate analysis of the categorical Verity column using count plots and a pie chart to show equal proportions across the three categories.

Learn bivariate analysis of two continuous features with scatterplots and correlation. Use seaborn and pandas to plot and compute correlation, confirming low link between fare and edge, with heatmap.

This final chapter introduces time series, showing how to analyze them, identify patterns, and develop forecasting models with machine learning using pandas, numpy, matplotlib, and Python's date time tools.

Learn to download stock data from Yahoo Finance using yfinance in a Jupyter notebook, import the library, and inspect a pandas time series dataframe.

Learn to work with time series in Python by treating each column as a time series, converting dates, indexing by date, and handling weekends and timestamp ranges.

Visualize a time series of Apple stock prices over five years using math plot lib in a jupyter notebook, plotting the adjusted close column and labeling the axes.

Plot line plots with Matplotlib by passing x and y lists to plt.plot in a Jupyter notebook, and change the line color with a third argument like 'r' for red.

Learn to label and title matplotlib graphs, add x and y axis labels, plot multiple series in one chart, and use legends to distinguish exponential and linear plots.

Create a polar plot using a mock plot library, mapping distance r and angle theta; generate theta from 0 to 2 pi in 0.01 steps and set r to 2.

Explore how machine learning powers diverse applications across health, transportation, finance, and language translation. See it enable cancer detection, self-driving cars, Gmail spam filtering, and personalized product recommendations.

Uncover patterns in unlabeled data by applying unsupervised learning, as algorithms group inputs by similarity. Leverage data that are abundant relative to labeled data, making unsupervised methods particularly valuable.

Explore how linear regression uses a scatterplot of house prices versus area to fit lines, minimize squared errors, and select the best fit for price prediction.