Generating synthetic data

# --------------------------------------------------------------------------
# The script below will eventually go in the tutorials, it generates points
# using standard keywords and plots them. It was also used to eyball the
# results and make sure they made sense
# --------------------------------------------------------------------------

# using PyPlot
#
# # BLOBS
#
# figure(figsize=(8, 6))
# X, y = make_blobs(100, 2, centers=3, as_table=false)
#
# plot(X[y.==1, 1], X[y.==1, 2], ls="none", marker="o")
# plot(X[y.==2, 1], X[y.==2, 2], ls="none", marker="o")
# plot(X[y.==3, 1], X[y.==3, 2], ls="none", marker="o")
#
# gcf()
#
# # CIRCLES
#
# figure(figsize=(8, 6))
# X, y = make_circles(200, as_table=false, factor=0.3)
#
# plot(X[y.==0, 1], X[y.==0, 2], ls="none", marker="o")
# plot(X[y.==1, 1], X[y.==1, 2], ls="none", marker="o")
#
# gcf()
#
# # MOONS
#
# figure(figsize=(8, 6))
# X, y = make_moons(200, as_table=false)
#
# plot(X[y.==0, 1], X[y.==0, 2], ls="none", marker="o")
# plot(X[y.==1, 1], X[y.==1, 2], ls="none", marker="o")
#
# gcf()
#
# # REGRESSION 1
#
# figure(figsize=(8, 6))
# X, y = make_regression(200, 1, as_table=false, noise=0.5, rng=550)
#
# plot(X[:, 1], y, ls="none", marker="o")
#
# gcf()
#
# # REGRESSION WITH OUTLIERS
#
# figure(figsize=(8, 6))
# X, y = make_regression(200, 1, as_table=false, noise=0.5, rng=550, outliers=0.1)
#
# plot(X[:, 1], y, ls="none", marker="o")
#
# gcf()

Start with the Julia code block in issue #70 and inspect the repository's tutorials area to determine where this material belongs. Integrate the synthetic-data examples and plots there, then verify that the tutorial examples run and produce the expected visualizations.