Machine Learning with Julia Programming for Everyone

Explore Julia, an open-source, fast, high-level language developed at MIT for data science and high-performance computing, and learn to download, install, and set up with Jupiter via the Anaconda distribution.

Explore the Julia environment and write your first program, hello world, with double-quoted strings and the print command; use semicolons to suppress output and control L to clear.

Master Julia environment management and interactive help by using keyboard shortcuts, workspace resets, and package commands, with rich read-eval-print loop guidance.

Explore managing Julia packages with the Pkg prompt, including add and help. Install IJulia and Plots, then use IPython notebook and Jupyter.

Learn to set up julia in jupyter notebook, install i julia and anaconda, create a project folder, write julia in notebook cells, and run code with ctrl+enter or shift+enter.

Explore automatic operator precedence in Julia, where division takes priority, then multiplication, then plus and minus. See how using parentheses clarifies evaluation and helps anticipate results.

Explore variable operations in Julia with an emi loan example, defining loan amount, interest rate, and tenure as changing variables and performing arithmetic. Also cover integer, float, and string variables.

Discover how to use comments in Julia to document code, calculations, and operator precedence. Learn best practices for elegant, readable documentation that stays with the code.

Learn to write correct Julia variable names by avoiding leading numbers, forbidden symbols, and reserved keywords, and by using underscores for multiword names.

Explore Julia arrays as ordered collections that span 1d, 2d, and 3d shapes, with numeric, string, or mixed types. Learn type coercion, explicit typing, and creating random and multi-column arrays.

Learn how to create and manipulate ranges in Julia using collect, including integer and decimal sequences, stepping by 1 or 0.5, and accessing range elements by index.

Discover how tuples in Julia are declared with parentheses, are immutable, and accessed by index; compare to arrays and explore named tuples for stable data like student marks.

Explore dictionaries in Julia as key-value data structures, create and access entries with string or symbol keys, and learn to use haskey, delete!, keys, values, and merge.

Learn how to work with sets in Julia, including creating unordered collections without duplicates, testing membership, and performing push, union, intersection, and difference operations.

Learn to manipulate date and time in Julia using the dates module, work with Date, DateTime, and UTC time zone, perform arithmetic, and format dates for analysis.

Explore conditional statements in Julia, using assignment and equality operators, compare values with greater than or equal to and less than, and apply and or with the ternary style.

Explore loops in Julia, including for and while loops, and learn to iterate over arrays, dictionaries, sets, and ranges for data manipulation with formatted output.

Master efficient looping in Julia with list, set, and dictionary comprehensions. Build two-dimensional arrays, generate sets and dictionaries, and apply conditional comprehensions for even, odd, and prime values.

Explore string manipulation in Julia: define strings, measure length with lastindex, index and slice with ranges, join and repeat, split, parse numbers, replace, and persist changes.

Learn to write and use functions in Julia, from simple one-line to multi-parameter forms, with return and if logic, and apply BMI index, EMI calculations, and meters to inches conversions.

Explore formatting in Julia by using printf-style macros to format numbers, strings, characters, and floats. Learn how to control decimal precision and display scientific notation for common data types.

Import csv files in Julia using the CSV package, upload the file, and load it into a dataframe to inspect columns, describe data, and access rows.

Visualize data in Julia by installing and using the plots package, create line, scatter, bar, and histogram charts, and assemble a dashboard from data frames like iris data.

Connect Julia to a light-weight database with sequel lite, create and manage tables, insert and query data using select, and visualize results with data frames, using Chinook as an example.

Learn to call Python packages from Julia using the pi call interface, importing libraries like NumPy and math, to perform array operations and functions such as cos, sin, and sqrt.

Learn machine learning in Julia by applying logistic regression, decision trees, and random forests to the iris dataset, split data for training and testing, and evaluate accuracy.