Build Next-Level Apps w/ TensorFlow, Python & Sketch

Explore Android Studio from download to interface, learn Java basics and syntax, and build a simple input-output Android app from start to finish to get you up to speed.

download and install Android Studio on Mac, move it to the Applications folder, and create a new project with an appropriate API level via the SDK manager.

Set up an android emulator in android studio by creating a virtual device with api 25. Run your app on the emulator or a real device, and see hello world.

Learn Java language basics: variables, syntax, type conversion, operations, collections (arrays, lists), control flow (if, switch, loops), functions, and core class concepts (fields, methods, constructors, inheritance, access modifiers, static).

Explore variables in Java by declaring and initializing primitive types—integers, longs, floats, doubles, booleans, characters—and strings, noting default values and strong typing.

Explore variables and basic types, mastering assignment, arithmetic, and conditional operators, type conversions and casting, including string to number parsing and string concatenation, with modulus and plus-equals examples.

Explore arrays and lists in Java, compare immutable arrays with mutable lists, and learn basic setup and operations like retrieving and modifying elements and adding or removing items.

Explore array and list operations, including zero-based indexing, length bounds checks, and updating values, plus get, set, and remove on array lists.

Master control flow with if and switch statements, testing conditions to execute code, using else, else if, and and/or operators, and handling traffic light logic with default and break.

Master while loops as a control flow tool that repeats a block until a condition becomes false, enabling tasks like tallying numbers and iterating through arrays with break and continue.

Explore how for loops replace while loops to iterate through arrays and accumulate a net balance from a transactions list, using for, for each, and safe indexing.

Learn how to declare, implement, and call functions in Java, using a bank transaction example, including return types, parameters, and void return types.

Explore how to define functions with names, return types, and parameters, use promises and return values, and implement robust, type‑checked purchases with cost parameters while understanding variable scope and overloading.

Define and instantiate objects by using classes as blueprints, detailing fields, methods, and constructors in Java. Create instances with new, access attributes and behaviors via dot syntax.

Explore subclass and superclass relationships by defining a strobe light as a subclass of light, inheriting attributes and behavior while adding new properties and overriding methods.

Discover that static variables belong to a class, not instances, and access them via the class name. Learn private, protected, public, and default modifiers for visibility control.

Begin an Android app project by building the UI with xml, connecting the UI to backend code, implementing calculations, variables, and classes, and testing on emulator with UI improvements.

Build a basic Android user interface with constraint layout, an edit text, and a text view, wire IDs and constraints, and implement string resources and hints for user input.

Connect the UI by wiring an edit text, a text view, and a button to the main activity; initialize in onCreate and handle clicks to display results.

Develop a simple Android text processor that reads input, displays it in a text view, prevents empty input, and demonstrates UI beautification with colors, padding, and constraints.

Learn how to download and install PyCharm and Python, choose the free community version, and set up a Python 3.x environment with virtual environments.

Explore PyCharm basics to manage a Python project: open and create files, navigate the project interpreter and virtual environments, run code, and install packages like TensorFlow.

Explore variables in Python by examining integers, floats, booleans, and strings. Learn automatic type inference and use print and type functions to inspect values and types with examples.

Master variables, the assignment operator, and the not operator, and explore arithmetic operators, modulus, and type conversions between integer, float, and string in Python.

Master Python collection types by exploring tuples, lists, and dictionaries. Learn how to store multiple values, access elements by index or key, and compare their use with inventory examples.

Explore operations on tuples, lists, and dictionaries, including retrieving and modifying elements, concatenating tuples, deleting items, and using length, min, and max, along with dictionary keys, values, and items.

Explore control flow with if statements, else if, and else to drive code by conditions, using banking and inventory examples to show and, or, and nesting.

Learn to declare, implement, and call functions in Python, parse promises into functions, and return values while managing starting and current positions through practical examples.

Explore the basics of TensorFlow with Python, including installation and core components for building training and testing machine learning models. Begin from scratch with a real linear regression project.

Explore the core TensorFlow concepts, from computational graphs and tensor inputs to nodes, operations, and placeholders, while prepping data and training and testing a simple linear regression model in Python.

Learn to import TensorFlow in PyCharm, alias it as tf, and troubleshoot installation by configuring the Python interpreter; then explore building a computational graph with constants and tf sessions.

Explore constant nodes in TensorFlow's computational graph and learn how sessions run nodes to reveal their stored values, shapes, and data types.

Explore variable nodes and how they differ from constant nodes, including training and manual changes, initialization, loading, and reassignment within a TensorFlow session.

Explore operation nodes in TensorFlow, including placeholders, constants, and variables, and build a mini computation graph with add and multiply operations and feed dictionaries.

Explore loss functions and optimizers, learn how training and testing minimize loss to improve model accuracy, and apply gradient descent with a learning rate to prepare a linear regression model.