Advanced Machine Learning & Data Analysis Projects Bootcamp

Learn to download, install, and launch Android Studio on Mac, set up a new project, configure API levels 25–26, and use the SDK Manager to resolve common compatibility issues.

Explore the Android Studio interface, navigate the project structure, understand activities and XML layout, and learn to add comments and log statements while preparing to run on an emulator.

Learn to set up an Android emulator in Android Studio, create a virtual device, build and run your app, and view logs to debug on real or emulated devices.

Explore Java language basics for Android Studio, covering variables, types, conversions, and operations; collections, control flow, functions, and classes with constructors, inheritance, and access modifiers.

Explore variables in Java by examining primitive types—integers, longs, floats, doubles, booleans, characters—and strings, noting that strings are not primitive, with declaration, initialization, default values, and strongly typed language.

Master variable operations with assignments, arithmetic and conditional operators, modulus, string concatenation, and type conversions including parsing strings to numbers and casting between numeric types.

Compare arrays and lists in Java, noting arrays are immutable while lists are mutable. Learn setup, initialization, and element modification for storing multiple values.

Explore array and list operations, including accessing elements by index (starting at zero), checking length, and avoiding out-of-bounds errors when modifying or removing items.

Discover control flow through if and switch statements, including else and else-if branches, and logical and operators, illustrated with a traffic light example and exhaustive switch cases.

Learn how while loops execute a block of code repeatedly until a condition becomes false, tallying numbers from 1 to 5 and beyond, with break and continue statements.

Explore for loops and their variants, including for, for each, and for-in, and learn to compute net balance by summing a list of transactions.

Learn how to declare, implement, and call functions in Java, including return types, parameters, void, and access modifiers, through a bank purchase example.

Define functions with parameters and return types, using promises and return values, including boolean returns for purchases with a parameterized item cost.

learn how to define a class as a blueprint for objects in Java, using fields, methods, and constructors to model state and behavior.

Explore subclass and superclass relationships by building a strobe light that extends a generic light, inheriting attributes and behavior, and overriding methods to add strobe controls and constructors.

Explore static variables and access modifiers in Java, using class-name access, then review private, protected, and public controls and getters.

Learn to build an Android app from start to finish by designing UI with XML, connecting UI to backend code via variables and methods, and testing on the emulator.

Build a basic Android UI by adding edit text, text view, and a button with constraint layout, IDs, and string resources; connect to main activity and display results.

Connect user interface to the backend by wiring edit text and text view in the main activity, initialize controls, and implement on click to display results.

Implement a simple text processing flow that captures added text, guards against empty input, displays it in the text view, and beautifies the UI with color and padding.

This two-part series guides you through downloading and installing the latest Python, exploring its interface, and quickly building, running, and debugging Python projects, with a five-part fundamentals overview.

Install PyCharm community edition and Python 3.x, configure a new PyCharm project with a virtual environment, and organize project files in your chosen folder for machine learning work.

Navigate PyCharm's project view and interpreter settings to manage a Python workflow. Create and run Python files, install packages, and configure plugins and Python versions.

Explore variables by examining four common types—integers, floats, booleans, and strings—along with print and type functions, dynamic typing, and basic type conversions in Python.

Explore variable operations and conversions in Python: assign values, negate booleans, perform arithmetic and modulus, and convert between int, float, and string while using concatenation and comparisons.

Explore Python collections by comparing tuples, lists, and dictionaries, learn how to store multiple values, access by index or key, and map items in an inventory.

Explore operations on tuples, lists, and dictionaries, including retrieving elements, mutating lists, concatenating tuples, deleting items, and using length, min, and max for quick insight.

Master control flow with if statements, else if, and else branches, using tests and conditions; preview while loops and for loops with examples like bank balances and inventory.

Master while and for loops to automate repetitive tasks, such as counting and summing totals or iterating through arrays. Learn how break and continue control flow and avoid infinite loops.

Explore how to declare, implement, and call functions in Python, work with promises, pass parameters, and return values; learn to modularize code with starting position, by amount, and position updates.

Explore the fundamentals of classes and objects in Python through a game character example, covering attributes, state, behavior, methods, initialisers, and creating new instances.

Beginner-friendly tour of TensorFlow basics in Python, covering installation, components for building training and testing machine learning models, and a from-scratch linear regression project.

Explore the core concepts of TensorFlow, including the computational graph and node types, and learn to build and train a simple linear regression model in Python.

Learn to set up and import TensorFlow in PyCharm, resolve installation issues, install the package, and access the library to begin building a computational graph with constants and sessions.

Explore constant nodes and sessions in a computational graph, learning how to store unchanging values, define tensor shapes and data types, and run nodes to observe current values.

Master variable nodes and their differences from constant nodes, including manual reassignment and training-driven changes. Learn to initialize and manage variable loads, shapes, data types, and trainability.

Explore placeholder nodes as inputs in computational graphs, assigned at runtime via a feed dictionary. Learn data type and shape guard inputs, with examples comparing placeholders to variables and constants.

Explore operation nodes that perform arithmetic on placeholders, constants, and variables to form a mini computational graph, including a simple linear regression with y = w*x + b.

Explore loss functions and optimizers, understand training and testing phases, and apply gradient descent with learning rate to minimize loss while building a linear regression model.

Build a linear regression model in TensorFlow, defining w, b, and x, training with squared loss and gradient descent to fit a slope and intercept, then validate with new inputs.

Explore three advanced models to boost efficiency: real time image recognition, real time object localization, and text summarization, using pre-built TensorFlow models for rapid, robust performance.

Explore TensorFlow Lite and its mobile-optimized interpreter, including flatbuffer, hardware-accelerated on-device inference, and quantized kernels, with a prebuilt demo and Android Studio setup.