Learn about Python and Blockchain: The Complete Guide!

Explore how decentralized apps work and differ from regular web apps, including how they run on a blockchain and the role of smart contracts, with a path to Solidity.

Explore how the Ethereum blockchain supports decentralized apps with consistent mining rewards, computation-based fees, faster transactions, and oracles, contrasted with Bitcoin and Ethereum Classic.

Explain how decentralized apps run on a blockchain, differ from centralized web apps, and use oracles to fetch external data, highlighting fees and censorship implications.

Learn how decentralized apps run on blockchains by fetching and modifying data through transactions, powered by smart contracts, with gas costs, miners, and blocks.

Learn how smart contracts on the blockchain enforce agreed actions with built-in safeguards for gas limits and transfers, illustrated by a Solidity coin contract.

Learn how blockchain stores transactions, how decentralized apps run on the chain with gas fees, and how smart contracts (often in Solidity) secure and automate transactions with ether.

Explore five Solidity language basics topics in Remix in the browser—common types, arrays, functions, destructs, and creating contracts and components, plus setup with pragma, default constructor, and imports.

Explore solidity boolean variables, default false values, and boolean operations like negation and and/or, then apply them to checks such as does have enough money and is eligible.

Explore signed and unsigned integers, basic arithmetic and assignment operators, and type conversions using a balance and transaction example, while noting bit widths and gas considerations in solidity.

Explore common types in Solidity, focusing on strings. Strings are arrays of characters stored as text, require consistent quotes, and length is obtained by converting to bytes.

Explore common types by examining 20-byte hex addresses, querying balances, and transferring ether between accounts, with sender address derived from message sender and hexadecimal concepts emphasized.

Demonstrate 1D and 2D arrays in Solidity, compare memory versus storage, and show declaring, initializing, and accessing integer and boolean arrays, including arrays of arrays.

Explore arrays in practice by performing get, set, add, and delete operations on storage and memory arrays, learning indexing, length, and runtime errors from out-of-bounds access.

Explore storage versus memory and value versus reference types in Solidity, and learn how arrays behave, including dynamic versus static storage and gas implications.

Explore Solidity functions syntax, constructors, and events to create and log contract behavior, handle parameters, and return values while mastering in-contract execution.

Explore function parameters and return values in solidity, learning how to pass values, modify memory-stored promises, and return single or multiple outputs.

Explore Solidity function semantics by examining visibility modifiers (external, public, internal, private), view, pure, payable, fallback, and function modifiers, and see how they govern access, state interaction, and payments.

Explore how to use if statements, else if, and else to control flow in Solidity, testing balances against transactions, returning booleans, and handling messages with nested conditions.

Explore while loops in Solidity, learn their syntax, and how break, continue, and return control flow exit conditions. Understand preventing infinite loops and gas implications.

Explore for loops in Solidity, comparing them to while loops, with a start point, end point, and increment, and learn to iterate through arrays and total transactions.

Define a Solidity user struct with first name, last name, balance, and a transactions array, and instantiate it in storage or memory to manage persistent versus ephemeral data.

Explore Solidity mappings as key-value stores, with default values and storage considerations. Learn to map uints to structs like person, manage IDs, and build scalable mappings with arrays.

Discover how a contract transaction records the message sender, the value sent, and gas usage, including remaining gas, using from address and message value concepts for mappings and voting.

Learn how enumerations create a new type to restrict values to a finite set, using the days of the week as an example to avoid strings or integers.

Explore solidity contracts by building a contacts list: define a contract with owner, a contact struct, and functions to add and retrieve contacts using for loops and keccak256 string comparisons.

Explore contract instantiation, access, and modification using getters and setters, including public versus private access, constructors, and the memory versus storage distinction in Solidity.

Learn inheritance in Solidity, covering super contracts, constructors, and function overriding with the super keyword. See how a subcontract inherits state and functions and extends behavior.

Explore solidity basics: variables, booleans and numbers, strings, addresses; memory versus storage; functions and events; structs, mappings, enums; contracts, inheritance, interfaces, and remix-based app creation.

Develop a simple decentralized voting contract in Solidity with candidate and voter structs, mappings to track voters, and functions to cast votes, count votes, and display results in Remix.

Implement a smart contract that manages a decentralized vote between Coke and Pepsi, including a constructor that sets the chairperson, casting votes, and a function to declare the winner.

Build the app interface with HTML, CSS, and JavaScript to create a basic voting page that interfaces with smart contracts in a decentralized app.

Apply css to center content, style the table, and adjust typography and layout using ids and divs, preparing the interface for javascript interactivity and smart contract integration.

Learn how to build a web interface for a decentralized voting app using JavaScript, updating Coke and Pepsi vote counts and preparing for smart contract integration.

Install and configure npm, install and run testrpc to simulate a blockchain environment, and add web3 version 0.20.6 to connect the smart contract to the interface for testing.

Connect your app interface to a smart contract using web3 on a local host, wiring HTML, CSS, and JavaScript to access the contract’s methods and variables.

Learn how to replace JavaScript vote logic with smart contract functions in a decentralized app, including a cost vote function that checks voter status and updates candidate votes.

Demonstrate replacing JavaScript vote logic with the smart contract getWinner to announce the winner. Explain decoding the bytes32 winner name with web3 for output and distinguishing transactions from read-only calls.

In this bonus section, update a decentralized app to handle ties, add a requires clause that restricts get winner to the chairperson, and set a safe default.

Build and test a simple decentralized voting app on a blockchain using a solidity smart contract and a web3 localhost interface, all run on a local blockchain simulation.

Explore blockchain concepts and decentralized apps. Build a complete decentralized app from start to finish, learn Solidity basics, test on a simulated network, and deploy a voting smart contract.

Learn to use PyCharm to easily create and run Python projects with a graphical interface, real-time debugging, code completion, straightforward package installation, and no prerequisites.

Download and install PyCharm community edition and the latest Python version, configure the project interpreter, and create a new project across Mac, Windows, and Linux platforms.

Explore the PyCharm interface, from menus and toolbars to the project interpreter, code navigation, and split-screen windows, then learn to customize appearance and run workflows.

Learn how to run Python files by creating Python files in root directory with a .py extension, using comments with # and print statements to test output in run window.

Build and run a simple python add program that prompts for two numbers, converts inputs to integers, sums them, and prints the result while handling non-numeric input with try-except.

Explore Python basics by learning variables and data types (numbers, strings, booleans), collections (tuples, lists/arrays, dictionaries/maps), control flow, functions, parsing and promises, and classes with instantiation and subclassing.

Master variables and Python's four basic types—integers, floats, booleans, and strings—using simple syntax and type checks. Learn to print, inspect types with type(), and explore basic type conversion.

Master Python variable operations by applying assignment, arithmetic, modulus, floor division, and other operators, and explore type conversion, string concatenation, and basic comparisons.

Learn how to use dictionaries in Python to map keys to values, access by key, and perform dictionary operations such as print, insert, and reassign, with Avengers examples.

Master how control flow uses true or false tests with if, elif, and else to choose code blocks, emphasizing Python indentation, while and for loops, and logical operators.

Explore how Python loops work, using while and for loops to automate repetitive tasks, traverse lists, and control flow with break and continue to avoid infinite loops.

Explore basic function implementation and execution in Python, define and call functions, manage scope with local and global variables, and prepare for parameters and return values.

Learn how to define functions with parameters in Python, pass values, and return outputs, including handling single and multiple return values and practical examples.

Learn Python classes and objects by building a superhero class with name and health attributes, an initializer, and a take_damage method, preparing for subclassing and inheritance.

Explore how a subclass inherits attributes from a superclass, adds its own variables and methods, uses the superclass initializer, and distinguishes class versus instance variables.

Learn Python basics: variables and basic types, including floats, booleans, and strings; collections, control flow, functions, and classes with inheritance, plus promises and return values.

Master the basics of machine learning by exploring data processing, building and training a model, and evaluating results with supervised learning and a convolutional neural network for image classification.

Discover how neural networks use layers, weights, biases, and activation functions to recognize patterns and map inputs to outputs. Training adjusts weights to shape pathways for image classification.

Explore how convolutional neural networks use convolutions with kernels or filters and max pooling to process image data, highlight features, and reduce input size before dense layers.

Learn what TensorFlow is and why we use it, then compare it with Keras, a high-level API built on TensorFlow. Install them in Python and build, train, and test models.

Learn Keras syntax for building a sequential convolutional neural network in Python, adding convolutional, max pooling, and dense layers with dropout, then compile, train, evaluate, and predict.

Explore activation functions from identity and binary step to sigmoid, tanh, ReLU, and Leaky ReLU, and learn how softmax yields final class probabilities in CNNs.

Explore the cifar-10 dataset by downloading, extracting, and loading the data in Python, then access the train and test images and labels for 10 classes.

Explore how 32x32 color images are stored as train and test image-label pairs, examine their row-column structure, and learn to normalize data and convert labels to categorical for model input.

Format input images for image classification by applying one-hot encoding to labels, normalizing pixel values to 0-1, and flattening arrays with a reshape function for model compatibility.

Construct a CNN by stacking convolutional, max-pooling, flatten, dense, and dropout layers with 3x3 kernels, ReLU activation, max-norm constraints, and a softmax output for 10 classes.

Import and format inputs, set up training data and labels, then compile and run a training loop for a multiclass classifier using a sagd optimizer and cross-entropy loss, tracking accuracy.

Explore gradient descent and optimizers, compare batch, stochastic, and mini-batch approaches, and understand momentum to improve neural network training.