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Google Developer Groups DEVELOPMENT WORKSHOP 2024.pdf
- 1. KICKSTART YOUR DEVELOPMENT JOURNEY 2025 GDG JIIT 128
- 2. Tell kro WHY DEVELOPMENT ??
- 3. WHO IS A DEVELOPER ? A developer is someone who creates, builds, and maintains software, websites, or applications. Developers use programming languages, tools, and frameworks to write code that makes a computer or system perform specific tasks. Here's a breakdown:
- 5. TYPES OF DEVELOPER HTML5 CSS3 Javascript Jquery React.js Angular.js React Native Flutter Swift Ruby Flask Django Express.js Node.js Rest API Nginx Frontend +Backend Next.js CI/CD AWS Docker WebSockets WebRTC Socket io Go Rust Kubernetes Dot-net ProsgresSQL MongoDB Redish
- 6. FRONTEND What is Frontend ? Frontend development refers to building the part of a website or application that users see and interact with directly. It focuses on designing the user interface (UI) and ensuring a great user experience (UX) by creating visually appealing and interactive layouts using technologies like HTML, CSS, and JavaScript.
- 7. FRONTEND Key Components of Frontend Development Structure (HTML): HTML (HyperText Markup Language) is used to define the structure of a webpage. Style (CSS): CSS (Cascading Style Sheets) is used to style the HTML structure. Interactivity (JavaScript): JavaScript adds functionality and makes the website interactive. Frameworks React,Angular,Vue.js,Svelte,Ember.js
- 8. HTML
- 9. CSS
- 10. JAVASCRIPT
- 12. FRAMEWORK
- 13. BACKEND To get Rich🤑 What is Backend ? The backend in development is the part of a web or software application that handles everything happening behind the scenes. It focuses on managing the server, database, and logic that powers the application.
- 14. BACKEND Functionality Server Management - Implementing the business logic that defines how data is created, processed, and delivered to the front end. Optimization - Databases store and manage application data - MySQL, PostgreSQL, MongoDB, Firebase Authentication and Authorization - Implementing secure user authentication (e.g., login and registration). Real-Time Communication - Implementing WebSockets or similar protocols for real-time features (e.g., chat, notifications).
- 15. BACKEND Components Programming Languages - Backend programming languages are used to write the server-side logic that powers the application - Node.js, python Databases - Databases store and manage application data - MySQL, PostgreSQL, MongoDB, Firebase Servers - Servers handle user requests and deliver responses -Apache, Nginx node.js best hai ^_^ Frameworks - Frameworks provide ready-made tools and libraries to simplify backend development - Django, Spring Boot, Express.js APIs - APIs enable communication between different parts of an application or between applications - REST APIs, GraphQL
- 16. BACKEND LANGUAGES Node.js (JavaScript runtime)- A runtime environment that lets you run JavaScript on the server side. Python - Known for its simplicity and versatility, Python is widely used for both small and large backend projects. Java- A robust, high-performance language commonly used for enterprise- grade applications. PHP - A server-side scripting language popular for web development.
- 17. BACKEND DATABASES MySQL- An open-source relational database management system (RDBMS) known for its speed and reliability. PostgreSQL - Another open-source RDBMS, but more advanced than MySQL. MongoDB - A document-based NoSQL database where data is stored in collections as documents (typically in JSON format) Firebase- A real-time NoSQL database offered by Google. All in One Combo
- 18. API BACKEND REST APIs (Representational State Transfer) - RESTful APIs use HTTP methods like GET, POST, PUT, and DELETE to perform actions on data. GraphQL - GraphQL allows you to request exactly the data you need in a single query.
- 19. BACKEND FRAMEWORKS Express.js: minimal and flexible framework for Node.js, ideal for building lightweight APIs quickly Django: feature-rich Python framework that comes with many built-in tools to simplify development, often used for complex, database-driven applications. Spring Boot - Java framework that provides robust, production-ready setups, often used for building large, scalable applications or microservices.
- 20. MERN MongoDB - A NoSQL database that deals with data. Express - A framework for NodeJS and handles GET, PUT, POST, and DELETE functions. React - A JavaScript library for building User Interfaces. NodeJS - An open-source server environment. MEAN The major difference between MERN and MEAN is MERN (written in JavaScript) works on React whereas MEAN deals with Angular (a framework written in TypeScript). CHOOSING A TECHNOLOGY
- 22. VERSION CONTROL What is Version Control? Version Control is a system that helps developers manage changes to source code over time. Why Use Version Control? Track Changes, Collaboration , Revert Changes , Branching and Merging Types of Version Control Systems Local Version Control: Tracks changes on your local machine. Example: RCS (Revision Control System). Centralized Version Control (CVCS): A single server stores all versions. Example: SVN (Apache Subversion). Distributed Version Control (DVCS): Every developer has a complete copy of the project history. Example: Git, Mercurial. SOME BASIC TECH TERMS
- 23. SOME BASIC TECH TERMS GIT Git: The Most Popular Version Control System Key Concepts: Repository (Repo): A directory that Git tracks. 1. Commit: A snapshot of your project at a specific point in time. 2. Branch: A separate line of development. 3. Merge: Combining changes from different branches. 4. Remote Repository: A version of your repository stored on a server (e.g., GitHub). 5. Version Control Platforms GitHub: A popular platform for hosting Git repositories. Use for open-source and private projects. GitLab: Offers CI/CD integration for automated testing and deployment. Bitbucket: Designed for team collaboration with private repo support.
- 24. STEP 1 - LEARN BASICS STEP 2 - CHOOSE A PATH OR TECH YOU WANT TO FOLLOW (MERN OR MEAN) STEP 3 - START WITH FRONTEND STEP 4 - BUILD PROJECT USING THE TECH-STACK LEARNED STEP 5 - CONTINUE WITH BACKEND AND VERSION CONTROL STEP 6 - BUILD FULL STACK PROJECTS STEP 7 - REVISIT THE BASICS!! STEPS TO FOLLOW
- 25. print(“AI/ML”)
- 26. Machine Learning Machine learning is a type of artificial intelligence that performs data analysis tasks without explicit instructions. Machine learning technology can process large quantities of historical data, identify patterns, and predict new relationships between previously unknown data. You can perform classification and prediction tasks on documents, images, numbers, and other data types.
- 27. Artificial Intelligence Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. It enables computers to perform a variety of advanced functions, including the ability to see, understand and translate spoken and written language, analyze data, make recommendations, and more.
- 28. Difference b/w AI and ML Artificial Intelligence Broad field focused on creating intelligent systems. Mimics human abilities like reasoning, problem-solving, and understanding language. Encompasses various techniques, including rule-based systems and advanced methods. Machine Learning Subset of AI focused on data-driven learning. Involves training algorithms to identify patterns and make decisions. Allows systems to improve with more data, used in tasks like recommendation engines and image recognition.
- 29. Generative AI Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. AI technologies attempt to mimic human intelligence in nontraditional computing tasks like image recognition, natural language processing (NLP), and translation.
- 30. Types of ML Techniques Supervised Learning Supervised machine learning is a machine learning technique that uses labeled data sets to train algorithms to predict outcomes and recognize patterns Unsupervised Learning Unsupervised machine learning is a machine learning technique that uses algorithms to analyze unlabeled data and find patterns without human intervention Reinforcement Learning Reinforcement Learning (RL) is a branch of machine learning focused on making decisions to maximize cumulative rewards in a given situation. RL involves learning through experience
- 31. Supervised Machine Learning USES Classification: Spam detection, image classification, sentiment analysis, etc. Regression: Stock price prediction, real estate value estimation, and demand forecasting. Time Series Forecasting: Weather forecasting, sales forecasting, etc. Object Detection: Applications: Autonomous driving, security surveillance, and image search engines. ALGORITHMS Linear Regression Logistic Regression Decision Trees K-Nearest Neighbors (KNN) Naïve Bayes
- 32. Unsupervised Machine Learning USES Clustering: Customer segmentation, image segmentation, document clustering, etc. Anomaly Detection: Fraud detection, network security, equipment failure prediction, and defect detection. Dimensionality Reduction: Image compression, feature extraction, noise reduction, etc. Topic Modeling: Document clustering, content categorization, and summarizing text data. ALGORITHMS K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Apriori Algorithm Gaussian Mixture Models (GMM)
- 33. Reinforcement Machine Learning USES Robotics: Robot Arm Control, Autonomous Robots Game Playing: RL helps create intelligent agents capable of playing and mastering games, from traditional board games to modern video games. Autonomous Vehicles: Self Driving Cars, Drone Navigation, etc. Finance and Trading: Algorithmic Trading, Portfolio Optimization. ALGORITHMS Q-Learning Policy Gradient Methods Proximal Policy Optimization (PPO) Actor-Critic Methods
- 34. Key AI Domains and Uses
- 35. Natural Language Processing Natural Language Processing (NLP) is a field of AI that enables computers to understand, interpret, and generate human language. It is used in applications like machine translation, chatbots, sentiment analysis, and text summarization, allowing machines to process and respond to natural language data more effectively.
- 36. Computer Vision Computer Vision is a field of AI that enables machines to interpret and make decisions based on visual data, such as images or videos. It is used in applications like image recognition, object detection, facial recognition, and autonomous vehicles, allowing computers to understand and analyze visual information similarly to how humans do.
- 37. Robotics Robotics is a field that combines engineering, computer science, and AI to design and build machines capable of performing tasks autonomously or semi-autonomously. Robots are used in a wide range of applications, including manufacturing, healthcare, agriculture, and exploration. They can perform repetitive, dangerous, or complex tasks with precision, improving efficiency and safety in various industries.
- 38. Tools for AI/ML
- 39. Programming Languages Python: The most popular language for AI/ML due to its simplicity, flexibility, and extensive ecosystem of libraries (e.g., TensorFlow, PyTorch, scikit-learn). R: Widely used for statistical analysis, data mining, and machine learning tasks, especially in academia and research. Julia: Known for its high-performance capabilities, often used in scientific computing, AI, and machine learning, particularly when performance is critical.
- 40. ML Libraries and Frameworks TensorFlow: An open-source deep learning library developed by Google. It is used for building complex neural networks and deploying models in production. PyTorch: Developed by Facebook, PyTorch is another deep learning framework known for its dynamic computation graph, making it popular for research and prototyping. scikit-learn: A versatile library for classical machine learning algorithms (regression, classification, clustering), focused on ease of use and integration with other Python tools.
- 41. Data Manipulation and Analysis Tools Pandas: A powerful data manipulation library in Python, used for handling, cleaning, and analyzing data, especially in tabular format (e.g., CSV, SQL). NumPy: Essential for numerical computing in Python, providing support for large multi-dimensional arrays and matrices along with a collection of mathematical functions. Matplotlib/Seaborn: Libraries for data visualization. Matplotlib is used for creating static, interactive, and animated plots, while Seaborn is built on top of Matplotlib and offers a higher-level interface for statistical graphics.
- 42. Model Training Tools Google Colab: A free, cloud-based notebook environment provided by Google, which allows running machine learning models with GPU/TPU support. Jupyter Notebooks: A widely used interactive computing environment for writing and running Python code in a notebook interface. It is great for experiment tracking, visualization, and presenting models.
- 43. Competitions: Hosts data science challenges where participants build machine learning models to solve real-world problems. Datasets: Provides a large collection of free datasets for analysis and modeling. Kaggle Notebooks: Cloud-based environment for writing and running code (similar to Jupyter Notebooks) for data analysis and collaboration. Courses: Offers free, interactive learning courses on data science and machine learning topics. Career Development: Competing and succeeding in Kaggle challenges can boost visibility and credibility in the data science field. Kaggle
- 44. Code Sharing: Hosts open-source AI/ML projects and models for collaboration. Pre-trained Models: Access to pre-trained models and popular algorithms. Collaboration: Enables joint research and contribution to AI/ML libraries. Jupyter Notebooks: Share and collaborate on AI/ML experiments. Dataset Sharing: Share datasets for training and testing models. Tool Integration: Works with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. Community: Contribute to and improve existing AI/ML projects. Github
- 45. Streamlit is an open-source Python library designed for building and sharing interactive web applications for data science and machine learning projects. It allows developers to quickly turn Python scripts into shareable web apps without requiring extensive knowledge of web development. Streamlit
- 46. Popular AI/ML APIs GEMINI API OPENAI API ANTHROPIC API
- 47. Que & Ans!!