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| import streamlit as st | |
| from streamlit_lottie import st_lottie | |
| import requests | |
| def load_lottieurl(url: str): | |
| """Fetch Lottie animation JSON from a URL.""" | |
| try: | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| return response.json() | |
| except requests.exceptions.RequestException: | |
| return None | |
| # ========== UPDATED CSS ========== | |
| st.markdown(""" | |
| <style> | |
| :root { | |
| --background-color: #f8f9fa; | |
| --text-color: #212529; | |
| --primary-color: #007acc; | |
| --secondary-color: #005b96; | |
| --card-bg: #ffffff; | |
| --text-muted: #6c757d; | |
| } | |
| @media (prefers-color-scheme: dark) { | |
| :root { | |
| --background-color: #0e1117; | |
| --text-color: #e5e5e5; | |
| --primary-color: #29b6f6; | |
| --secondary-color: #90caf9; | |
| --card-bg: #1f2937; | |
| --text-muted: #a3a3a3; | |
| } | |
| } | |
| body { | |
| margin: 0; | |
| padding: 0; | |
| font-family: 'Roboto', sans-serif; | |
| background-color: var(--background-color) !important; | |
| color: var(--text-color) !important; | |
| } | |
| h1 { | |
| font-size: 2.5rem !important; | |
| color: var(--primary-color) !important; | |
| text-align: center; | |
| margin-bottom: 15px; | |
| border-bottom: 2px solid var(--primary-color); | |
| padding-bottom: 0.5rem; | |
| } | |
| h2 { | |
| font-size: 2rem !important; | |
| color: var(--secondary-color) !important; | |
| margin: 1.5rem 0 1rem !important; | |
| } | |
| h3 { | |
| font-size: 1.5rem !important; | |
| margin: 1rem 0 0.5rem !important; | |
| } | |
| p { | |
| font-family: 'Georgia', serif; | |
| color: var(--text-color) !important; | |
| line-height: 1.6; | |
| text-align: justify; | |
| } | |
| .content-block { | |
| margin: 1.5rem 0; | |
| padding: 1.5rem; | |
| background: var(--card-bg); | |
| border-radius: 10px; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.1); | |
| } | |
| .about-author { | |
| background-color: var(--card-bg) !important; | |
| border-radius: 10px; | |
| padding: 25px; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| margin: 20px auto; | |
| max-width: 700px; | |
| text-align: center; | |
| color: var(--text-color) !important; | |
| } | |
| .social-icons { | |
| display: flex; | |
| justify-content: center; | |
| gap: 20px; | |
| margin-top: 15px; | |
| } | |
| .social-icons a img { | |
| width: 40px; | |
| height: 40px; | |
| transition: transform 0.3s ease-in-out; | |
| } | |
| .social-icons a img:hover { | |
| transform: scale(1.2); | |
| } | |
| footer { | |
| margin-top: 50px; | |
| text-align: center; | |
| font-family: 'Georgia', serif; | |
| color: var(--text-muted) !important; | |
| } | |
| .css-1d391kg, .css-1d391kg * { | |
| background-color: var(--card-bg) !important; | |
| color: var(--text-color) !important; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # ========== NAVIGATION (UNCHANGED) ========== | |
| chapters = [ | |
| "Foundation", "ML Project Lifecycle", "Core Algorithms", "Model Evaluation", | |
| "Data Handling", "Computer Vision Basics", "Natural Language Processing (NLP)", | |
| "Deployment & Tools" | |
| ] | |
| selected_chapter = st.sidebar.radio("Select Chapter", chapters) | |
| if selected_chapter == "Foundation": | |
| foundation_main = st.sidebar.radio("Select Page", [ | |
| "Home", "Introduction to Data Science", "Machine Learning vs Deep Learning" | |
| ]) | |
| if foundation_main == "Introduction to Data Science": | |
| selected_subtopic = st.sidebar.radio("Explore More", [ | |
| "Understanding Intelligence", "AI Tools: ML, DL, and Gen‑AI", | |
| "Real‑Life Analogies and Examples", "What is Data Science?", | |
| "The Role of a Data Scientist", "Why AI and Data Science Matter", "Did You Know?" | |
| ]) | |
| elif foundation_main == "Machine Learning vs Deep Learning": | |
| st.sidebar.radio("Explore Comparison", [ | |
| "Understanding Machine Learning and Deep Learning", "Comparison Table for ML vs DL" | |
| ]) | |
| # Other navigation sections remain unchanged... | |
| # ========== CONTENT RENDERING ========== | |
| content_rendered = False | |
| if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science": | |
| content_rendered = True | |
| if selected_subtopic == "Understanding Intelligence": | |
| st.markdown("# 🧠 Understanding Intelligence") | |
| with st.container(): | |
| st.markdown("### Natural vs Artificial Intelligence") | |
| st.markdown(""" | |
| <div class="content-block"> | |
| <h3 style='color: var(--primary-color);'>Natural Intelligence 🐾</h3> | |
| <p>Innate cognitive abilities found in living organisms:</p> | |
| <ul> | |
| <li>Dogs learning commands through repetition</li> | |
| <li>Human problem-solving capabilities</li> | |
| <li>Bird migration patterns</li> | |
| </ul> | |
| </div> | |
| <div class="content-block"> | |
| <h3 style='color: var(--primary-color);'>Artificial Intelligence 🤖</h3> | |
| <p>Machine-based systems demonstrating intelligent behavior:</p> | |
| <ul> | |
| <li>Netflix's recommendation engine</li> | |
| <li>Google Maps traffic predictions</li> | |
| <li>Voice assistants like Alexa</li> | |
| </ul> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI": | |
| st.markdown("# 🔧 AI Toolkit Breakdown") | |
| with st.container(): | |
| st.markdown(""" | |
| <div class="content-block"> | |
| <h3>Machine Learning (ML)</h3> | |
| <p>🎯 <strong>Purpose:</strong> Pattern recognition & decision making</p> | |
| <p>👶 <strong>Analogy:</strong> Teaching toddler to recognize fruits</p> | |
| <p>🚀 <strong>Applications:</strong></p> | |
| <ul> | |
| <li>Spam email filtering</li> | |
| <li>Stock price prediction</li> | |
| <li>Customer churn analysis</li> | |
| </ul> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with st.container(): | |
| st.markdown(""" | |
| <div class="content-block"> | |
| <h3>Deep Learning (DL)</h3> | |
| <p>🎯 <strong>Purpose:</strong> Complex data processing</p> | |
| <p>🧠 <strong>Structure:</strong> Neural networks with multiple layers</p> | |
| <p>🚀 <strong>Applications:</strong></p> | |
| <ul> | |
| <li>Facial recognition systems</li> | |
| <li>Medical image analysis</li> | |
| <li>Voice-controlled assistants</li> | |
| </ul> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Other subtopics updated similarly... | |
| # ========== HOME PAGE CONTENT ========== | |
| if not content_rendered: | |
| st.markdown("# Mastering Machine Learning: From Basics To Brilliance 🚀🤖") | |
| st.markdown("## Your Gateway to Data Science Mastery") | |
| lottie_animation = load_lottieurl("https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json") | |
| if lottie_animation: | |
| st_lottie(lottie_animation, height=250) | |
| with st.container(): | |
| st.markdown("## About This Application") | |
| st.markdown(""" | |
| <div class="content-block"> | |
| A comprehensive learning platform covering: | |
| <ul> | |
| <li><strong>Fundamental Concepts:</strong> Build strong theoretical foundations</li> | |
| <li><strong>Practical Implementation:</strong> Real-world project workflows</li> | |
| <li><strong>Industry Best Practices:</strong> Professional development techniques</li> | |
| </ul> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Author Section | |
| st.markdown(""" | |
| <div class="about-author"> | |
| <h2>About the Author</h2> | |
| <p> | |
| Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist | |
| passionate about transforming raw data into actionable insights through | |
| machine learning and AI technologies. | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Social Links | |
| st.markdown(""" | |
| <div class="social-icons"> | |
| <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| </a> | |
| <a href="https://github.com/YashGupta018" target="_blank"> | |
| <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| </a> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Footer | |
| st.markdown(""" | |
| <footer> | |
| <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| </footer> | |
| """, unsafe_allow_html=True) | |
| # ------------------------------------------------------------------------------------------------------------------------------------- | |
| # import streamlit as st | |
| # from streamlit_lottie import st_lottie | |
| # import requests | |
| # # Function to load Lottie animation from a URL | |
| # def load_lottieurl(url: str): | |
| # """Fetch Lottie animation JSON from a URL.""" | |
| # try: | |
| # response = requests.get(url) | |
| # if response.status_code == 200: | |
| # return response.json() | |
| # except requests.exceptions.RequestException: | |
| # return None | |
| # # CSS Styling for light and dark modes with adaptive theme support | |
| # st.markdown(""" | |
| # <style> | |
| # :root { | |
| # --background-color: #f8f9fa; | |
| # --text-color: #212529; | |
| # --primary-color: #007acc; | |
| # --secondary-color: #005b96; | |
| # --card-bg: #ffffff; | |
| # --text-muted: #6c757d; | |
| # } | |
| # @media (prefers-color-scheme: dark) { | |
| # :root { | |
| # --background-color: #0e1117; | |
| # --text-color: #e5e5e5; | |
| # --primary-color: #29b6f6; | |
| # --secondary-color: #90caf9; | |
| # --card-bg: #1f2937; | |
| # --text-muted: #a3a3a3; | |
| # } | |
| # } | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color) !important; | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color) !important; | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color) !important; | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg) !important; | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color) !important; | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted) !important; | |
| # } | |
| # /* Sidebar adjustments */ | |
| # .css-1d391kg, .css-1d391kg * { | |
| # background-color: var(--card-bg) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # </style> | |
| # """, unsafe_allow_html=True) | |
| # # Sidebar Navigation | |
| # chapters = [ | |
| # "Foundation", | |
| # "ML Project Lifecycle", | |
| # "Core Algorithms", | |
| # "Model Evaluation", | |
| # "Data Handling", | |
| # "Computer Vision Basics", | |
| # "Natural Language Processing (NLP)", | |
| # "Deployment & Tools" | |
| # ] | |
| # selected_chapter = st.sidebar.radio("Select Chapter", chapters) | |
| # # Nested page selection based on chapter | |
| # if selected_chapter == "Foundation": | |
| # foundation_main = st.sidebar.radio("Select Page", [ | |
| # "Home", | |
| # "Introduction to Data Science", | |
| # "Machine Learning vs Deep Learning" | |
| # ]) | |
| # if foundation_main == "Introduction to Data Science": | |
| # selected_subtopic = st.sidebar.radio("Explore More", [ | |
| # "Understanding Intelligence", | |
| # "AI Tools: ML, DL, and Gen‑AI", | |
| # "Real‑Life Analogies and Examples", | |
| # "What is Data Science?", | |
| # "The Role of a Data Scientist", | |
| # "Why AI and Data Science Matter", | |
| # "Did You Know?" | |
| # ]) | |
| # elif foundation_main == "Machine Learning vs Deep Learning": | |
| # st.sidebar.radio("Explore Comparison", [ | |
| # "Understanding Machine Learning and Deep Learning", | |
| # "Comparison Table for ML vs DL" | |
| # ]) | |
| # elif selected_chapter == "ML Project Lifecycle": | |
| # stage = st.sidebar.radio("Select Stage", ["Life Cycle of ML Project"]) | |
| # if stage: | |
| # st.sidebar.radio("Subtopic", [ | |
| # "Problem Statement", | |
| # "Data Collection", | |
| # "Data Preprocessing", | |
| # "Exploratory Data Analysis (EDA)", | |
| # "Feature Engineering", | |
| # "Model Selection", | |
| # "Model Training", | |
| # "Model Evaluation & Tuning", | |
| # "Model Deployment", | |
| # "Monitoring" | |
| # ]) | |
| # elif selected_chapter == "Core Algorithms": | |
| # st.sidebar.radio("Select Algorithm", [ | |
| # "Linear Regression", | |
| # "Logistic Regression", | |
| # "k‑Nearest Neighbors (kNN)", | |
| # "Decision Trees", | |
| # "Support Vector Machines (SVM)", | |
| # "Ensemble Techniques" | |
| # ]) | |
| # elif selected_chapter == "Model Evaluation": | |
| # metric_main = st.sidebar.radio("Select Topic", ["Performance Metrics"]) | |
| # if metric_main: | |
| # st.sidebar.radio("Choose Metric", [ | |
| # "Accuracy, Precision, Recall", | |
| # "Confusion Matrix", | |
| # "ROC‑AUC" | |
| # ]) | |
| # elif selected_chapter == "Data Handling": | |
| # data_main = st.sidebar.radio("Select Topic", [ | |
| # "Data Types Overview", | |
| # "Data Cleaning", | |
| # "Feature Engineering" | |
| # ]) | |
| # if data_main == "Data Types Overview": | |
| # st.sidebar.radio("Choose Type", [ | |
| # "Structured Data (SQL, Excel)", | |
| # "Semi‑Structured Data (JSON, XML)", | |
| # "Unstructured Data (Images, Text)" | |
| # ]) | |
| # elif selected_chapter == "Computer Vision Basics": | |
| # cv_topic = st.sidebar.radio("Select Topic", ["Image Processing", "OpenCV Basics"]) | |
| # if cv_topic == "Image Processing": | |
| # st.sidebar.radio("Subtopic", [ | |
| # "Color Spaces", | |
| # "Image Augmentation", | |
| # "Splitting/Merging Images" | |
| # ]) | |
| # elif selected_chapter == "Natural Language Processing (NLP)": | |
| # st.sidebar.radio("Select Topic", ["NLP Introduction", "Text Preprocessing"]) | |
| # elif selected_chapter == "Deployment & Tools": | |
| # st.sidebar.radio("Select Tool", [ | |
| # "Model Deployment", | |
| # "Working with Excel/CSV", | |
| # "SQL for Data Science" | |
| # ]) | |
| # # ======== NEW CONTENT RENDERING ======== | |
| # content_rendered = False | |
| # if selected_chapter == "Foundation" and foundation_main == "Introduction to Data Science": | |
| # content_rendered = True | |
| # # Load animations | |
| # brain_animation = load_lottieurl("https://lottie.host/8d7bdc88-7e11-44b5-995a-6561230e54a1/4X3p3YVQZ5.json") | |
| # ml_animation = load_lottieurl("https://lottie.host/5b6292ff-aad4-4a34-8c0a-4c4cd186f80e/5ZVgB9Q9kF.json") | |
| # art_animation = load_lottieurl("https://lottie.host/0d0cf470-8cef-4a0a-8980-3f2d5e573e98/5XvjOZPmhG.json") | |
| # if selected_subtopic == "Understanding Intelligence": | |
| # st.header("🧠 Understanding Intelligence") | |
| # if brain_animation: | |
| # st_lottie(brain_animation, height=200, key="brain") | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # st.markdown(""" | |
| # ### Natural Intelligence 🐾 | |
| # **Definition:** Innate intelligence in living beings | |
| # - Dogs learning tricks | |
| # - Humans solving puzzles | |
| # - Birds building nests instinctively | |
| # """) | |
| # with col2: | |
| # st.markdown(""" | |
| # ### Artificial Intelligence 🤖 | |
| # **Definition:** Human-like intelligence in machines | |
| # - Netflix recommendations | |
| # - Google Maps routing | |
| # - Alexa voice assistant | |
| # """) | |
| # elif selected_subtopic == "AI Tools: ML, DL, and Gen‑AI": | |
| # st.header("🔧 AI Toolkit Breakdown") | |
| # if ml_animation: | |
| # st_lottie(ml_animation, height=250, key="ml") | |
| # tabs = st.tabs(["Machine Learning", "Deep Learning", "Generative AI"]) | |
| # with tabs[0]: | |
| # st.markdown(""" | |
| # **Machine Learning (ML)** | |
| # 🎯 **Purpose:** Pattern recognition & decision making | |
| # 👶 **Analogy:** Teaching toddler to recognize fruits | |
| # 🚀 **Applications:** | |
| # - Spam email filtering | |
| # - Stock price prediction | |
| # - Customer churn analysis | |
| # """) | |
| # with tabs[1]: | |
| # st.markdown(""" | |
| # **Deep Learning (DL)** | |
| # 🎯 **Purpose:** Complex data processing | |
| # 🧠 **Structure:** Neural networks with multiple layers | |
| # 🚀 **Applications:** | |
| # - Facial recognition systems | |
| # - Medical image analysis | |
| # - Voice-controlled assistants | |
| # """) | |
| # with tabs[2]: | |
| # st.markdown(""" | |
| # **Generative AI** | |
| # 🎯 **Purpose:** Creative content generation | |
| # 🎨 **Analogy:** Digital artist with infinite ideas | |
| # 🚀 **Applications:** | |
| # - ChatGPT conversations | |
| # - DALL·E image creation | |
| # - AI-composed music | |
| # """) | |
| # elif selected_subtopic == "Real‑Life Analogies and Examples": | |
| # col1, col2 = st.columns([2, 1]) | |
| # with col1: | |
| # st.header("🎨 Learning vs Generating") | |
| # st.markdown(""" | |
| # **Art Analogy:** | |
| # - ML = Sketching with pencil (foundations) | |
| # - DL = Inking with pen (details) | |
| # - Gen-AI = Color painting (creation) | |
| # **Child Development Analogy:** | |
| # 1. Learn alphabet → ML (pattern recognition) | |
| # 2. Write essays → DL (complex processing) | |
| # 3. Create poetry → Gen-AI (original content) | |
| # """) | |
| # with col2: | |
| # if art_animation: | |
| # st_lottie(art_animation, height=300, key="art") | |
| # elif selected_subtopic == "What is Data Science?": | |
| # st.header("🔍 Data Science Demystified") | |
| # st.image("https://miro.medium.com/v2/resize:fit:1400/1*K7nYl2D2QO5B9v6U-4yQxA.png", | |
| # width=600, caption="Data Science Process Flow") | |
| # st.markdown(""" | |
| # **Three Pillars of Data Science:** | |
| # 1. 📥 Data Collection: | |
| # - Databases, APIs, IoT sensors | |
| # - Structured & unstructured data | |
| # 2. 🧠 Data Analysis: | |
| # - Statistical modeling | |
| # - Pattern identification | |
| # 3. 📊 Data Visualization: | |
| # - Interactive dashboards | |
| # - Business intelligence tools | |
| # """) | |
| # elif selected_subtopic == "The Role of a Data Scientist": | |
| # st.header("👨💻 Data Scientist's Toolkit") | |
| # st.markdown(""" | |
| # **Core Responsibilities:** | |
| # - Build predictive models for business outcomes | |
| # - Analyze customer behavior patterns | |
| # - Optimize operational efficiency | |
| # **Essential Skills Matrix:** | |
| # | Technical Skills | Business Skills | | |
| # |------------------|------------------| | |
| # | Python/R | Domain Knowledge | | |
| # | SQL | Storytelling | | |
| # | ML Frameworks | Problem Solving | | |
| # """) | |
| # elif selected_subtopic == "Why AI and Data Science Matter": | |
| # st.header("🌍 Transformative Impact") | |
| # st.markdown(""" | |
| # **Industry Revolution:** | |
| # - Healthcare: Early disease detection (40% faster diagnosis) | |
| # - Agriculture: AI-powered yield prediction (+25% productivity) | |
| # - Entertainment: Personalized content recommendations | |
| # **Economic Impact:** | |
| # > "By 2030, AI could contribute up to $15.7 trillion to global economy" | |
| # -*PwC Global AI Study* | |
| # """) | |
| # elif selected_subtopic == "Did You Know?": | |
| # st.header("🤖 AI in Action: Surprising Uses") | |
| # cols = st.columns(3) | |
| # cols[0].markdown(""" | |
| # **Medical Imaging** | |
| # - 92% accuracy in tumor detection | |
| # - Reduces diagnosis time by 60% | |
| # """) | |
| # cols[1].markdown(""" | |
| # **Smart Farming** | |
| # - Automated irrigation systems | |
| # - Pest prediction algorithms | |
| # """) | |
| # cols[2].markdown(""" | |
| # **Creative AI** | |
| # - AI-written novels | |
| # - Algorithmic music composition | |
| # """) | |
| # st.video("https://www.youtube.com/watch?v=JMUxmLyrhSk") | |
| # # ======== ORIGINAL PAGE ELEMENTS ======== | |
| # if not content_rendered: | |
| # # Title and Tagline | |
| # st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖") | |
| # st.markdown("## Your Gateway To Become Master In Data Science") | |
| # # Display Lottie animation | |
| # animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json" | |
| # lottie_animation = load_lottieurl(animation_url) | |
| # if lottie_animation: | |
| # st_lottie(lottie_animation, height=200, key="animation") | |
| # # About the App Section | |
| # st.subheader("About This Application") | |
| # st.markdown(""" | |
| # This platform serves as a **comprehensive guide to Machine Learning and Data Science**. | |
| # From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle: | |
| # - **Problem Definition**: Understand the business context and set clear objectives. | |
| # - **Data Handling**: Collect, clean, and explore datasets to uncover insights. | |
| # - **Model Development**: Build and optimize machine learning models. | |
| # - **Model Deployment**: Deliver real-world solutions and monitor performance. | |
| # Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples. | |
| # """) | |
| # # Key Takeaways Section | |
| # st.subheader("What You'll Learn Here") | |
| # st.markdown(""" | |
| # 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges. | |
| # 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning. | |
| # 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts. | |
| # 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls. | |
| # """) | |
| # # Author Section (Always visible) | |
| # st.markdown(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning. | |
| # My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions. | |
| # Currently, I am learning and preparing to embark on my professional career in this exciting field. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, unsafe_allow_html=True) | |
| # ----------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| # import streamlit as st | |
| # from streamlit_lottie import st_lottie | |
| # import requests | |
| # # Function to load Lottie animation from a URL | |
| # def load_lottieurl(url: str): | |
| # """Fetch Lottie animation JSON from a URL.""" | |
| # try: | |
| # response = requests.get(url) | |
| # if response.status_code == 200: | |
| # return response.json() | |
| # except requests.exceptions.RequestException: | |
| # return None | |
| # # CSS Styling for light and dark modes with adaptive theme support | |
| # st.markdown(""" | |
| # <style> | |
| # :root { | |
| # --background-color: #f8f9fa; | |
| # --text-color: #212529; | |
| # --primary-color: #007acc; | |
| # --secondary-color: #005b96; | |
| # --card-bg: #ffffff; | |
| # --text-muted: #6c757d; | |
| # } | |
| # @media (prefers-color-scheme: dark) { | |
| # :root { | |
| # --background-color: #0e1117; | |
| # --text-color: #e5e5e5; | |
| # --primary-color: #29b6f6; | |
| # --secondary-color: #90caf9; | |
| # --card-bg: #1f2937; | |
| # --text-muted: #a3a3a3; | |
| # } | |
| # } | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color) !important; | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color) !important; | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color) !important; | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg) !important; | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color) !important; | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted) !important; | |
| # } | |
| # /* Sidebar adjustments */ | |
| # .css-1d391kg, .css-1d391kg * { | |
| # background-color: var(--card-bg) !important; | |
| # color: var(--text-color) !important; | |
| # } | |
| # </style> | |
| # """, unsafe_allow_html=True) | |
| # # Sidebar Navigation | |
| # chapters = [ | |
| # "Foundation", | |
| # "ML Project Lifecycle", | |
| # "Core Algorithms", | |
| # "Model Evaluation", | |
| # "Data Handling", | |
| # "Computer Vision Basics", | |
| # "Natural Language Processing (NLP)", | |
| # "Deployment & Tools" | |
| # ] | |
| # selected_chapter = st.sidebar.radio("Select Chapter", chapters) | |
| # # Nested page selection based on chapter | |
| # if selected_chapter == "Foundation": | |
| # foundation_main = st.sidebar.radio("Select Page", [ | |
| # "Home", | |
| # "Introduction to Data Science", | |
| # "Machine Learning vs Deep Learning" | |
| # ]) | |
| # if foundation_main == "Introduction to Data Science": | |
| # st.sidebar.radio("Explore More", [ | |
| # "Understanding Intelligence", | |
| # "AI Tools: ML, DL, and Gen‑AI", | |
| # "Real‑Life Analogies and Examples", | |
| # "What is Data Science?", | |
| # "The Role of a Data Scientist", | |
| # "Why AI and Data Science Matter", | |
| # "Did You Know?" | |
| # ]) | |
| # elif foundation_main == "Machine Learning vs Deep Learning": | |
| # st.sidebar.radio("Explore Comparison", [ | |
| # "Understanding Machine Learning and Deep Learning", | |
| # "Comparison Table for ML vs DL" | |
| # ]) | |
| # elif selected_chapter == "ML Project Lifecycle": | |
| # stage = st.sidebar.radio("Select Stage", ["Life Cycle of ML Project"]) | |
| # if stage: | |
| # st.sidebar.radio("Subtopic", [ | |
| # "Problem Statement", | |
| # "Data Collection", | |
| # "Data Preprocessing", | |
| # "Exploratory Data Analysis (EDA)", | |
| # "Feature Engineering", | |
| # "Model Selection", | |
| # "Model Training", | |
| # "Model Evaluation & Tuning", | |
| # "Model Deployment", | |
| # "Monitoring" | |
| # ]) | |
| # elif selected_chapter == "Core Algorithms": | |
| # st.sidebar.radio("Select Algorithm", [ | |
| # "Linear Regression", | |
| # "Logistic Regression", | |
| # "k‑Nearest Neighbors (kNN)", | |
| # "Decision Trees", | |
| # "Support Vector Machines (SVM)", | |
| # "Ensemble Techniques" | |
| # ]) | |
| # elif selected_chapter == "Model Evaluation": | |
| # metric_main = st.sidebar.radio("Select Topic", ["Performance Metrics"]) | |
| # if metric_main: | |
| # st.sidebar.radio("Choose Metric", [ | |
| # "Accuracy, Precision, Recall", | |
| # "Confusion Matrix", | |
| # "ROC‑AUC" | |
| # ]) | |
| # elif selected_chapter == "Data Handling": | |
| # data_main = st.sidebar.radio("Select Topic", [ | |
| # "Data Types Overview", | |
| # "Data Cleaning", | |
| # "Feature Engineering" | |
| # ]) | |
| # if data_main == "Data Types Overview": | |
| # st.sidebar.radio("Choose Type", [ | |
| # "Structured Data (SQL, Excel)", | |
| # "Semi‑Structured Data (JSON, XML)", | |
| # "Unstructured Data (Images, Text)" | |
| # ]) | |
| # elif selected_chapter == "Computer Vision Basics": | |
| # cv_topic = st.sidebar.radio("Select Topic", ["Image Processing", "OpenCV Basics"]) | |
| # if cv_topic == "Image Processing": | |
| # st.sidebar.radio("Subtopic", [ | |
| # "Color Spaces", | |
| # "Image Augmentation", | |
| # "Splitting/Merging Images" | |
| # ]) | |
| # elif selected_chapter == "Natural Language Processing (NLP)": | |
| # st.sidebar.radio("Select Topic", ["NLP Introduction", "Text Preprocessing"]) | |
| # elif selected_chapter == "Deployment & Tools": | |
| # st.sidebar.radio("Select Tool", [ | |
| # "Model Deployment", | |
| # "Working with Excel/CSV", | |
| # "SQL for Data Science" | |
| # ]) | |
| # # Title and Tagline | |
| # st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖") | |
| # st.markdown("## Your Gateway To Become Master In Data Science") | |
| # # Display Lottie animation | |
| # animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json" | |
| # lottie_animation = load_lottieurl(animation_url) | |
| # if lottie_animation: | |
| # st_lottie(lottie_animation, height=200, key="animation") | |
| # # About the App Section | |
| # st.subheader("About This Application") | |
| # st.markdown(""" | |
| # This platform serves as a **comprehensive guide to Machine Learning and Data Science**. | |
| # From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle: | |
| # - **Problem Definition**: Understand the business context and set clear objectives. | |
| # - **Data Handling**: Collect, clean, and explore datasets to uncover insights. | |
| # - **Model Development**: Build and optimize machine learning models. | |
| # - **Model Deployment**: Deliver real-world solutions and monitor performance. | |
| # Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples. | |
| # """) | |
| # # Key Takeaways Section | |
| # st.subheader("What You'll Learn Here") | |
| # st.markdown(""" | |
| # 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges. | |
| # 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning. | |
| # 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts. | |
| # 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls. | |
| # """) | |
| # # Author Section | |
| # st.markdown(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning. | |
| # My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions. | |
| # Currently, I am learning and preparing to embark on my professional career in this exciting field. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, unsafe_allow_html=True) | |
| # ------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| # import streamlit as st | |
| # from streamlit_lottie import st_lottie | |
| # import requests | |
| # # Function to load Lottie animation from a URL | |
| # def load_lottieurl(url: str): | |
| # """Fetch Lottie animation JSON from a URL.""" | |
| # try: | |
| # response = requests.get(url) | |
| # if response.status_code == 200: | |
| # return response.json() | |
| # except requests.exceptions.RequestException: | |
| # return None | |
| # # CSS Styling for light and dark modes | |
| # st.markdown(""" | |
| # <style> | |
| # body { | |
| # margin: 0; | |
| # padding: 0; | |
| # font-family: 'Roboto', sans-serif; | |
| # background-color: var(--background-color, #f8f9fa); | |
| # color: var(--text-color, #212529); | |
| # } | |
| # h1 { | |
| # font-size: 3rem; | |
| # color: var(--primary-color, #007acc); | |
| # text-align: center; | |
| # margin-bottom: 15px; | |
| # } | |
| # h2, h3 { | |
| # font-size: 1.5rem; | |
| # color: var(--secondary-color, #005b96); | |
| # text-align: center; | |
| # margin-top: 20px; | |
| # } | |
| # p { | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-color, #212529); | |
| # line-height: 1.6; | |
| # text-align: justify; | |
| # } | |
| # .about-author { | |
| # background-color: var(--card-bg, #ffffff); | |
| # border-radius: 10px; | |
| # padding: 25px; | |
| # box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| # margin: 20px auto; | |
| # max-width: 700px; | |
| # text-align: center; | |
| # color: var(--text-color, #212529); | |
| # } | |
| # .social-icons { | |
| # display: flex; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # margin-top: 15px; | |
| # } | |
| # .social-icons a img { | |
| # width: 40px; | |
| # height: 40px; | |
| # transition: transform 0.3s ease-in-out; | |
| # } | |
| # .social-icons a img:hover { | |
| # transform: scale(1.2); | |
| # } | |
| # footer { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # font-family: 'Georgia', serif; | |
| # color: var(--text-muted, #6c757d); | |
| # } | |
| # </style> | |
| # """, unsafe_allow_html=True) | |
| # # Title and Tagline | |
| # st.title("Mastering Machine Learning: From Basics To Brilliance 🚀🤖") | |
| # st.markdown("## Your Gateway To Become Master In Data Science") | |
| # # Display Lottie animation | |
| # animation_url = "https://lottie.host/a45f4739-ef78-4193-b3f9-2ea435a190d5/PsTVRgXekn.json" | |
| # lottie_animation = load_lottieurl(animation_url) | |
| # if lottie_animation: | |
| # st_lottie(lottie_animation, height=200, key="animation") | |
| # # About the App Section | |
| # st.subheader("About This Application") | |
| # st.markdown(""" | |
| # This platform serves as a **comprehensive guide to Machine Learning and Data Science**. | |
| # From grasping the fundamentals to deploying models, it offers insights into the entire lifecycle: | |
| # - **Problem Definition**: Understand the business context and set clear objectives. | |
| # - **Data Handling**: Collect, clean, and explore datasets to uncover insights. | |
| # - **Model Development**: Build and optimize machine learning models. | |
| # - **Model Deployment**: Deliver real-world solutions and monitor performance. | |
| # Designed for both beginners and those looking to refine their skills, this app provides a structured learning path enriched with practical examples. | |
| # """) | |
| # # Key Takeaways Section | |
| # st.subheader("What You'll Learn Here") | |
| # st.markdown(""" | |
| # 1. **Step-by-Step Roadmaps**: Detailed guidance to help you navigate through data science challenges. | |
| # 2. **Hands-on Projects**: Real-world examples and code snippets for applied learning. | |
| # 3. **Visualizations**: Clear, intuitive graphs and plots to simplify complex concepts. | |
| # 4. **Insights from Experience**: Lessons from my personal journey to help you avoid common pitfalls. | |
| # """) | |
| # # Author Section | |
| # st.markdown(""" | |
| # <div class="about-author"> | |
| # <h2>About the Author</h2> | |
| # <p> | |
| # Hello! I'm <strong>Yash Harish Gupta</strong>, an aspiring data scientist deeply passionate about machine learning. | |
| # My journey began with curiosity about how data drives decisions and has evolved into a mission to create impactful solutions. | |
| # Currently, I am learning and preparing to embark on my professional career in this exciting field. | |
| # </p> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Social Links Section | |
| # st.markdown(""" | |
| # <div class="social-icons"> | |
| # <a href="https://www.linkedin.com/in/yash-harish-gupta-71b011189/" target="_blank"> | |
| # <img src="https://upload.wikimedia.org/wikipedia/commons/c/ca/LinkedIn_logo_initials.png" alt="LinkedIn"> | |
| # </a> | |
| # <a href="https://github.com/YashGupta018" target="_blank"> | |
| # <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub"> | |
| # </a> | |
| # </div> | |
| # """, unsafe_allow_html=True) | |
| # # Footer Section | |
| # st.markdown(""" | |
| # <footer> | |
| # <p>Made with ❤️ by <strong>Yash Harish Gupta</strong> | © 2024</p> | |
| # </footer> | |
| # """, unsafe_allow_html=True) |