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    21 Data Science Portfolio Projects In 21 Days

    Posted By: ELK1nG
    21 Data Science Portfolio Projects In 21 Days

    21 Data Science Portfolio Projects In 21 Days
    Published 3/2025
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 6.06 GB | Duration: 6h 42m

    Master Machine Learning & AI: From Time Series Analysis to Reinforcement Learning with Real-World Applications

    What you'll learn

    Build and implement various machine learning models for real-world business applications, from time series forecasting to natural language processing

    Master practical data science techniques including customer segmentation, sentiment analysis, and predictive modeling using industry-standard tools

    Develop end-to-end AI solutions for business problems such as fraud detection, product recommendations, and risk analysis

    Apply advanced analytics techniques to create actionable insights from complex datasets across multiple domains (finance, retail, healthcare, etc.)

    Requirements

    Basic understanding of Python programming language

    Familiarity with fundamental mathematical concepts (statistics, probability, and algebra)

    No prior machine learning or AI experience required

    A computer with internet access and ability to install required software packages

    Basic understanding of data structures and algorithms would be helpful but not mandatory

    Description

    This comprehensive data science course is structured as an intensive 21-day journey through the most relevant and in-demand areas of machine learning and artificial intelligence. Each day focuses on a complete project implementation, carefully designed to build both your technical skills and your professional portfolio.The curriculum progresses logically from foundational concepts to advanced applications:**Week 1 (Days 1-7):**- Begin with time series forecasting using ARIMA- Master customer analytics and segmentation- Develop credit risk models- Build social media sentiment analyzers- Create e-commerce recommendation systems- Design employee attrition predictors- Implement real estate pricing models**Week 2 (Days 8-14):**- Develop cybersecurity threat detection systems- Create fraud detection algorithms- Build energy consumption forecasting models- Design traffic flow prediction systems- Calculate customer lifetime value- Analyze stock market patterns- Implement NLP text classification**Week 3 (Days 15-21):**- Conduct market basket analysis- Create health risk prediction models- Build music genre classifiers- Forecast housing market trends- Develop automated trading systems- Master demand forecasting with Prophet- Build AI agents using reinforcement learningEach project utilizes industry-standard tools and frameworks including:- Python programming language- Popular libraries like Scikit-learn, TensorFlow, and PyTorch- Data manipulation tools like Pandas and NumPy- Visualization libraries including Matplotlib and Seaborn- Advanced ML frameworks such as Prophet and NLTKThe course includes:- On-demand video content- Downloadable source code for all projects- Real-world datasets for practical experience- Interactive coding exercises- Project-based assessments- Certificate of completionAll materials reflect the latest industry practices and technological advances in the field of data science and machine learning.

    Overview

    Section 1: Introduction

    Lecture 1 Day 1: Time Series Forecasting with ARIMA

    Lecture 2 Day 2 Customer Segmentation

    Lecture 3 Day 3: Credit Risk Analysis

    Lecture 4 Day 4: Sentiment Analysis on Social Media

    Lecture 5 Day 5: E-commerce Product Recommendations

    Lecture 6 Day 6: Predicting Employee Attrition

    Lecture 7 Day 7: Real Estate Price Prediction

    Lecture 8 Day 8: Cybersecurity Threat Detection Model

    Lecture 9 Day 9: Fraud Detection in Transactions

    Lecture 10 Day 10: Energy Consumption Forecasting

    Lecture 11 Day 11: Traffic Flow Prediction

    Lecture 12 Day 12: Customer Lifetime Value Prediction

    Lecture 13 Day 13: Time Series Analysis of Stock Prices

    Lecture 14 Day 14: Natural Language Processing for Text Classification

    Lecture 15 Day 15: Market Basket Analysis

    Lecture 16 Day 16: Health Risk Prediction

    Lecture 17 Day 17: Music Genre Classification

    Lecture 18 Day 18: Predicting Housing Market Trends

    Lecture 19 Day 19: Building a Trading Bot

    Lecture 20 Day 20: Demand Forecast using Prophet

    Lecture 21 Day 21: AI Agent using Reinforcement Learning

    Data analysts and business analysts looking to advance their career with AI/ML skills,Software developers wanting to transition into machine learning and AI,Business professionals seeking to understand and implement AI solutions in their organizations,Students and graduates interested in practical applications of AI in business contexts,nyone interested in learning how to solve real-world problems using machine learning, regardless of their background