21 Data Science Portfolio Projects In 21 Days

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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

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