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
TurboBit
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
