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5 Days 5 Machine Learning Projects From Scratch - mitsumi - 11-20-2024 5 Days 5 Machine Learning Projects From Scratch Published 11/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 453.91 MB | Duration: 1h 20m Complete 5 Days 5 Machine Learning Projects From Scratch What you'll learn Understand the foundations of machine learning through hands-on projects. Learn the end-to-end workflow of a machine learning project, from data preparation to model deployment. Gain experience with popular machine learning libraries like scikit-learn, TensorFlow, and Pandas. Explore diverse domains such as regression, classification, NLP, computer vision, and recommendation systems. Requirements Basic Python & Machine Learning Required Description Welcome to the "5 Days 5 Machine Learning Projects From Scratch" course, a fast-paced, hands-on learning experience designed to take you from theory to practice in just five days! This course is perfect for aspiring data scientists, machine learning enthusiasts, and professionals who want to enhance their portfolio with real-world projects.Each day focuses on building a fully functional machine learning project from scratch, covering various domains and techniques to give you a well-rounded experience. By the end of this course, you'll have completed five diverse projects, gaining invaluable skills in data preprocessing, model building, evaluation, and deployment. This course emphasizes practical skills, critical thinking, and hands-on implementation to ensure you're job-ready.What You'll Builday 1: Create Project Using Natural Language Processing.Day 2: Create Project Using logistic regression and neural networks.Day 3: Create Project Using Naive Bayes classification.Day 4: Create Project Using Medical Image Prediction.Day 5: Create Project For Ad Click Through Prediction.Whether you're a student, professional, or self-taught learner, this course equips you with the knowledge and confidence to tackle real-world machine learning problems. Acquire skills to confidently implement machine learning solutions in real-world scenarios. Start your journey today and transform your passion for machine learning into tangible expertise! Overview Section 1: Introduction To Resume Analyzer Project For Machine Learning Job Lecture 1 Introduction To Project Lecture 2 Resume Class 1 : Import Packages Lecture 3 Resume Class 2 : Import Dataset Lecture 4 Resume Class 3 : Extract Text From Resume Lecture 5 Resume Class 4 : Tokenization Lecture 6 Resume Class 5 : Match Keywords & Skills Lecture 7 Resume Class 6 : Analyze Resume Score & Conclusion Section 2: Ecommerce Product Category Classification Using Machine Learning Lecture 8 Introduction To Project Lecture 9 Ecommerce Class 1 : Import Packages Lecture 10 Ecommerce Class 2 : Import Dataset Lecture 11 Ecommerce Class 3 : Data Preprocessing Lecture 12 Ecommerce Class 4 : Train Dataset Using Logistic Regression Lecture 13 Ecommerce Class 5 : Output & Conclusion Section 3: Emotion Detection From Text Using Natural Language Processing Lecture 14 Introduction To Project Lecture 15 Emotion Class 1 : Import Packages Lecture 16 Emotion Class 2 : Import Dataset Lecture 17 Emotion Class 3 : Dataset Preprocessing Lecture 18 Emotion Class 4 : Train Dataset Using Naive Bayes Lecture 19 Emotion Class 5 : Output & Conclusion Section 4: AD Click Prediction Project Using Machine Learning Lecture 20 Introduction To Project Lecture 21 AD Class 1 : Import Packages Lecture 22 AD Class 2 : Import Dataset Lecture 23 AD Class 3 : Dataset Preprocessing Lecture 24 AD Class 4 : Train Dataset Using Regression Lecture 25 AD Class 5 : Output & Conclusion Section 5: Diabetes Prediction Project Using Machine Learning Lecture 26 Introduction To Project Lecture 27 Diabetes Class 1 : Import Packages Lecture 28 Diabetes Class 2 : Import Dataset Lecture 29 Diabetes Class 3 : Data Preprocessing Lecture 30 Diabetes Class 4 : Train Dataset Lecture 31 Diabetes Class 5 : Output & Conclusion Students,Python & Machine Learning Coders Screenshots Say "Thank You" rapidgator.net: k2s.cc: |