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Crash Course Introduction To Machine Learning - OneDDL - 09-18-2024 Free Download Crash Course Introduction To Machine Learning Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 292.05 MB | Duration: 0h 40m Kickstart Your Machine Learning Journey: Hands-On Projects with Python Libraries What you'll learn Learn the key concepts of Machine Learning Get experienced with Jupyter Notebooks Learn how to use Python libraries, such as Scikit-learn, numpy, pandas, matplotlib Data handling & cleaning to be used in Machine Learning Introduced to common ML algorithms Learn to evaluate the performance of a model Have hands-on experience with ML algorithms Requirements Basic understanding of high school mathematics Some Python experience would be helpful Description Welcome to "Crash Course Introduction to Machine Learning"! This course is designed to provide you with a solid foundation in machine learning, leveraging the powerful Scikit-learn library in Python.What You'll Learn:The Basics of Machine Learning: Understand the key concepts and types of machine learning, including supervised, unsupervised, and reinforcement learning.Setting Up Your Environment: Get hands-on experience setting up Python, Jupyter Notebooks, and essential libraries like numpy, pandas, matplotlib, and Scikit-learn.Data Preprocessing: Learn how to load, clean, and preprocess data, handle missing values, and split data for training and testing.Building Machine Learning Models: Explore common algorithms such as Linear Regression, Decision Trees, and K-Nearest Neighbors. Train and evaluate models(Linear Regression), and understand performance metrics like accuracy, R^2 and scatter values in plots to measure the prediction.Model Deployment: Gain practical knowledge on saving your pre-trained model for others to use.This course is structured to provide you with both theoretical understanding and practical skills. Each section builds on the previous one, ensuring you develop a comprehensive understanding of machine learning concepts and techniques.Why This Course?Machine learning is transforming industries and driving innovation. This course equips you with the knowledge and skills to harness the power of machine learning, whether you're looking to advance your career, work on personal projects, or simply explore this exciting field.Prerequisites:Basic understanding of Python programming.No prior knowledge of machine learning is required.Enroll Today!Join me on this journey to become proficient in machine learning with Scikit-learn. By the end of this course, you'll have the confidence to build, evaluate, and deploy your machine learning models. Let's get started! Overview Section 1: Introduction Lecture 1 Introduction Section 2: Basics of Machine Learning Lecture 2 AI vs Machine Learning vs Deep Learning Lecture 3 Types of Machine Learning Lecture 4 Key Terminology Section 3: Setting up the environment Lecture 5 Installing Anaconda Distribution Lecture 6 The importance of Jupyter Notebooks Section 4: Data Preprocessing Lecture 7 Data Loading & Cleaning Lecture 8 Data Splitting Section 5: Building a simple ML model Lecture 9 Introduction to ML models & using one Lecture 10 Common ML models Lecture 11 Evaluating accuracy Section 6: Saving the trained model Lecture 12 Saving the model using Pickle Lecture 13 Publishing the ML model Section 7: Conclusion and Next Steps Lecture 14 Recap of What You've Learned Lecture 15 Resources Section 8:[Extra] Improving a model's performance Lecture 16 5 common methods to improve a model's performance Anyone eager enough to learn how machine learning works and to break down the magic to reality Homepage Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live No Password - Links are Interchangeable |