Crash Course Introduction To Machine Learning




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



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