Crash Course Introduction To Machine Learning

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