Machine Learning Basics - Machine Learning for Beginners

Overview

Dive into a comprehensive 57-minute tutorial on machine learning fundamentals, exploring an end-to-end case study using the Diabetes dataset. Learn data manipulation with pandas and NumPy, data visualization with Matplotlib and Seaborn, and perform exploratory data analysis. Build a machine learning model to predict diabetic patients based on various attributes using scikit-learn. Gain insights into the differences between traditional programming and machine learning, understand various types of machine learning algorithms, and explore concepts like logistic regression, data labeling, and train-test splits. Perfect for beginners looking to grasp essential machine learning concepts and practical implementation techniques.

Syllabus

Introduction
Agenda
Why do we need Machine Learning
What is Machine Learning
How Machine Learning Works
Steps in Machine Learning
Data Scientist
Data Cleaning
Data Selling
Data Analysis
Programming Languages
Machine Learning in Data Science
Python Community
Google Collab
Traditional Programming vs Machine Learning
Types of Machine Learning
Unsupervised Learning
Data Label
Outcome
Reinforcement Learning
Logistic Regression
Prediction
Data Sets
Deep Learning
Sigmoid
Google Cool Lab
Google Colab
Data Visualization
Data Set Shape
Count Plot
Correlation
Train Test Split
Train Logistic Regression
Outro