Machine Learning in 30 mins: fast practice

Name: Machine Learning in 30 mins: fast practice
Rating: 3.9 (64 reviews)

This course is for absolute beginners in machine learning

Created byMahmood Abdulkareem

Last updated 9/2023

English

English [Auto],

What you'll learn

How to use machine learning to solve real-world problems
How to build and evaluate machine learning models in Python
Different types of machine learning algorithms
the steps you should learn to build any machine learning model

Course content

5 sections • 13 lectures • 32m total length

Machine Learning Crash Course For Absolute Beginners 20240:50
Learn by doing through a hands-on crash course for absolute beginners, guiding you to build, train, evaluate, and deploy machine learning models from scratch for real-world problems.
What we'll cover in this course0:45
Cover the basics of machine learning, including features, labels, algorithms, supervised learning, classification, and regression, and outline six steps to build any model, plus three real-life projects solving issues.

The Tools and Libraries we will use.1:02
Master the basics of machine learning by using Python, NumPy, pandas, and psychic learn, with Google Colab as an online coding environment.
Step-by-Step Guide to Building Models2:01
Collect data from Kaggle, clean and pre-process it, split into training and testing sets, build and train a model with the psychic learn library, and assess accuracy.

Diabetes Prediction Project0:28
Launch your first machine learning project to predict diabetes using a support vector machine, coding in Google Colab.
Import The Dependencies.2:31
Import numpy and pandas, load train_test_split, initialize an SVC classifier, and use accuracy_score to evaluate a classification model from data to prediction in Google Colab.
Data Collection & preprocessing2:57
download a diabetes dataset, upload to Colab, and load with pandas read_csv for initial inspection and preprocessing, exploring features like glucose, blood pressure, bmi, age, and outcome.
Data Splitting1:54
Split the data into training and testing sets with train_test_split, using 80% for training, 20% for testing, and set the random state to 42.
Create A Model & Train It2:48
Choose a model, specifically a linear-kernel support vector machine classifier from scikit-learn, train it on the training data, and use it to predict diabetes on new data.
Prediction and Evaluation2:04
Generate predictions from the trained classifier, then compute and print the accuracy score, revealing a 0.78 (78%) accuracy and noting potential improvements with more data.

Wine Quality Prediction Model1:07
Collect wine data from Kaggle, clean and pre-process it, then split into training and testing sets. Build and train a random forest model, make predictions, and evaluate accuracy.
Building the model8:17
Import and prepare data with numpy and pandas, load the wine quality dataset from Kaggle, split it, and train a random forest classifier to predict wine quality, achieving 0.925 accuracy.

Requirements

Basic knowledge of Python programming, Access to a computer And Internet

Description

This course is a comprehensive introduction to machine learning for beginners. You will learn the basics of machine learning, including supervised learning. You will also learn how to build and evaluate machine learning models in Python.

In the first section of the course, you will learn what machine learning is and how it works. You will also learn about the different types of machine learning algorithms and their applications.

In the second section of the course, you will learn about supervised learning. Supervised learning is a type of machine learning in which the algorithm is trained on a set of labeled data. The algorithm learns to predict the output for new data based on the patterns in the training data.

In the third section of the course, you will learn about unsupervised learning. Unsupervised learning is a type of machine learning in which the algorithm is trained on a set of unlabeled data. The algorithm learns to identify patterns in the data without being told what the patterns are.

In the fourth section of the course, you will learn about reinforcement learning.

In the fifth section of the course, you will learn how to build and evaluate machine learning models in Python. You will learn how to use popular Python libraries such as NumPy, pandas, and scikit-learn to build and evaluate different types of machine learning models.

By the end of this course, you will have a solid understanding of the basics of machine learning and you will be able to build and evaluate machine learning models in Python. You will also be able to apply machine learning to solve real-world problems.

these are the main things will be explained in this course

Machine Learning for Beginners
Learn Machine Learning Step-by-Step
Machine Learning for Real-World Applications
Introduction to Machine Learning
Supervised Learning Algorithms
Machine Learning for Medical Diagnosis
Machine Learning for Financial Trading
Machine Learning for the Future
3 Machine Learning Projects
Machine Learning Algorithms classification and regression
How Machine Learning Works

Who this course is for:

This course is for absolute beginners in machine learning. You will learn the basics of machine learning, including supervised learning. You will also learn how to build and evaluate machine learning models using Python.

Machine Learning in 30 mins: fast practice

What you'll learn

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Course content

Introduction2 lectures • 2min

The basics of machine learning2 lectures • 3min

Build Your First Machine Learning Model6 lectures • 13min

Build Your Second Machine Learning Model2 lectures • 9min

Third Project1 lecture • 6min

Requirements

Description

Who this course is for: