Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

If you want to know:

• How can I predict car purchases using machine learning?

• What is logistic regression and how is it used in predictive modeling?

• How do I use Python and Scikit-learn for machine learning projects?

• What steps are involved in building a basic machine learning model?

• How can I visualize machine learning results effectively?

Then this lecture is for you!

In this hands-on machine learning lecture, you'll learn how to predict car purchases using Python and Scikit-learn. We'll walk through a real-world data science project, from data loading to model deployment. You'll discover how to use logistic regression for predictive modeling, visualize your data and results, and apply feature scaling. We'll cover essential machine learning concepts like supervised learning, training sets, and model evaluation. By the end of this lecture, you'll have practical experience in building a machine learning model that can optimize marketing efforts and improve ROI. This introduction to machine learning is perfect for beginners looking to start their journey in AI and data science.

If you want to know:

- How can I use Google Colab for machine learning projects?

- What are the advantages of Google Colab for beginners in data science?

- How do I import datasets and run machine learning models in Google Colab?

- Is Google Colab suitable for deep learning and neural networks?

- Can I use popular machine learning libraries like TensorFlow and XGBoost in Google Colab?

Then this lecture is for you!

This beginner's guide to Google Colab for machine learning introduces you to a powerful, user-friendly platform for data science projects. Learn how to access pre-installed machine learning libraries like TensorFlow, scikit-learn, and XGBoost without any setup hassles. Discover how to import datasets, create and modify notebooks, and run Python code for various machine learning algorithms. The lecture covers practical examples, including logistic regression, and demonstrates how to visualize results directly in the browser. By the end of this session, you'll be equipped to start implementing machine learning models, from basic regression to advanced deep learning, all within the convenient Google Colab environment.

In this video, Hadelin explains in details how to install R programming language and R studio on your computer so you can swiftly go through the rest of the course.

Understand the steps involved in Machine Learning: Data Pre-Processing (Import the data, Clean the data, Split into training & test sets, Feature Scaling), Modelling (Build the model, Train the model, Make predictions), and Evaluation (Calculate performance metrics, Make a verdict).

Understand why it's important to split the data into a training set and a test set, how they differ and what they are used for.

Two types of feature scaling: Normalization and Standardization. In the practical tutorials we focus on Standardisation and here we will discuss the intuition behind Normalisation.

A short written summary of what needs to know in Object-oriented programming, e.g. class, object, and method.

What is regression? 6 types of regression models are taught in this course.

The math behind Simple Linear Regression.

Finding the best fitting line with Ordinary Least Squares method to model the linear relationship between independent variable and dependent variable.

Data preprocessing for Simple Linear Regression in R.

Fitting Simple Linear Regression (SLR) model to the training set using R function ‘lm’.

Predicting the test set results with the SLR model using R function ‘predict’ .

Visualizing the training set results and test set results with R package ‘ggplot2’.

An application of Multiple Linear Regression: profit prediction for Startups.

The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.

The 5 assumptions associated with a linear regression model: linearity, homoscedasticity, multivariate normality, independence (no autocorrelation), and lack of multicollinearity - plus an additional check for outliers.

Coding categorical variables in regression with dummy variables.

Dummy variable trap and how to avoid it.

An intuitive guide to 5 Stepwise Regression methods of building multiple linear regression models: All-in, Backward Elimination, Forward Selection, Bidirectional Elimination, and Score Comparison.