Machine Learning Regression Masterclass in Python

The age of artificial intelligence (AI) has arrived! The technology is advancing rapidly and is finding widespread application in the healthcare, defence, banking, gaming, transportation, and robotics industries.
Machine Learning is a subfield of Artificial Intelligence that allows machines to learn and improve at a given task over time. Machine Learning is a hot topic, with a steady increase in demand for experienced machine learning engineers and data scientists over the last five years. According to a report published by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4 billion to $8.8 billion by 2022, with the AI tech sector expected to create approximately 2.3 million jobs by 2020.
The goal of this course is to teach students key aspects of machine learning regression techniques in a practical, easy, and enjoyable manner. Regression is a useful machine learning technique that predicts a continuous (dependent) variable based on a number of other independent variables. Regression strategies are commonly used to forecast the stock market, analyse real estate trends, and create targeted marketing campaigns.

KEY FEATURE

Python was designed to be a high-level programming language, which means that you don’t need to understand the coding structure or memory management when you code in Python.

Python code is executed, it is immediately converted into an intermediate form known as bytecode, which makes it easier to execute and saves runtime in the long run.

Other programming languages’ code can be used in the Python source code. Python source code can also be used in other programming languages.

Python is an open-source programming language which has an online forum where thousands of coders meet on a daily basis to improve the language.

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Key Topics
    The course gives students hands-on experience training machine learning regression models with real-world datasets. This course teaches several techniques in a hands-on manner, including:
    • Simple Linear Regression
    • Multiple Linear Regression
    • Polynomial Regression
    • Logistic Regression
    • Decision trees regression
    • Ridge Regression
    • Lasso Regression
    • Artificial Neural Networks for Regression analysis
    • Regression Key performance indicators
    The course is designed for students who want to learn the fundamentals of machine learning regression models. A basic understanding of programming is recommended. However, because these topics will be thoroughly covered during the first few course lectures, the course has no prerequisites and is open to any student with basic programming knowledge. Students who take this course will learn how to use machine learning regression models and how to apply these skills to solve real-world problems.

    Important Concepts Taught By Our Experts

    1. Understand Python programming and Scikit learn as they relate to machine learning regression.
    2. Understand the theory underlying simple and multiple linear regression techniques.
    3. Predict product sales volume and vehicle fuel economy using simple linear regression techniques.
    4. Predict stock prices and university acceptance rates using multiple linear regression.
    5. Explain the fundamentals and underlying theory of polynomial regression.
    6. Use polynomial regression to forecast employee salaries and commodity prices.
    7. Learn about the theory behind logistic regression.
    8. Using customer features, use logistic regression to predict the likelihood that a customer will purchase a product on Amazon.
    9. Learn about the underlying theory and mathematics of Artificial Neural Networks.
    10. Learn how to train network weights and biases and how to choose the appropriate transfer functions.
    11. Back propagation and gradient descent methods are used to train Artificial Neural Networks (ANNs).
    12. To improve network performance, optimise ANN hyper parameters such as the number of hidden layers and neurons.
    13. Use ANNs to forecast house prices based on parameters such as size, number of rooms, and so on.
    14. KPI (Key Performance Indicators) such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared intuition, and F-Test are used to evaluate the performance of trained Machine Learning models.
    15. Learn about the theory and intuition behind the Lasso and Ridge regression techniques.
    16. Real-world, practical project examples

    Key Topics In Which We Can Provide Help With

    1. Anaconda and Jupyter Installation

    • Download and Set up Anaconda
    • What is Jupyter Notebook

    2. Simple Linear Regression

    • Intro to Simple Linear Regression
    • Simple Linear Regression Intuition
    • Least Squares
    • Data Visualization
    • Divide Data into Training and Testing
    • Train Model
    • Visualization

    3. Regression Key Performance Indicators

    • Regression Metric
    • Bias Variance Tradeoff

    4. Polynomial Regression

    • Polynomial Regression Intro
    • Polynomial Regression – Intuition
    • Poly Regression – Salary Load Data
    • Poly Regression – Visualize Data
    • Poly Regression – Linear Trainingtesting
    • Poly Regression – Poly Part
    • Poly Regression – Economies Linear
    • Poly Regression – Economies Poly

    5. Multiple Linear Regression

    • Multiple Linear Regression Intro
    • Load Data and Libraries
    • Data Visualization
    • Model Training and Evaluation
    • Model Results Evaluation
    • Data Visualization
    • Train the Model
    • Model Evaluation
    • Retraining Model

    6. Logistic Regression

    • Logistic Regression Intuition
    • Confusion Matrix
    • Model Testing Visualization

    7. Apply Artificial Neural Networks To Perform Regression Tasks

    • Artificial Neural Networks Intro
    • Load Dataset
    • Visualize Dataset
    • Scale the Data
    • Train the Model
    • Evaluate the Model
    • Multiple Linear Regression
    • Model Improvement with More Features

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    The course gives students hands-on experience training machine learning regression models with real-world datasets. This course teaches several techniques in a hands-on manner, including:

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