Overview What You Will Learn Curriculum Instructor

Course Overview

Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Linear algebra helps in creating better machine learning algorithms You can use your learning of linear algebra to build better supervised as well as unsupervised machine learning algorithms. Logistic regression, linear regression, decision trees, and support vector machines (SVM) are a few supervised learning algorithms that you can create from scratch with the help of linear algebra.

In this course, we are going to understand what machine learning is, the core of linear algebra, and how we can optimize our ML solution using different linear algebra techniques. Further in the course, we’ll learn about the regression models, the reason behind optimizing techniques, and the classification models. We’ll also go through the topics, imbalance classification, and the ADASYN technique. Finally, after learning about dimensionality reduction and its needs, we’ll work on a project.

After the course completion, you’ll master linear algebra and modeling, and eventually machine learning. You will know how you can boost your model performance with different boosting techniques in machine learning. You’ll also know how clustering helps different sectors in industries.

What You Will Learn

  • Get master with linear algebra & modeling.
  • You will learn how linear algebra works practically with machine learning modeling.
  • Building model is not the key but you will learn how you can boost your model performance with different boosting techniques in machine learning.
  • Instead of learning what is clustering we will understand how clustering helps different sectors in industries.

Program Curriculum

  • What is Machine Learning?
  • Types of Machine Learning
  • Kinds of Data Available
  • Introduction to Machine Learning Packages
  • $3.5 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Overview of Linear Models
  • Mathematics Behind Linear and Multiple Regression
  • Mathematics Behind Lasso and Ridge Regression
  • Hands-on Session with Linear Models
  • Chapter 2 Quiz

  • Reason Behind Optimizing Algorithms
  • Mathematics Behind Gradient Descent
  • Hands-on with Gradient Descent
  • Chapter 3 Quiz

  • Overview of Classification
  • Mathematics Behind Logistic Regression
  • Precision and Recall
  • Hands-on with Logistic Regression
  • Chapter 4 Quiz

  • Introduction of Imbalance Classification and ADASYN Technique
  • Introduction of SMOTE Techniques
  • Hands-on with SMOTE Techniques
  • SMOTE + Tomek Links
  • Chapter 5 Quiz

  • What Is Dimensionality Reduction and Its Need
  • Mathematics Behind Principal Component Analysis
  • Mathematics Behind Linear Discriminant Analysis
  • Hands-on with PCS and LDA
  • Chapter 6 Quiz

  • Understanding Problem Statement
  • Choosing the Right Model
  • How to Build Scalable Models
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Instructor

Vivek Chaudhary chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

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