Master Machine Learning: Basic to Advanced

Master Machine Learning: Basic to Advanced - feature image

Code Warriors, Anup Mor, Gaurav Sharma, Mayank Bajaj

Last Updated on September 30, 2023 by GeeksGod

Course : Machine Learning From Basic to Advanced

Introduction to Machine Learning

Welcome to our comprehensive course on becoming a Machine Learning Engineer! In this course, we will guide you through the process of analyzing data, creating visualizations, and implementing powerful machine learning algorithms using Python.

If you’ve ever been intrigued by the field of Machine Learning, then you’re in the right place! Whether you’re a beginner with some programming experience or an experienced developer looking to transition into Machine Learning and Data Science, this course is designed to cater to your needs.

Why should you consider a career in Data Science? Well, for starters, Glassdoor has ranked Data Scientist as the number one job. Additionally, the average salary of a data scientist in the United States is over $120,000, according to Indeed. With the ability to solve intriguing problems and make a real impact, a career in Data Science is incredibly rewarding.

The Course Structure

This course has been developed by Code Warriors, a group of Machine Learning enthusiasts who are passionate about sharing their knowledge and helping others learn. Our goal is to simplify complex theories, algorithms, and coding libraries, making it easier for you to grasp these concepts.

Throughout this course, we will guide you step-by-step into the world of Machine Learning. Each tutorial will help you develop new skills and enhance your understanding of this challenging, yet lucrative, sub-field of Data Science.

Now, let’s take a closer look at the structure of this course:

Part 1 – Data Preprocessing

Before diving into machine learning algorithms, it’s crucial to preprocess and clean your data. In this section, we will teach you how to handle missing data, encode categorical data, and scale features.

Part 2 – Regression

In this section, we will explore regression techniques, which are used to predict continuous values. You will learn about simple linear regression, multiple linear regression, polynomial regression, support vector regression (SVR), decision tree regression, and random forest regression.

Part 3 – Classification

When it comes to classifying data into categories, classification algorithms come into play. In this section, we will cover logistic regression, K-nearest neighbors (K-NN), support vector machines (SVM), kernel SVM, naive Bayes, decision tree classification, and random forest classification.

Part 4 – Clustering

Clustering algorithms are designed to group similar data points together. In this section, we will delve into K-means clustering and hierarchical clustering.

As a bonus, this course also includes Python code templates that you can download and utilize for your own projects. These templates will save you time and provide a solid foundation to build upon as you advance in your Machine Learning journey.

Free Udemy Coupon and Machine Learning Tutorial

If you’re looking for a free Udemy coupon and a reliable machine learning tutorial, you’re in luck! This course offers a comprehensive learning experience while providing valuable insights and practical knowledge.

With emphasis on a free Udemy coupon and machine learning tutorial throughout the content, you can rest assured that you’ll make the most out of this course. We understand the importance of affordability and accessibility, and we strive to provide a top-notch learning experience for everyone.

Don’t miss out on this incredible opportunity to delve into the exciting world of Machine Learning. Enroll in this course today and embark on your path to becoming a skilled Machine Learning Engineer!

Udemy Coupon :


What you will learn :

1. Master Machine Learning on Python
2. Make accurate predictions
3. Make robust Machine Learning models
4. Use Machine Learning for personal purpose
5. Have a great intuition of many Machine Learning models
6. Know which Machine Learning model to choose for each type of problem
7. Use SciKit-Learn for Machine Learning Tasks
8. Make predictions using linear regression, polynomial regression, and multiple regression
9. Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.

100% off Coupon