Projects in Machine Learning : Beginner To Professional

A complete guide to master machine learning concepts and create real world ML solutions

4.55 (600 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Learn core concepts of Machine Learning

Learn about differnt types of machine learning algorithms

Build real world projects using Supervised and Unsupervised learning algorithms

Learn to implement neural networks

Why take this course?

Update: This course has been updated to include 8 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future!

If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter.

Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them.

So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand.

This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer.

The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects.

The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples!

9 Projects That Are Included in This Course:

Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project.
Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud.
Project 3 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.

Project 4– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network.

Project 5 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality.
Project 6 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language
Processing by solving a text classification task using multiple classification algorithms.
Project 7 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised
learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset.
Project 8 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress
our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering.

All of this and so much more is included in this course. So, what are you waiting for?

Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming.

Our review

📚 **Course Overview:** The online Machine Learning course has garnered a global rating of 4.55, with recent reviews reflecting a range of perspectives and experiences. The course is well-regarded for its practical hands-on projects and has been praised for its ability to consolidate and expand upon existing knowledge in the field of machine learning. It's suitable for both beginners and professionals looking to deepen their understanding or apply machine learning to real-world problems like trading. **Pros:** ✅ **Comprehensive Learning Experience:** The course covers a wide array of machine learning concepts and provides practical, hands-on projects that allow learners to apply what they've learned. ✅ **Real-World Application:** Many users have reported that the knowledge gained from this course is directly applicable to their careers, with particular mention of its utility in trading and other professional contexts. ✅ **Engaging Instruction:** The instructor is commended for explaining complex concepts clearly and engagingly, making the content accessible even to those without a strong educational background in related fields. ✅ **Diverse Projects:** Learners have appreciated the variety of projects offered, which cater to both beginners and more advanced individuals looking to challenge themselves further. ✅ **Support for Career Growth:** Users have expressed confidence that the skills acquired from this course will significantly aid their professional development in the field of machine learning and AI. **Cons:** ❌ **Confusing Theory:** Some users found the theoretical explanations provided to be dull or confusing, suggesting that the course could benefit from more engaging presentation of theory. ❌ **Lack of Beginner Support:** A few reviews indicate that the course is not well-suited for absolute beginners, as some fundamental concepts and code functionalities are not adequately explained. ❌ **Technical Issues:** There have been reports of outdated data sources, challenging installations (e.g., Keras), and problems with the course material, such as unavailable URLs or errors in the provided text. ❌ **Q&A Response Time:** Some users have expressed frustration with slow responses to questions posed on the course's Q&A platform, which affected their learning experience. ❌ **Audio Quality and Subtitles:** The audio quality of tutorials was criticized in one instance, and the absence of subtitles or poorly matching auto-generated subtitles were also points of contention. **User Feedback Summary:** The course is generally well-received for its practical approach to teaching machine learning, with a strong emphasis on real-world applications. However, users have highlighted areas for improvement, particularly in the presentation of theoretical content and the technical aspects of the course materials. Despite these issues, the course remains a top choice for many learners seeking to dive into or expand their expertise in machine learning. **Final Verdict:** This Machine Learning course is a valuable resource for those looking to gain practical skills and real-world experience in the field. While there are some areas that need improvement, the overall positive feedback from users underscores its effectiveness as a learning tool for both beginners and experienced learners alike.

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