Machine Learning- From Basics to Advanced (Udemy.com)
A beginners guide to learn Machine Learning (including Hands-on projects - From Basic to Advance Level)
Created by: www.edyoda.com EdYoda
Last updated December 2020
What you will learn
- Learn how to use NumPy, to do fast mathematical calculations in machine learning.
- Learn what is Machine Learning and Data Wrangling in machine learning.
- Learn how to use scikit-learn for data-preprocessing in machine learning.
- Learn different model selection and feature selections techniques in machine learning.
- Learn about cluster analysis and anomaly detection in machine learning.
- Learn about SVMs for classification, regression and outliers detection in machine learning.
How it compares
Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:
- Rating4.4 out of 5higher than 49% of them
- Reviews2,344#35 of 156 by number of reviews
- Length6.8 hoursshorter than 50% of them
- Last updatedDecember 2020earlier than 62% of them
Course Description
If you are looking to start your career in Machine learning then this is the course for you.
This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels.
This course has 5 parts as given below:
Introduction & Data Wrangling in machine learning
Linear Models, Trees & Preprocessing in machine learning
Model Evaluation, Feature Selection & Pipelining in machine learning
Bayes, Nearest Neighbors & Clustering in machine learning
SVM, Anomalies, Imbalanced Classes, Ensemble Methods in machine learning
For the code explained in each lecture, you can find a GitHub link in the resources section.
Who's teaching you in this course?
I am Professional Trainer and consultant for Languages C, C++, Python, Java, Scala, Big Data Technologies - PySpark, Spark using Scala Machine Learning & Deep Learning- sci-kit-learn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan - Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS - Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach towards ML.
Machine learning is the fuel we need to power robots, alongside AI. With Machine Learning, we can power programs that can be easily updated and modified to adapt to new environments and tasks to get things done quickly and efficiently.
Here are a few reasons for you to pursue a career in Machine Learning:
1) Machine learning is a skill of the future – Despite the exponential growth in Machine Learning, the field faces skill shortage. If you can meet the demands of large companies by gaining expertise in Machine Learning, you will have a secure career in a technology that is on the rise.
2) Work on real challenges – Businesses in this digital age face a lot of issues that Machine learning promises to solve. As a Machine Learning Engineer, you will work on real-life challenges and develop solutions that have a deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world struggles gives high satisfaction.
3) Learn and grow – Since Machine Learning is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer.
4) An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have the experience and expertise to follow an upward career graph and approach your ideal employers.
5) Build a lucrative career– The average salary of a Machine Learning engineer is one of the top reasons why Machine Learning seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by.
6) Side-step into data science – Machine learning skills help you expand avenues in your career. Machine Learning skills can endow you with two hats- the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge.
Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise.
Enroll Now!! See You in Class.
Happy learning
Team Edyoda
Instructor Details
- 4.4 Rating
2,344 Reviews
www.edyoda.com EdYoda
EdYoda is re-imagining skill based education, educating on job-relevant real world skills.
Edyoda courses are on job-relevant technical skills. We have professional team of instructors, some of the courses we specialize in are Web development, Mobile App Development, Cloud & DevOps, Machine Learning, Artificial Intelligence and Big Data.
We believe that access to education and opportunities is the biggest enabler, and we are on a mission to enable the same for everyone across the world.
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Reviews
By Aakanksha Chaudhary on 7/4/2024
This course covered all the basic concepts that essential for a beginner to know for becoming a data scientist . Some concepts were still missing. but still it was worth taking this course. Thank you!
By Victor Alfayo on 8/24/2022
Very educative and informative.Love to recommend it to all who want ML as a career Udemy is the place
By Aniket Mane on 4/9/2022
It was an amazing experience in learning this subject, the tutor was amazing and had good knowledge for the subject.
By Rohit Dasanoor on 9/6/2021
I felt that it was a good match for me. The problem was that some of the explanations were unclear, and some of the code could have been explained better. I did, however, learn new concepts.
By Pablo Rod on 9/5/2021
De momento, la calidad del sonido es bastante baja y, para mi gusto, necesitaría algo más de práctica (todo es teoría, lo entiendo pero tengo dificultad para ponerlo en practica).
By Avuyile Philasande Ndunge on 8/22/2021
Wow, this course was super helpful in getting me started on my machine learning journey. It took too long for me to finish because I wanted to apply these concepts to my everyday data at work as a data analyst. Lastly, this did kind of take me back to my computational fluid dynamics days. I hope the next person enjoys this course as much as I did or even more and improve on their skill set as well. Thank you!
By Ogheneochuko Haisley Okpako on 1/14/2021
Thus far, I have learnt that machine learning (ML) is divided into three sub-divisions namely, supervised, unsupervised and reinforced. Where the supervised is task driven, unsupervised is data driven while the reinforced is environmentally driven. Also, though raw data is applied as input to ML, this has to be converted to a vector array of numbers for ML utilization. I was introduced to Linear models, trees and preprocessing - all valuable techniques in ML.
By Rishabh Dutt on 9/28/2020
As the name suggests, it is worth going through small details, bigger picture concepts and some code, at the same time.
By Madhav Arora on 9/25/2020
Concepts so generously explained. Tutor is very informative and teaching method is very satisfying.
By Naman Shah on 7/31/2020
The content and explanation is good but the instructor needs to get a good Microphone for voice modification. The voice in Section 1 module 2 is up the mark and hope to have the similar mic settings.
Quality Score
No CourseDuck member has rated this course yet. Taken it? Give each part a thumbs up or down.
Overall Score : 88 / 100











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