Machine Learning From Basic to Advanced
What you'll learn
- Master Machine Learning on Python
- Make accurate predictions
- Make robust Machine Learning models
- Use Machine Learning for personal purpose
- Have a great intuition of many Machine Learning models
- Know which Machine Learning model to choose for each type of problem
- Use SciKit-Learn for Machine Learning Tasks
- Make predictions using linear regression, polynomial regression, and multiple regression
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, etc.
- Some basic python programming experience.
- Basic understanding of python libraries like numpy, pasdas and matplotlib.
- Some high school mathematics.
Are you ready to start your path to becoming a Machine Learning Engineer!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Machine Learning as well as Data Scientist!
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:
Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Who this course is for:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Hi, We are Code Warriors an E learning organisation . This is our Udemy Handle where we will provide you some awesome courses with very basic price. The courses will be very much informative and you will enjoy a lot. We focus on your learning in an enjoying manner so you don't get bored.
- 3.7 Instructor Rating
- 806 Reviews
- 115,728 Students
- 2 Courses
Hi, I'm Gaurav Sharma, I'm a Deep Learning and Crypto Enthusiast whose vision is to keep contributing to the community. I'm a confident speaker and always ready to spread knowledge and information to others. Interested in Graphic Designing, delivering designs with creativity and innovation since the beginning.
I also love organizing events such as workshops, Hackathons, and webinars and had founded Code Warriors for the same purpose.
Proficient: Python (SciKit-learn, NumPy, Matplotlib, Pandas), TensorFlow, Keras
Familiar : Computer Vision
I am an aspiring data scientist who enjoys connecting the dots: be it ideas from different disciplines, people from different teams, or applications from different industries. I have strong technical skills and an academic background in engineering, statistics, and machine learning.
Interested in finding valuable insights from the data, Passionate about implementing Data Science techniques and expand the domain of my knowledge base.
I also like organizing events such as workshops, Hackathons, and webinars and had founded Code Warriors for the same purpose.
My passion lies in solving business problems with tailored data and algorithms and communicating complex ideas to non-technical stakeholders. I am able to jump across verticals to deliver high-performing AI solutions.
An ambitious individual with a desire to succeed. A Machine Learning enthusiast with an additional knack in Web Development. A confident public speaker possessing intermediate leadership qualities. A Cricket fanatic. A student who like to take risks and does not shy away from experimenting various combinations in life. Striving to the best of the lot. Wish me good luck ??
Proficient: Python (scikit-learn, NumPy, nltk, pandas), TensorFlow, Keras
Familiar: NLP (Natural Language Processing)