4.14 (382 reviews)
☑ 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.
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.
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Support Vector Regression(SVR)
Decision Tree Regression
Random Forest Regression
Support Vector Machine (SVM)
Decision Tree Classification
Random Forest Classification
The instructor did not explain why we are preferring one option over the other for the steps performed
The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior.
It starts right away. Doesn't explain what we will do, which is the goal and why we should follow this approach. It just takes a script and runs one line at a time
The course lacks structure, quality is very low, audio is awful maybe the added resources can be useful but better explanations and proper introduction can be found for free on YouTube.
need more explanation of the mathematical model to help us understand why there is a data relationship
It really was a good match for me. Best course for a quick revision so far. Instructor explained every line and word of the program, which can be really helpful for beginners. All in all, great experience.
The way one of the trainer was speaking was a little bit hard to understand and adjust with. But good thing is I learnt new things and terminologies that have not even heard of before.
The instructor was kind of reading the code. It would have been better if he would have explained why the code was written in this order.
Explanation about code is good. concept explanation but why standardization used ?why not normalization needs to elaborate more
Not a very descriptive course. Okay for those who just want to know the code to create the models. No proper explanation/intuition provided for the theory behind the models.