A beginners guide to learn Machine Learning from scratch. Learn various algorithms and techniques using ML libraries.
What you’ll learn
- Learn how to use NumPy to do fast mathematical calculations
- Learn what is Machine Learning and Data Wrangling
- Learn how to use scikit-learn for data-preprocessing
- Learn different model selection and feature selections techniques
- Learn about cluster analysis and anomaly detection
- Learn about SVMs for classification, regression and outliers detection.
Requirements
- Basic knowledge of scripting and programming
- Basic knowledge of python programming
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 to Machine Learning & Data Wrangling
- Linear Models, Trees & Preprocessing
- Model Evaluation, Feature Selection & Pipelining
- Bayes, Nearest Neighbours & Clustering
- SVM, Anomalies, Imbalanced Classes, Ensemble Methods
For the code explained in each lecture, you can find a GitHub link in the resources section.
Who this course is for:
- Beginners who want to become a data scientist
- Software developers who want to learn machine learning from scratch
- Python developers who are interested to learn machine learning
- Professionals who want to start their career in Machine Leaning
FREE FOR LIMITED TIME
Not FREE Anymore?
Please share this
Facebook Group
