Learn Machine Learning in 3 Hours, published by packt
This is the code repository for Learn Machine Learning in 3 Hours [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Given the constantly increasing amounts of data they're faced with, programmers have to come up with better solutions to make machines smarter and reduce manual work. In this Machine Learning course, you'll use Python to craft better solutions and process them effectively.
We start by focusing on key ML algorithms and how they can be trained for classification and regression. We will also work with Supervised and Unsupervised learning to help to get to grips with both types of algorithm. We will use the highly popular Scikit-learn library throughout the course while performing various ML tasks. By the end of the course, you will be adept at using the concepts and algorithms involved in Machine Learning. This is a highly practical course and will equip you with sufficient hands-on training to help you implement ML skills right after finishing the course.
- How Machine Learning algorithms fit data.
- Using PCA (Principal Component Analysis) to explore and visualize data easily.
- Implementing Unsupervised K-Means clustering.
- Leveraging the power of Unsupervised K-Nearest-Neighbor clustering.
- Effective implementation of Supervised SVM (Support Vector Machine) fitting
- Getting hands-on with Supervised Random Forest Fitting
- Implementing Supervised Gradient Boosting for classification
- Hyperparameter fitting and performance-tuning algorithms.
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
• Prior working knowledge of the Python 3 language
• Some familiarity with statistics.
This course has the following software requirements:
This course has the following software requirements:
• An editor like Pycharm, Idle, emacs etc.
• Scikit-Learn (pip3 install scikit-learn)
• Numpy (pip3 install numpy)
• Matplotlib (pip3 install matplotlib)
This course has been tested on the following system configuration:
• OS: Ubuntu
• Processor: Dual Core 3.0 Ghz
• Memory: 4GB
• Hard Disk Space: 200MB
• Video Card: 256MB Video Memory