About This Course
As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars, spam detection, document searches, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and data science. The main challenge is how to transform data into actionable knowledge.
In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning-with-scikit-learn
- Certificate will provided in this course on Completion
- Full lifetime access
- Available on Mobile & Laptop
What Students Will Learn In Your Course?
- Acquaint yourself with important elements of Machine Learning
- Understand the feature selection and feature engineering process
- Assess performance and error trade-offs for Linear Regression
- Build a data model
- Understand how a data model works
- Understand strategies for hierarchical clustering
- Ensemble learning with decision trees
- Learn to tune the parameters of Support Vector machines
- Implement clusters to a dataset
Are There Any Course Requirements Or Prerequisites?
Familiarity with languages such as R and Python will be invaluable here.
Who Are Your Target Students?
This course is for IT professionals who want to enter the field of data science and are very new to Machine Learning.
- 34 lectures