Machine learning (Machine Learning) is a branch ofartificial intelligence (HE) focused on building applications that learn from data and improve accuracy over time without being programmed to do so.
In machine learning, the algorithms are 'trained’ to find patterns and characteristics in massive amounts of data in order to make decisions and predictions based on new data.
The better the algorithm, more accurate decisions and predictions will be as you process more data.
Machine learning methods (also called machine learning styles) fall into three main categories. Then:
Supervised machine learning
Supervised machine learning trains itself on a tagged data set.
Unsupervised machine learning
Unsupervised machine learning ingests untagged data (Many, Many) and uses algorithms to extract significant characteristics needed to label, sort and classify data in real time, without human intervention.
Semi-supervised learning offers a happy medium between supervised and unsupervised learning.
Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain calledartificial neural networks.
Con el Deep Learning, a computer model learns to perform classification tasks directly from images, text or sound.
Deep Learning models can achieve cutting-edge accuracy that, sometimes, exceeds human performance.
Models are trained using a large set of tagged data and neural network architectures that contain many layers.
It is important to start working with these technologies, since they have become the future and the present of modern computing.
Machine Learning and Deep Learning in Python & R
Covers regression, decision trees, SVM, neural networks, CNN, time series prediction and more using Python and R.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in real world business problems, This course will give you a solid foundation by teaching you the most popular machine learning techniques.
Who is this course for?
- People pursuing a career in data science
- Working professionals starting their data journey
- Statisticians who need more practical experience
- Students will need to install Anaconda software, but we have a separate lesson to guide you to install the same.
What you will learn
- Learn to solve real-life problems using machine learning techniques.
- Machine learning models as linear regression, Logistic regression, KNN, etc.
- Advanced machine learning models as decision trees, XGBoost, Random Forest, SVM, etc.
- Understanding of the basic concepts of statistics and Machine Learning concepts.
- How to perform basic statistical operations and run ML models in Python
- Deep understanding of data collection and data pre-processing for the problem of machine learning
- How to turn a business problem into a machine learning problem.
This course is free thanks to a coupon that you can find below. Take into account that these types of coupons last for a very short time.
If the coupon has already expired you can purchase the course with a great discount.
The estimated coupon end date is for the day 29-30 of March, but it can beat at any time.
To obtain the course with your coupon, click on the following button: