MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Difficulty: Bner | Genre: eLearning | Language: English | Duration: 7 Lectures (48m) | Size: 350 MB
In this course, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field.
Understand what Machine Learning is and what it offers
Understand the benefits of using the Machine Learning
Understand business use cases and scenarios that can benefit from using the Machine Learning
Understand the different Machine Learning training techniques
Understand the difference between Supervised and Unsupervised training
Understand the difference between Classification and Regression
Become familiar with several of the commonly used and popular Machine Learning algorithms discussed
Understand the basic principles behind Deep Learning and Deep Neural Networks
A background in statistics or probability
Familiarity and understanding of computer algorithms
Basic understanding of data analytics
The course proceeds with a formal definition of Machine Learning and continues on with explanations for the various machine learning and training techniques. We review both Supervised and Unsupervised learning, showcasing the main differences between each type of learning method. We review both Classification and Regression models, showcasing the main differences between each type of training model.
We provide a basic review of several of the most popular and commonly used machine learning algorithms including:
Linear Regression
Logistic Regression
K Nearest Neighbour (KNN)
K-Means
Decision Tree
Random Forest
Support Vector Machines (SVM)
Naïve Bayes
Finally, we'll provide a basic-level introduction to Deep Learning and Deep Neural Networks, as a more specialized form of Machine Learning.
Bners starting out to the field of Machine Learning
Anyone interested in understanding how Machine Learning works
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