MACHINE LEARNING
•Machine learning is the state of the art approach to data science.
•The main advantage machine learning has over any of the traditional data science techniques
is the fact that at its core resides the algorithm. These are the directions a computer uses to find a model that fits the data as well as possible.
is the fact that at its core resides the algorithm. These are the directions a computer uses to find a model that fits the data as well as possible.
•The difference
between machine learning and traditional data science methods is that we do not give the computer instructions on how to find the sought dependence; it takes the algorithm and uses its directions to learn on its own how to find that model.
between machine learning and traditional data science methods is that we do not give the computer instructions on how to find the sought dependence; it takes the algorithm and uses its directions to learn on its own how to find that model.
•Unlike in traditional data science, human involvement is minimized. In fact, machine learning, especially deep learning algorithms are so complicated, that humans cannot genuinely understand what is happening “inside” the model.
MACHINE LEARNING THE ALGORITHM
•A machine learning algorithm is like a trial-and-error process, but the special thing about it is that each consecutive
trial is at least as good as the previous one.
trial is at least as good as the previous one.
• But bear in mind that in order to learn well, the machine has to go through hundreds of thousands of trial-and- errors, with the frequency of errors decreasing throughout.
•Once the training is complete, the machine will be able to apply the complex computational model it has learned to novel data still to the result of highly reliable predictions.
•There are three major types of machine learning:
-supervised
-unsupervised, and
-reinforcement learning.
SUPERVISED
LEARNING
•Supervised learning rests on using labeled data. Imagine having big data consisting of video files and images, labelled as “cats”, “dogs”, & “other”.
•The machine gets data that is associated with a correct answer; if the machine’s performance does not get that correct answer, an optimization algorithm adjusts the computational
process, and the computer does another trial.
process, and the computer does another trial.
•Typically, the machine does this on hundreds of data points at once.
•Support vector machines, neural networks, deep learning, random forest models, and Bayesian networks are all instances of supervised
learning.
learning.
UNSUPERVISED LEARNING
•When the data is too big, or the data scientist is pressured for resources to label the data, or they do not know what the labels are at all, data science resorts to using unsupervised learning.
• This consists of giving the machine unlabeled data and asking it to extract insights from it. This often results in the data being divided in a certain way according to its properties. In other words, it is clustered.
•Unsupervised learning is extremely effective for discovering patterns in data, especially things that humans using traditional analysis techniques would miss.
REINFORCEMENT LEARNING
•This
is a type of machine
learning where the
focus is on performance (to walk, to see, to read), instead of accuracy.
is a type of machine
learning where the
focus is on performance (to walk, to see, to read), instead of accuracy.
•Whenever the machine performs better than it has before, it receives a reward, but if it performs sub-optimally, the optimization algorithms does not adjust the computation.
•This
optimal behavior is learned through interactions with the environment and
observations of how it responds, similar to children exploring the world around
them and learning the actions that help them achieve a goal.
optimal behavior is learned through interactions with the environment and
observations of how it responds, similar to children exploring the world around
them and learning the actions that help them achieve a goal.
•Some applications are in robotics, autonomous driving, etc
Class Sessions
1- Introduction to Data Analytics
2- What is Data Science
3- Overview
4- Data preprocessing
5- Data Quality Assessment and Use Case
6- Where does Data come from?
7- Who handles th Data
8- BUSINESS INTELLIGENCE
9- Traditional Methods
10- Traditional Forecasting uses
11- Content
12- Machine Learning Uses
13- Introduction to Large Language Model and Generative AI
14- Data protection
15- Data Management practises
16- Tools and Frameworks