Fraud detection:
•With machine learning and supervised learning in particular, banks can take past data, label the transactions as legitimate, or fraudulent, and train models to detect fraudulent activity.
•When these models detect even the slightest probability of theft, they flag the transactions, and prevent the fraud in real time.
Client retention:
•With machine learning algorithms, corporate organizations can know which customers may purchase goods from them.
•This means the store can offer discounts and a ‘personal touch’ in an efficient way, minimizing marketing costs and maximizing profits. A couple of prominent names come to mind: Google, and Amazon.
WHO USES
MACHINE
LEARNING
MACHINE
LEARNING
•As we mentioned already, the data scientist is deeply involved in designing machine learning algorithms, but there is another star on this stage.
•The machine learning engineer. This is the specialist who is looking for ways to apply state of the
art computational models developed in the field of machine learning into solving complex problems such as business tasks, data science tasks, computer vision, self-driving cars,
robotics, and so on.
art computational models developed in the field of machine learning into solving complex problems such as business tasks, data science tasks, computer vision, self-driving cars,
robotics, and so on.
Artificial Intelligence
Andrew Ng,
co-founder of Google Brain and
former Chief Scientist at Baidu, defines artificial intelligence as
“a
huge set of tools for making computers behave intelligently.” This definition casts a
wide net and
it’s worth providing some examples to make clear what “behaving intelligently” means:
•Voice assistants, such as
Siri
Siri
•Recommendation systems, such as
Netflix
Netflix
•Self-driving cars
•Drones that fly over fields and
capture footage to optimize crop yield
capture footage to optimize crop yield
•Google Search
•Surfacing algorithms, such as
those employed by Twitter and
Facebook, that decide what content to show you in your feed
those employed by Twitter and
Facebook, that decide what content to show you in your feed
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