MACHINE LEARNING FUNDAMENTALS
Course Structure
The course is divided into four main parts:
Introduction
Data Science and Machine Learning
Preliminary Concepts
Machine Learning
In part one, we will learn the basics of data science and machine
learning. In part two, we will answer questions such as what data
science and machine learning are and when they can be applied.
Part three will focus on the preliminary concepts required to
understand data science and machine learning. In part four, we
will discuss the best practices in data science and machine
learning. By the end of the course, you will receive a gift that will
help you on your learning journey.
Data Science and Machine Learning
Data science is a multidisciplinary field combining different areas
such as computer science, mathematics, and statistics. It
requires domain expertise in each particular area. Data scientists
explore data, visualize it, and calculate important statistics from
it. They then develop a machine learning model to identify
patterns. Machine learning and deep learning are the subfields of
data science.
Data Science, Data Analytics, and Big Data
Big data refers to huge volumes of various types of data, which
are distinct from ordinary data based on their four v's: volume,
velocity, variety, and veracity. Data analytics is more about
extracting information from the data by calculating statistical
measures and visualizing the relationship between the different
variables. Data science is applied in many fields, such as
healthcare, finance, transport, social media, ecommerce, and
virtual assistant apps.
Applications of Data Science and Machine Learning
Machine learning and data science are currently being used in
many fields, including healthcare, finance, transport, social
media, ecommerce, and virtual assistant apps. In healthcare,
machine learning is used in disease diagnosis and drug
discovery. In transport, machine learning algorithms are used in
Course Structure
The course is divided into four main parts:
Introduction
Data Science and Machine Learning
Preliminary Concepts
Machine Learning
In part one, we will learn the basics of data science and machine
learning. In part two, we will answer questions such as what data
science and machine learning are and when they can be applied.
Part three will focus on the preliminary concepts required to
understand data science and machine learning. In part four, we
will discuss the best practices in data science and machine
learning. By the end of the course, you will receive a gift that will
help you on your learning journey.
Data Science and Machine Learning
Data science is a multidisciplinary field combining different areas
such as computer science, mathematics, and statistics. It
requires domain expertise in each particular area. Data scientists
explore data, visualize it, and calculate important statistics from
it. They then develop a machine learning model to identify
patterns. Machine learning and deep learning are the subfields of
data science.
Data Science, Data Analytics, and Big Data
Big data refers to huge volumes of various types of data, which
are distinct from ordinary data based on their four v's: volume,
velocity, variety, and veracity. Data analytics is more about
extracting information from the data by calculating statistical
measures and visualizing the relationship between the different
variables. Data science is applied in many fields, such as
healthcare, finance, transport, social media, ecommerce, and
virtual assistant apps.
Applications of Data Science and Machine Learning
Machine learning and data science are currently being used in
many fields, including healthcare, finance, transport, social
media, ecommerce, and virtual assistant apps. In healthcare,
machine learning is used in disease diagnosis and drug
discovery. In transport, machine learning algorithms are used in
