Machine learning is a subset of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed. It builds mathematical models from sample data to make predictions or decisions. Machine learning algorithms are used widely in applications like email filtering and computer vision. It can help businesses unlock value from their data and make more competitive decisions. There are several types of machine learning including supervised, unsupervised, and reinforcement learning. Popular tools include Scikit-Learn, TensorFlow, and PyTorch. Languages used include Python, C++, and R. Machine learning has applications in manufacturing, retail, healthcare, travel, finance, and energy.

1. Machine Learning

2. What is Machine Learning??
Machine learning is an application of artificial intelligence (AI) that
provides systems the ability to automatically learn and improve from
experience without being explicitly programmed. Machine
learning focuses on the development of computer programs that can
access data and use it learn for themselves. It is seen as a subset
of artificial intelligence. Machine learning algorithms build
a mathematical model based on sample data, known as "training data",
in order to make predictions or decisions without being explicitly
programmed to do so. Machine learning algorithms are used in a wide
variety of applications, such as email filtering and computer vision,
where it is difficult or infeasible to develop conventional algorithms to
perform the needed tasks.

3. Importance of Machine Learning
Data is very important for all the businesses. Data-driven decisions
increasingly make the difference between keeping up with competition
or falling further behind. Machine learning can be the key to unlock the
value of corporate and customer data and enacting decisions that keep
a company ahead of the competition. The machine learning field is
continuously evolving. And along with evolution comes a rise in
demand and importance.

4. Types of Machine Learning
• Supervised ML- Past data is used to make predictions in supervised
machine learning.
• Unsupervised ML- Unsupervised machine learning finds hidden
patterns.
• Reinforcement ML- Reinforcement machine learning is used for
improving or increasing efficiency.

5. Popular Machine Learning Tools
• Knime
• Accord.net
• Scikit-Learn
• TensorFlow
• Weka
• Pytorch
• RapidMiner,
• Google Cloud AutoML
• Jupyter Notebook
• Apache Mahout etc.

6. Popular Machine Learning Software
• Python: a popular language with high-quality machine learning and
data analysis libraries
• C++: a middle-level language used for parallel computing on CUDA
• R: a language for statistical computing and graphics

7. Applications of Machine Learning
• Manufacturing- Predictive maintenance and condition monitoring
• Retail- Upselling and cross-channel marketing
• Healthcare and life sciences- Disease identification and risk
satisfaction
• Travel and hospitality- Dynamic pricing
• Financial services- Risk analytics and regulation
• Energy- Energy demand and supply optimization

8. About Henry Harvin
Henry Harvin® is a leading career and competency development
organization with focus on value creation. We are into the business of
training, skill development, assessment centres, content services and
higher education. Our dream is to establish 'Henry Harvin®' in line with
the vision of Mr.Henry Dunster 400 years ago which now resonates in
the form of a prestigious educational institution respected worldwide.

9. If you are interested to know more about Machine Learning, join the
Certified Machine Learning Practitioner Course by Henry Harvin®.