2. The best subject of all the Artificial Intelligence domain is machine learning which has been in
the news for quite some time. This field has the potential to provide a better and necessary
opportunity. It is very easy to start a career even if you have zero in Mathematics or
Programming. Even if you have experience, there is no problem because it is the most
important element for your success, purely to help you learn those things. Data for which you
have your interest and inspiration.
If you are new then you do not know where to start studying and why you need machine
learning and why it is gaining the most popularity, then you have come to the right place to
get better knowledge from here. I have collected better information and useful resources to
help you complete all your projects.
Technologies and Tools covered
Python Programming language
Python packages/libraries
Anaconda Navigator
Machine Learning algorithms
https://uii.io/pyml
3. Python is a general-purpose, high level programming
language. It was mainly developed for an emphasis on code
readability. A great choice of
library is one of the main reasons python is the most popular
programming language used
for AI. Some of the libraries which we used for machine learning
are
4. NumPY: NumPY stands for Numerical Python. NumPy is used to
perform mathemat_x0002_ical operations on Arrays. Various other
libraries like Pandas, Matplotlib, and Scikit_x0002_learn are built
on top of this amazing library.
Pandas: Pandas is a python data analysis library. It is an open-
source python library
that provides high-performance, easy-to-use data structures and
data analysis tools.
It is one of the tools used for data cleaning and analysis.
5. Matplotlib: Matplotlib is one of the most popular and oldest
plotting libraries in Py_x0002_thon which is used in Machine
Learning. From histograms to scatterplots, matplotlib
lays down an array of colors, themes, palettes, and other options to
customize and
personalize our plots.
Seaborn: Seaborn is a library for making statistical graphics in
Python. It builds on
top of matplotlib and integrates closely with pandas data structures.
Seaborn helps
you explore and understand your data.
6. Types of Machine learning
Supervised Learning: Supervised learning uses a training set to
teach models to
yield the desired output. This training dataset includes inputs and
correct outputs,
which allow the model to learn over time.
Unsupervised Learning: unsupervised learning uses unlabeled data.
This is partic_x0002_ularly useful when subject matter experts are
unsure of common properties within a
data set.
7. Classification Model
i. Logistic Regression: Logistic Regression is a supervised
classification is unique
machine learning algorithm in python that finds its use in estimating
discrete values
like 0/1, yes/no and true/false.
ii. K-Nearest Neighbor: This is a supervised learning algorithm that
considers different
centroids and uses Euclidean function to compare distance. Then, it
analyses the re_x0002_sults and classifies each point to group to
optimize it to place with all closest points
to it.
8. iii. Decision Tree: A decision tree falls under supervised machine
learning algorithm in
python that comes of use for both classification and regression,
although mostly for
classification.
iv. Random Forest: Random Forest is a popular machine learning
algorithm that be_x0002_longs to the supervised learning technique. It
can be used for both Classification and
Regression problems in ML. I
