Top Prerequisites to learn Machine Learning

Machine Learning, the one the most demanding and trending course to learn and for making a career in the technical field. While you don't actually need to have prior skills in the field while doing machine learning courses, it eventually comes down to how well you can do and function with programming languages, mathematics, variables, linear equations, histograms, etc. It's important to know the prerequisites for Machine Learning if you're a beginner who's getting started with Machine Learning. This blog help you to get knowledge of prerequisites to learn machine learning:

Prerequisites for Machine Learning

Statistics 

Statistics provide methods that can be used to extract some results from the data. When we talk about statistics, there are two types i.e. One is descriptive statistics, and the another one is inferential statistics.There are descriptive statistics that are used to turn some valuable details into raw data. Inferential statistics may also be used instead of using a full dataset to get useful information from a subset of data.

Machine Learning expert should familiar with:

• Mean

• Median

• Standard deviations

• Outliers

• Histogram

Probability

Probability helps to estimate the probability of events, which allows one to reason why the case may or may not happen again. We can usually research the probability of their occurrence or the probability that they have those characteristics if they have not occurred yet. If they have taken place in the past already, we may use probabilities to show our measure of uncertainty in that situation. The theory of probability is the basis for building models of machine learning that include uncertainties. For Machine Learning we can deal with:

• Notation

• Probability distribution (joint and conditional)

• Different rules of probability such as Bayes theorem, sum rule, and product or chain rule

• Independence

• Continuous random variables

Programming languages

As Machine Learning algorithms are implemented with code, this is good news for you if you have a good foundation in programming. Although as an inexperienced programmer you could get away and concentrate on the mathematics front, it is advisable to pick up at least one programming language as it will really assist you to understand the internal mechanisms. You need to pick up a programming language, however, which will make it easy to implement algorithms for machine learning.

Here some popular programming languages to learn:

Python

In machine learning programming, Python is very popular. Python is one of the first programming languages to use a range of libraries and tools to support machine learning. Python leads all the other languages with more than 60% of machine learning developers using and prioritising it. Python has many great visualisation packages and helpful core libraries such as Numpy, Scipy, Pandas, Matplotlib, Seaborn, Sklearn that make it very easy for you to function and empower the machines to learn.

Numpy: Numpy, is a Python Linear Algebra Library with strong data structures for efficient multi-dimensional array and matrix computation.

Pandas: It is the most common Python library which provides data analysis with highly optimised performance.

Matplotlib: It is a popular library of python plotting used to create fundamental graphs such as line charts, bar charts, histograms, and many more.

Seaborn: Provides a high-level interface for attractive graph development.

Scikit Learn: It is used for data mining and data analysis to implement a broad variety of machine-learning algorithms, including support vector machines, random forests, gradient boosting, k-means, classification, regression and clustering algorithms.

R

Another of the AI and Machine Learning prerequisites used as often as Python is R programming. Nowadays, various machine learning frameworks are implemented by R.

• Regression and classification-based operations at Kernlab and Caret 

• DataExplorer for data exploration

• Rpart and SuperML for Machine Learning

• Mlr3 for workflows in Machine Learning 

• Plotly and ggplot for data visualization

 

C++

In the field of machine learning, the superfast C ++ programming language is also very popular. Most machine-learning systems support this powerful language. If you have some decent working knowledge using C++, then learning machine learning using C++ is a pretty good idea. Compared to most programming languages, C++ is much more powerful. In the C++ programming language, several powerful libraries such as TensorFlow and Torch are implemented, so machine learning and C++ are truly a great combination.

Java

This programming language is the "Jack of all business" and in the ML industry it still continues to dominate. Java offers many good algorithms such as Weka, Knime, RapidMiner, Elka, which use graphical user interfaces to perform machine learning tasks.

Also Read: How Machine Learning Help in Businesses

Conclusion

As above are among the essential prerequisites for machine learning, it is also crucial to know how to work with knowledge. It is also important that you know how to extract, process and analyse data, in addition to having basic programming skills. This is one of the most crucial skills that Machine Learning requires.

Apart from this if you want to start your career in Artificial Intelligence and want to do a course then join “Nearlearn”. Nearlearn is the Foremost Machine Learning Training Institute in Bangalore and also the best Artificial Intelligence Training Institute.They provide both online training and classroom training facilities. After completion, of course, they help you to get placement in various companies.

 

For more information contact us:

Visit: www.nearlearn.com