Top 5 Essential Prerequisites To Learn Machine Learning And Become An Expert

With the growing technological advancement, machines are getting smarter, and more intelligent. Machine Learning is used in every aspect or field in our daily life. Be it predicting the weather, indicating the cost of flight/train tickets, in healthcare, which movie are you going to see next, and the list is never-ending. It is the most demanded technology of this era. If you are a beginner, who is thinking to get started with machine learning, you should be aware of the prerequisites of Machine Learning.

1. Statistics
2. Linear algebra
3. Probability 
4. Calculus 
5. Programming Languages

1. Statistics:

Statistics is a science concerned with collecting, presenting, giving insight into data, and interpreting data. Data plays a huge part in the field of machine learning. However, data without meaning is useless. There are two kinds of statistics:

a. Descriptive Statistics

b. Inferential Statistics

Descriptive Statistics: Descriptive Statistics is a tabular, graphical, and numerical summarize of data, i.e., it outlines the dataset into something meaningful.

Inferential Statistics: Inferential Statistics helps us test hypotheses or conclude sample data instead of whole datasets.

In Statistics, you should be familiar with:

1. Mean
2. Mode
3. Median
4. Outliers
5. Histogram
6. Standard deviation

2. Linear Algebra:

Linear Algebra plays an integral part in the field of Machine Learning. It deals with vectors, matrices, and linear transformation. Furthermore, it is used to transform and perform operations on the dataset.

Concepts required in Linear Algebra are:

1. Matrix and Vectors
2. Diagonal matrix, Orthogonal matrix, Symmetric matrix
3. Principal Component Analysis
4. Singular Value Decomposition
5. Tensors and Tensor Ranks
6. Algorithm in codes
7. Notations

3. Probability:

Words like “probably” and “likely or unlikely” we use in our day-to-day conversation are the basic understanding of Probability. In theory, an event(E) is a set of events, P(E) represents the probability of event E. For example, the event might happen (1) or might not occur (0).  Probability is the foundation of machine learning. It helps us to predict an event to occur or predict the likelihood of incidents.

Concepts required in Probability are:

1. Joint Probability
2. Conditional Probability
3. Bayes’ Theorem
4. Continuous Random Variables
5. Independence
6. Notations

4. Calculus:

Calculus is an important field in mathematics and plays a vital role in machine learning. If we want to understand what is going inside the working algorithm, we should be familiar with calculus. The data set that we use in machine learning have multiple feature/attributes. It is used to build, train and test models.

Concepts required in Calculus are:

1. Integration and Differentiation are most
2. Derivatives
3. Gradient and Slope
4. Chain Rule (Neural Network)

5. Programming Languages:

A good foundation of programming language can help you boost the skills required for machine learning. For example, a problem statement can be solved mathematically, but we need knowledge of programming language to implement it in real-life projects. Various programming language is present which can be used to implement machine learning algorithm.

Programming languages are:

1. Python

2. R

3. C++

4. MATLAB

5. Java

1. Python:
Python is the most popular programming language for machine learning. It has easy syntax, pre-built functions, and packages are already present. We can access 235,000 packages and excellent community support.

We have:

1. NumPy for Mathematics
2. OpenCV for Computer Vision
3. Pandas for file operation
4. Matplotlib for representation of data
5. TensorFlow for Deep Learning
6. Scikit-Learn for Classification and Regression

2. R:
R is another programming language that is widely used as much as python. Many applications are implemented through R. It has excellent library support.

We have:

1. Kernlab for regression and classification
2. DataExplorer for data exploration
3. Rpart and SuperML for machine learning
4. Mlr3 for machine learning workflows.
5. Plotly for data visualization.

3. C++:
C++ is majorly used for large systems and building games. It is used primarily for making libraries that we use in our python programming.

We have:

1. TensorFlow and Microsoft Cognitive Toolkit (CNTK) for Deep Learning
2. OpenCV for Computer Vision
3. Shogun and mlpack for machine learning.

4. MATLAB:
MATLAB comes with predefined functions and in addition, it has a GUI interface which makes it a lot easier to interact and work. It supports machine learning operations and is used in applications and computer vision.

We have:

1. Optimized and reduced coded models using AutoML

2. Sensor analytics using automatic code generation

5. Java:
Infrastructure, software, applications, and many other working parts of the company’s tech is already built upon Java. Popular frameworks and tools like Fink, Hadoop, Hive, Spark, and Weka used for Big Data are written in Java. Java Virtual Machine is the best platform for machine learning and data science for identical writing code across multiple platforms. It also allows them to create custom tools faster and features a load of IDEs that help improve overall productivity levels.

We have:

1. Weka
2. Apache Mahout
3. Deeplearning4j
4. Mallet
5. Spark MLlib
6. MOA

Use Cases of Machine Learning:

1. Voice Assistant
2. Dynamic Pricing
3. Email Filtering
4. Fraud Detection
5. Automation
6. Recommendation Systems

Conclusion:

As a beginner,  these are the essential prerequisites for getting started with machine learning. One should also know how to work with data. In this blog, we have only mentioned the requirement, along with the most preferred programming language. Paths can differ, but the goal remains the same. Machine learning keeps on changing and updating, so we need to be updated with the current market requirements.

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A semi-manual internal process to assign categories to the ticket based on their severity could not keep up with the expanding business. Moreover, due to the sheer volume of incoming log messages, multiple high severity errors went unnoticed, resulting in several days of production downtime. Therefore, the objective of implementing the Azure solution is to correctly classify the tickets according to the severity along with Metadata edition, data preparation, and modelling/algorithm selection.

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Feel free to drop any questions in the comment box, I would love to address them. I hope you enjoyed the article. Best of luck!

WRITTEN BY Rahul Kumar Sharma