Tagged by many as the technology with the highest demand in the modern era, Machine Learning (ML) is a field of study within the Artificial Intelligence (AI) domain that allows computers to learn from experience and improve on its own when exposed to new data, independent of human intervention or explicit programming. It uses an algorithm method to extract patterns out of raw data. In Machine learning, a computer is made to perform a task without explicitly programming it.

Basically, there are two kinds of machine learning tasks. They are: Supervised Learning and Unsupervised Learning.

In supervised learning, the system is presented with some example inputs, based on which the desired outputs are to be formed. Unsupervised learning is a machine learning technique in which the model is left unsupervised by the user, thereby allowing the model to discover previously undetected information and patterns on its own. Its most common method is cluster analysis.

In a nutshell, in supervised machine learning technique, the input data will always target variables, whereas, in unsupervised learning technique, no target variables, only the input data

Are you a newbie just setting out on the Machine learning journey? Here are the basic concepts you must be familiar with before starting.

- Linear algebra
- Statistics
- Probability
- Calculus
- Familiarity with major machine learning algorithms
- Coding Ability
- Programming languages (Preferably Python for Ease of Use)

Also, it’s important you be able to explain terms such as Data Science, AI, ML, Analytics in their simplest forms.

Here we take a look at each of them one after the other.

## Linear Algebra

Linear algebra is one of the unavoidable mathematical parts of machine learning. It’s a key to excellence in the field. A firm background in linear algebra is needed for deep learning of machine learning.

Simply put, Linear algebra is a continuous branch of mathematics with applications throughout engineering and science.

There are areas of study focus for machine learning in linear algebra, so, you needn’t study linear algebra from scratch or try to put the whole stuff in your head. You just have to pick topics that are in one way or the other used in machine learning. It is good to have comprehensive knowledge of the selected topics so that the underlying concepts behind machine learning can be well understood. Areas of focus in linear algebra for effective machine learning are **vectors, matrices, **including its basic operations like **inverse, multiplication and transpose, Tensor** **(a matrix with more than two dimensions**), etc. Linear algebra is needed to transform and perform operations on the dataset.

## Statistics

Statistics can best be described as a collection of tools that can be used to generate outcomes from the data. Descriptive statistics can be used to transform raw data into important information that is comprehensible and shareable, while inferential statistics are needed to get information from small data samples instead of using the whole dataset. Areas you will need statistics include, data understanding, data cleaning, data framing, data preparation, data selection, model evaluation, configuration, selection, presentation predictions, etc.

Sure, you need to know statistics to be a machine learning expert, but you don’t need to be an expert in statistics to do it. I put forward this good news for the sake of those who may feel like chickening out from machine learning because they struggle with statistics. Information gathered from experienced developers in the field shows that. In machine learning and data science, it’s a known fact that statistical concepts can never be ignored, but you need not be a statistician to be a machine learning expert. A good understanding of certain concepts, the theories behind the concepts, and the time they may be applied will place you in a very good position.

## Probability

Probability involves the measurement of uncertainty.

Probability helps in reasoning if a situation may happen or may not happen again. It’s a good tool in the prediction of the likelihood of occurrences. Algorithms such as Naive Bayes, Probabilistic Graphical Models, etc. are designed by utilizing techniques and tools from probability. Designing, training, tuning, and evaluation of models all involve probability.

Probability is a very important pillar that’s recommended by many as a prerequisite area to study before diving into machine learning. However, some still think it’s not compulsory to start with it and should be left for later as it only makes sense to practitioners that already have the context of the applied machine learning process in which to interpret it. To them, the timeline of learning probability is dependent on your location in the Machine learning journey at a particular point in time. Anyway, since the majority agreed that Probability is a foundation for Machine Learning, they sure carry the day.

## Calculus

This is another needed foundational knowledge to have before doing Machine learning. Its uses in there are numerous. Also called analysis, Calculus is the mathematics branch that studies the rate of the change of quantities, the length, volume, and area of an object. It is divided into integral and differential calculus. Integral calculus (as the name implies) integrates small pieces together to find out how much there is, but Differential calculus cuts something into small pieces to see how it changes.

Numerical optimization

It is used in training models given a dataset, which will be used in the performance of things like data generation, sequential decision-making, and inference.

Gradient computations

This is an optimization algorithm that is used to find the parameters of a function which that minimizes a cost function. It is best used when the parameters cannot be analytically calculated. In machine learning, optimization is a sizable part as almost all machine learning algorithm has optimization at its core. **Convexity** and **Convergence** are additional concepts to know in Gradient Descent.

## Familiarity with Major Machine Learning Algorithms

- Clustering: it’s for structuring or summarization of data. Some are mean-shift, hierarchical, K-means, etc.
- Classification: For the prediction of the class or category of a set of items. Some classification algorithms are; support vector machine, random forests, K-nearest Neighbors, boosted trees, Artificial neural networks (ANN), Logistic regression, Deep learning, etc.
- Regression: For the prediction of continuous values. Exponential regression, polynomial regression, linear regression, kernel regression, LASSO, etc. are all types of regression algorithms problem-solving methods.
- Anomaly detection: It’s for disturbing unusual cases and abnormal activities, e.g., fraud.
- Association: For associating co-occurring events or items. Apriori is an association algorithm.
- Recommendation systems: it is used for building recommendation engines.
- Sequence Pattern Mining: This is for the prediction of next data events between data examples in a sequence.
- Dimension Reduction: this refers to the reduction of a number of random variables. It is then divided into feature extraction and feature selection. Methods in solving problems relating to dimension reduction are; principal component analysis, non-negative matrix factorization, compressed sensing, independent component analysis, Manifold learning, etc.

## Coding Ability

To enter into the world of working with Machine Learning one of the next important things to learn is the ability to solve problems through writing code that can analyze data, apply Machine Learning algorithms to train a model on either labeled or unlabeled data, and apply that model to new data to predict a result.

The core concepts of coding you will need to learn are as follows:

- Variables
- Loops and Conditionals
- Writing Methods
- Object Oriented Programming for clean organized code
- Data Structures
- Algorithm principles like sorting and graph traversals
- Functional programming
- Parallel computing and concurrency

Link to resources to learn basic concepts can be found here.

## Programming Languages

Almost all programming languages available can be used to write machine learning-based applications. Java, JavaScript, C#, R, C++, Python, etc. even though the focus here is on Python, as it is acknowledged as the best for machine learning, we will still look at 3 of the other programming languages a little before turning to Python which is ideal for learning Machine Learning and Artificial Intelligence.

**Java**

Developed by James Gosling of Sun Microsystems before being acquired by Oracle, currently, Java is acknowledged as the most popular programming language in the world, boasting an average of 9 million developers according to statistics gathered from credible sources.

Easy debugging, ease of use, better user interaction, package services, graphical representation support, ease in application scaling, etc are some features that make Java an appealing language to the Machine learning engineer. With Java virtual machine, engineers can easily write the same code on multiple platforms.

**R**

R first came to the limelight in 1993 and has recently become one of the most popular programming languages among machine learning developers and data scientists. R is an array-based, functional, object-oriented, dynamic, and reflective programming language. It shares a lot of characteristics in common with Python.

Understanding of statistical principles is quite pivotal in the role of a machine learning engineer, as they can be applied to big data. When it comes to big loads of statistical data, R is the right programming language to go for. Anyone into machine learning should have R in their kit. R possesses various tools for drawing graphs and library management. It also has user-friendly IDEs such as RStudio. R provides large machine learning techniques such as model evaluation, data analysis, data sampling, data visualization, etc. It allows for tool mixing and doesn’t impose restrictions while tasks are performed in its language. Like Python, R is compatible across multiple platforms. It is open-source; hence, cost-effective for any machine learning project size. Packages in R include CARET, MICE, PARTY, randomFOREST, dplyr and tidyr, ggplot, etc.

**C++**

Recognized as one of the oldest and most popular programming languages in the world, C++ is used to drive many of the worlds recommendation and speech recognition engines used by big companies. Also most data science and machine learning libraries in Python were not directly written with Python. Almost all of them were written in C/C++ using the python wrapper for the libraries implementations to be used within the Python runtime.

So, behind the scene, the libraries are running on C/C++ runtime for speed purposes. This is because C++ compiles directly to machine code which a computer can run very quickly. Interpreted languages like Python and JavaScript however need another program to parse the instructions and constructs from their code which takes extra time for a computer to do and makes it less efficient for code that requires low latency for customers.

**Python**

Created by Guido Van Rossum in 1981, Python is one of the most popular programming languages of the present time. It’s a general-purpose, high-level open-source programming language that supports functional, procedural, object-oriented, and imperative paradigms. It shares similar characteristics with R to a large extent.

Due to its simple and quite understandable syntax, data handling capacity, open-source characteristic, and ease of interaction with third-party platforms and languages, Python is acknowledged by developers all over the world as the best programming language for the teaching and learning of machine learning. A programming language that best suits machine learning is one that comes with proven pre-built libraries and has gotten advanced support of data models and data science. Python offers this. It is a programming language that’s characterized by dynamic data types, classes, modules, and clear syntax. Python is among the first set of programming languages to get machine learning support via varieties of tools and libraries.

Why chose Python for machine learning ahead of the other programming languages available? Here we go

- Great data handling capacity, even though it may be slower than some other languages.
- Easy to learn, compared to other programming languages like Java, C#, and C++
- Open-source characteristics with a large active community of developers
- Built in data structures such as dictionaries and lists
- Ability to interact with almost all third-party platforms and programming languages
- Lots of highly evolved code libraries specifically made for learning data science and for machine learning applications are available with Python. Examples are:

- Scikit-learn: This library possesses most of the regression, classification, and clustering algorithms.
- Numpy: it enables effective and efficient computations. Numpy is a math library that is used to work with n-dimensional arrays in Python.
- Pandas: it’s a fast, flexible, easy to use library mainly for data manipulations and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
- Matplotlib, Seaborn, Plotly, etc.: Trendy plotting packages which provides 2D and 3D plotting
- Tensorflow and Pytorch: These are the two popular frameworks used for deep learning

After learning all of the above, you will be set for a smooth sail in the machine learning world. Let the trip begin and have fun too!