Are you also confused about where to start and which path to follow to become a machine learning engineer? Do not worry! We are here to answer these questions and will discuss the roadmap for learning Machine Learning.

*Didn’t understand the definition?*

* Well, no worries*. Let us explain this to you through an example.

Let’s say you want to buy a new laptop. You go to Amazon and search for laptops. Next time, when you visit again, Amazon will try to show you products frequently bought by people along with laptops like a Laptop Bag, Microphone, etc. So now, the Amazon recommendation system has understood your preferences and shows you products that you may be willing to buy. Just imagine, if the company can understand people’s preferences, they may show the same products along with fascinating discounts which can hugely drive the company’s profits. It is actually Machine Learning only which is driving this kind of system where the machine has understood directly from people’s preferences without being explicitly programmed.

It is used in many other applications like Image Recognition, Fraud Detection, Malware Analysis, Speech Recognition, etc.

Do you also want to get started in this rapidly growing field? No worries, we’ll be discussing the roadmap for learning Machine Learning, and then you can also build some truly amazing products and applications.

“Machine Intelligence is the last invention that humanity will ever need to make”

Nick Bostrom

## Table of content

- Get Started with Machine Learning
- Mathematics
- Probability and Statistics
- Select a Programming Language
- Database Management
- Machine Learning
- Deployment

- What Next
- Conclusion

## Get Started with Machine Learning

### 1. Mathematics

**You might be wondering why Mathematics is Required for Machine Learning? **

Well, Machine Learning is not just about coding in your preferred programming language. Researchers have very diligently created many machine learning algorithms using mathematical techniques. A good understanding of mathematics helps in understanding Machine Learning Algorithms well.

When you understand mathematics, you can select the right algorithm considering aspects like accuracy, model complexity, training time.

There are certain specific topics you should be well versed about. Those topics include:

**Calculu**s**Linear Algebra****Matrices****Vectors****Principal Component Analysis**

### 2. Probability and Statistics

Ok, let me give you a choice. You have a dataset, will you directly create the ML model? If your answer is yes, you got it wrong. You need to understand the dataset you are working on in the first place. Just dive deep into the data and explore all the hidden information from the vast amount of data.

You should first treat your data well and make changes like dealing with missing values, detecting outliers.

Why do we need this process? You will only get the garbage if you dump the garbage. That’s why first clean your data and then feed it to the Machine Learning model.

#### Importance of Probability and Statistics for Data Science

Probability and statistics are integral parts when it comes to Data Analysis. Before making machine learning models, you should first understand your data and statistics is imperative for Data Science. You should know what distribution your dataset follows and what would be an ideal way of replacing missing values for instance. The topics you should particularly understand are:

- Random Variables
- Measures of Central Tendency
- Standard Deviation and Variance
- Different types of distribution (Binomial, Bernoulli, Uniform, Gaussian, etc.)
- Hypothesis Testing
- Regression
- Correlation and its types
- Covariance
- Normalization and Standardization

I would recommend you to exactly know why these particular statistical topics are required for Data Science. You can learn them here.

### 3. Select a Programming Language

Now, it’s fine if you understand the mathematical and statistical techniques. But is it enough? No, now you need to get to the action and start coding since you need to code to interact with the ML Models and start getting results.

But which programming language you should move with for a Machine Learning task?

Most people prefer Python for Machine Learning though, but R is also a good choice especially for Data and Statistical Analysis.

In Python, you must know basic data structures like List, Tuple, Dictionary, Dataframe, etc. These data structures will help you to store your data efficiently.

Python has many libraries and modules built in for analysis, modeling, and visualization purposes.

#### Analysis Libraries

- NumPy
- Pandas

#### Visualization Libraries

- Pandas
- Matplotlib
- Seaborn
- Bokeh
- Plotly

#### Statistical Library

- statsmodel

### 4. Machine Learning

Now comes the moment you all are pretty excited about. The most critical goal of all this long process is to finally create models and get the predictions. Just imagine, you feed the data about your heart test parameters and you get the prediction whether you have heart disease or not. Isn’t that fascinating!

Regarding Machine Learning, you need to understand the difference between Supervised, Unsupervised, and Clustering and also the difference between classification and regression. Along with the implementation of algorithms, you must also understand the mathematics behind the algorithms.

#### Supervised Learning Algorithms

Linear Regression

Logistic Regression

Decision Trees

Random Forest

Naive Bayes

Support Vector Machine

XGBoost

#### Unsupervised Learning Algorithms

K-Means

K-NN

Hierarchical Clustering

DB-SCAN

Along with them, you should know feature selection and feature engineering techniques. For this, statistics knowledge will come in handy. Feature Selection helps you to select the best features for the model. It is not like you should feed all the features directly into your model. Only a few features that are correlated with the output variable are selected for the machine learning model.

### 5. Deployment

Deployment is one thing that you just can’t ignore in the ML Pipeline. It is crucial to deploy your application in real life so that people can use it. You can first integrate your ML Models with websites using Frameworks like Flask, Django.

Then you can deploy them on various cloud platforms like:

- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Heroku

## What Next?

Once you have understood the whole Machine Learning Pipeline, you should start creating some end-to-end projects along with deployment. Find any real-life use case and start making the project on the same.

For getting some freely available datasets, you can check Kaggle. It is an awesome resource for getting datasets and also participating in Data Science Competitions to hone your Data Science skills. Having at least 2-3 good projects in your resume gives you an edge in data science interviews.

Further, you can explore Deep Learning, NLP, and Computer Vision as well.

## Conclusion

In conclusion, Machine Learning is one of the trending technologies today in the market having abundant great opportunities worldwide.

This blog discusses the importance of mathematics and statistics in Machine Learning and also gave a few real-life examples to spark an interest and fascination in you about this amazing field.

We discussed the roadmap for learning Machine Learning.

We are organizing an “End-to-End Machine Learning Project” Bootcamp that will make you learn everything about building the pipeline of a Machine Learning Project. Check out the details below!

You can learn through our YouTube channel as well.

https://www.youtube.com/watch?v=ZNpmpl4WVVM&t=71s&ab_channel=LetTheDataConfess