Machine Learning Course

Part Time | 2 Months

machine learning tools
There is an increasing demand for engineers who can deploy machine learning models. You will evaluate and update machine learning models into a production environment such as a web application by using performance metrics. Are you ready to give your career a boost?
Machine learning
Machine Learning

Course Overview

A growing number of companies are developing machine learning products, increasing the demand for engineers who can deploy machine learning models to a global audience. Clarusway Machine Learning Course is designed to help you gain the advanced skills you need to become a machine learning engineer. 

In Machine Learning training, you will evaluate and update machine learning models into a production environment such as a web application by using performance metrics.

Online Lesson Activity

135+ Hours Live In-Class

Online CMS Activity

54+ Hours CMS

Hands on Activies

Hands-on Activities

Machine Learning Course is a 13-week program that includes more than 135+ hours of in-class sessions and a bonus package of 54+ hours of Career Management Services (CMS). Our specialty CMS activities for the Machine Learning program include sessions on life coaching, resume building, Linkedin training, and interview preparation support.

In addition to the curriculum, you will have the opportunity to practice what you’ve learned with hands-on activities + 10 projects + 3 Capstone Projects at the end of the course. 

Why Machine Learning?


Option to work remote or in office


Average salary $110,000+ in the US


Machines are eager to learn more. Are you?

Upcoming Programs

Machine Learning

Schedule : Part-time
Duration : 2 Months
Curriculum : Module 2 (Machine Learning & Deep Learning & NLP)

  • Python (with Data Analysis skills)
  • Linux
  • GIT

31 July 2023

What Will You Learn?

Module 2 Graphicon

Machine Learning & Deep Learning & NLP

  • Machine Learning
  • Deep Learning (ANN)

What Our Alumni Told ?

David E.
David E.
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“Having decided to attend Clarusway as a part of my personal upskilling and career-shifted initiatives, I was unsure as to what to expect. Although my experience exceeded my expectations, I strongly believe it was the right move at the right time. There is a group of smart, energetic, and talented people who run the program...”
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“I am 41 years old and I did not have any previous experience in IT. Today's conditions pushed me to make a career change, and in the meantime, I met Clarusway (a friend of mine recommended it highly). I was delighted to be a student of Clarusway from the moment I started the course until I finished it...”
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"You can reach the instructors and mentors whenever you want. There are live lessons for 15 hours a week, you can ask questions and get instant answers in live lessons. No question goes unanswered. they also train you from scratch even if you had no interest in It in the past. The one that encouraged me the most was the 1-month trial period..."
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“Clarusway is more than a bootcamp.. Because the menthorship mentality and the curriculums are up to date and giving insights about not about what the quality must be but also what the trends in IT sector going to...That was wonderful to have this experience ...Thank you Clarusway ..."
Sean Snow
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“As a graduate, I can surely say, it's the best bootcamp with all its instructors, cirriculum, online labs, mentors and job assistance. Thank you for ALL Clarusway!"
Yalcin Kose
Yalcin Kose
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“I highly recommend to take this course if you really want to be a AWS Cloud / DevOps Engineer. The course structure & curriculum are excellent. First you learn, then you practice with hands-on, next you do the project with the knowledge that you just got. When you attend, more than 20 projects waiting for you! ”
Betul Kaplan
Betul Kaplan
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“Clarusway has been a turning point for me. For a long time, I had wanted to switch to IT and Clarusway gave me the best opportunity for that. Their schedule adjusted so that you have a very natural learning on the road. Also the mentoring activities are very supportive and motivating..."

Weekly Schedule

Mon Tue Wed Thu Fri Sat Sun
07 – 10 P.M. 07 – 10 P.M.
07 – 10 P.M.
07 – 10 P.M.
07 – 10 P.M.
10 A.M. – 10 P.M.
Live Class
Live Class
Live Class
Live Class
  • The hours are indicated in the “Eastern Standard Time (EST)”.

Course Prices & Payment Options

Pay upfront and save $2,500 on tuition.



Total discounted fee is paid at the time of enrollment.
Meritize Credit (Student Loan)

as low as

$125 /mo

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Apply for Student Loan via Meritize. Fund your education with low monthly payments customized to your needs. Learn More

Clarusway Installment Plan (CIP)


save $1,500 on tuition

as low as

$300 /mo

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Split payments into small and fixed installments. CIP carries 0% interest.

Contingent Payment Plan (CPP)
30 months installment

$330 /mo

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Apply for CPP via LEIF. This plan carries 0% interest. Pay nothing while you’re enrolled. Start monthly payments when you get a job. Learn More

Income Share Agreement (ISA)

as low as

$250 /mo

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Apply for ISA via LEIF. This plan carries 0% interest. Pay nothing while you’re enrolled. Start monthly payments when you get a job. Learn More

Frequently Asked Questions

Students will learn about machine learning algorithms and deployment techniques in the Machine Learning Engineering program. They will be qualified to fill roles at companies seeking machine learning engineers and specialists. These skills can also be applied in positions at companies looking for data scientists to introduce machine learning techniques into their organizations.

This program introduces the basic concepts of supervised and unsupervised machine learning. It will teach you how to create your machine learning product from scratch. Are you interested in deploying an application powered by machine learning? If so, this program can be a good fit for you! 

We provide various payment options to make the program work for you: 

Our program offers machine learning training in addition to Deep Learning and NLP in the class. This program is the best fit for an intermediate-level trainee with a background in IT. A student in this program will need motivation, commitment, discipline, and a willingness to work hard. With the right mindset, you can distinguish yourself as an IT expert in this field! 

The Machine Learning Program is meant to equip you with the advanced skills necessary to pursue a career as a machine learning engineer. 
Using performance measurements, you will analyze and update machine learning models in a production context such as a web application during Machine Learning training.

As many businesses build machine learning solutions, the demand for engineers capable of deploying machine learning models to a worldwide audience grows. 
The benefits of participating in this profession are far too significant to ignore – it will be a growth field for many years to come.

Machine Learning (Module 2 (Machine Learning & Deep Learning & NLP)) program’s prerequisites are:
  • SQL
  • Linux (Shell Scripting)
  • GIT
  • Phyton

Billions of data bytes propel businesses to deploy machine learning to stay relevant in today’s business climate. If you have an interest in data, automation, and algorithms, machine learning can be an excellent career choice for you! Job duties include spending time evaluating massive amounts of data to apply and automate them. A machine learning career will remain in high-demand for the foreseeable future – it’s predicted to continue to be a high-growth field. 

Machine learning is a subfield of artificial intelligence (AI) and computer science. It entails using data and algorithms to mimic the way people learn, progressively increasing its reliability.

Machine learning applies to various sectors and businesses, and its application is expected to develop over time. These are six examples of machine learning in action.

  • Image recognition
  • Speech recognition
  • Medical diagnosis
  • Statistical arbitrage
  • Predictive analytics
  • Extraction

The following are the key distinctions between Artificial Intelligence (AI) and Machine Learning (ML): 

Artificial IntelligenceMachine learning
Artificial intelligence (AI) is a field of study that focuses on developing machines that are capable of imitating human behavior.System learning is a subtype of artificial intelligence that enables a machine to learn automatically from prior data without explicit programming.
The goal of AI is to create a computer system that is as intelligent as humans and capable of solving complicated issues.Machine learning’s objective is to enable machines to learn from data to provide accurate output.
In AI, we create intelligent computers capable of performing any work the same way humans do.We educate machines to complete a task and produce an accurate output using data in machine learning.
Machine learning and deep learning are the two primary subfields of artificial intelligence.The term “deep learning” refers to a significant subset of machine learning.
AI has a very broad application base.Machine learning has a finite application.
AI is an attempt to create an intelligent system capable of executing a wide range of complicated tasks.Machine learning aims to develop machines that can do just the tasks for which they have been educated.
The AI system’s primary goal is to maximize its odds of success.Machine learning is primarily concerned with precision and pattern recognition.


The following list is the five most frequently used machine learning algorithms.
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Naive Bayes
  • kNN

Only mathematics and a small number of statistics are required to grasp the fundamental concepts of machine learning. However, to utilize machine learning techniques to solve a problem or train a model, programming competence is required. 

The average salary of a Machine Learning Engineer is $128,210 per year in the United States.

AI is tasked with achieving the conditions necessary for a successful run. On the other side, machine learning aims for maximum accuracy to enable artificial intelligence. In other words, machine learning focuses on analyzing many parts of data to help AI make better decisions. 
AI has a broader scope than ML. AI is a goal-oriented discipline that includes a pre-installed intelligence system. However, we cannot deny that AI is meaningless without machine learning learnings. They absolutely complement one another to produce high-quality products.

Machine Learning is a branch of Artificial Intelligence that is frequently used to predict and classify data. There are two broad categories of learning: supervised and unsupervised. 

  • Unsupervised learning is included to find patterns in data to facilitate processing or cluster similar samples.
  • Supervised learning entails developing a model that can subsequently be used to predict or classify new data.

For instance, you can train a model by inputting the house’s length, height, and width and outputting the roof dimensions. And, if you provide enough data, the model can make predictions about the roof dimensions of a house whose roof dimensions are unknown given the house dimensions.

Additionally, you can use it to classify data. For instance, you can train a model to detect a dog’s face in a photograph by presenting it with hundreds of instances and counter-examples.

Data science and machine learning are like a room and a house. Machine learning is a subset of data science, but data science is not always machine learning. Machine learning modeling only makes up a portion of a data science career. 

The Clarusway Data Science course includes Module 1 (Data Analytics) and Module 2 (Machine Learning, Deep Learning, and NLP) programs, but the Machine Learning course is only Module 2.

NLP (natural language processing), like machine learning and deep learning, is a subfield of AI. It allows computers to understand, interpret, and manipulate human language. 

Tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection, and semantic relationship detection are some of the basic NLP tasks.

In general, NLP tasks break down language into smaller, more basic parts, try to figure out how the parts work together, and look into how the parts work together to make sense.

These fundamental tasks are usually applied in higher-level NLP capabilities such as:

  • Content categorization
  • Topic discovery and modeling
  • Corpus Analysis
  • Contextual extraction
  • Sentiment analysis
  • Speech-to-text and text-to-speech conversion
  • Document summarization
  • Machine translation
The machine learning types are:
  • Supervised learning (Task-Driven)
  • Unsupervised learning (Data-Driven)
  • Reinforcement learning (Learn from Errors)
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