Machine Learning Course

Part time | 3.5 Months

machine learning tools

The demand for engineers proficient in deploying machine learning models is on the rise. In this program, you will assess and implement updates to machine learning models in a production environment, including web applications, utilizing performance metrics. Are you prepared to elevate your career?

machine learning
Machine Learning

Course Overview

With an increasing number of companies venturing into machine learning products, there’s a rising demand for engineers capable of deploying machine learning models globally. The Clarusway Machine Learning Course is crafted to empower you with the advanced skills required to thrive as a machine learning engineer.

Throughout the training, you will learn to assess and update machine learning models in a production environment, including web applications, employing performance metrics. This program is designed to prepare you for a successful career in the dynamic field of machine learning.

135+ Hours Live In-Class

54+ Hours CMS

Hands-on Activities

The Machine Learning Course spans 13 weeks, encompassing over 135+ hours of in-class sessions, complemented by a bonus package of 54+ hours of Career Management Services (CMS). Our specialized CMS activities for the Machine Learning program feature sessions on life coaching, resume building, LinkedIn training, and interview preparation support.

Beyond the curriculum, you’ll solidify your learning through hands-on activities, including 10 projects and 3 Capstone Projects at the conclusion of the course. This comprehensive program ensures not only theoretical knowledge but also practical skills, preparing you thoroughly for a successful career in machine learning.

Why Machine Learning?


The flexibility to work remotely or in an office is available globally.


The average salary for this field is $110,000+ in the United States.


Machines are eager to learn more. Are you?

Upcoming Programs

Machine Learning

Schedule : Part time
Duration : 3.5 Months
Curriculum : Module 2 (Machine Learning & Deep Learning & NLP)

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

What Will You Learn?

project management

Machine Learning & Deep Learning & NLP

  • Machine Learning
  • Deep Learning
  • NLP
  • Model Deployment and Cloud for ML
  • Small and Medium Projects (for each section)
  • Capstone Projects

What Our Alumni Said?

David E.
David E.
Switch Up
Read More
“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...”
Switch Up
Read More
“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...”
Course Report
Read More
"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..."
Course Report
Read More
“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
Sean Snow
Career Karma
Read More
“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
Career Karma
Read More
“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
Read More
“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..."

Frequently Asked Questions

Upon completion of the program, students will acquire knowledge about machine learning algorithms and deployment methods, preparing them for roles in companies actively seeking machine learning engineers and specialists. Additionally, these skills can be applied in positions at companies looking for data scientists to implement machine learning techniques within their organizations.

The program covers fundamental concepts in supervised and unsupervised machine learning, guiding you through the process of creating your machine learning product. If you have an interest in deploying applications powered by machine learning, this program may align well with your needs!

Various payment options are available, including:

This program includes machine learning, Deep Learning, and NLP training. It is well-suited for intermediate-level trainees with an IT background. Successful participation requires motivation, commitment, discipline, and a strong work ethic. A positive mindset can help you excel and establish yourself as an IT expert in this domain!

The Machine Learning Program is designed to provide you with advanced skills for a career as a machine learning engineer. It includes the analysis and updating of machine learning models in real-world applications, such as web applications, using performance measurements.

The increasing adoption of machine learning solutions by businesses is leading to a growing demand for engineers who can deploy machine learning models globally. The advantages of pursuing a career in this field are substantial, making it a promising and continually expanding field.

To enroll in the Machine Learning (Module 2) program, you need to meet the prerequisites, which include knowledge of SQL, Linux (Shell Scripting), GIT, and Python.

Embracing a career in machine learning is advantageous, especially in the current business landscape driven by billions of data bytes. If you have an inclination towards data, automation, and algorithms, a career in machine learning can be highly rewarding. The job involves analyzing extensive data sets for application and automation. Machine learning careers are expected to remain in high demand, making it a promising field for the future.

Machine learning is a subset of artificial intelligence and computer science that involves utilizing data and algorithms to replicate the learning process of humans, progressively improving its accuracy.

Here are six instances where machine learning is actively applied:

  1. Image recognition
  2. Speech recognition
  3. Medical diagnosis
  4. Statistical arbitrage
  5. Predictive analytics
  6. Information extraction

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

Artificial Intelligence

Machine Learning

It is a field focused on creating machines that can imitate human behavior.

A subset of AI that enables machines to learn from data without explicit programming.

Aims to create a computer system as intelligent as humans for solving complex problems.

Aims to teach machines to learn from data and produce accurate output.

Creates intelligent systems for various tasks mimicking human capabilities.

Educates machines to perform specific tasks using data.

Includes machine learning and deep learning as primary subfields.

Encompasses deep learning as a significant subset.

Has a broad range of applications.

Has a limited set of applications.

Aims to create a system capable of a wide range of complex tasks.

Aims to develop machines specialized in tasks for which they are trained.

Focuses on maximizing the chances of success for the system.

Primarily concerned with precision and pattern recognition.

Certainly! Here are the five popular machine learning algorithms:

  1. Linear Regression
  2. Logistic Regression
  3. Decision Tree
  4. Naive Bayes
  5. k-Nearest Neighbors (kNN)

While understanding the core concepts of machine learning requires mathematics and some statistics, the practical application of machine learning techniques, such as solving problems or training models, necessitates coding proficiency.

The average annual salary for a Machine Learning Engineer is $128,210 in the United States.

AI and ML are complementary, with AI having a broader scope and machine learning focusing on achieving maximum accuracy to enhance artificial intelligence. They work together to produce high-quality outcomes.

Machine Learning, a subset of Artificial Intelligence, is widely utilized for predicting and categorizing data. It encompasses both supervised and unsupervised learning. In supervised learning, models are trained to predict or classify new data based on existing information. For instance, a model can predict a house’s roof dimensions given its length, height, and width. Additionally, machine learning is used for classification tasks, such as detecting a dog’s face in a photograph by training the model with numerous instances and counter-examples.

Certainly, machine learning and data science are interconnected, with machine learning being a subset of data science. However, data science encompasses a broader range of activities beyond just machine learning modeling. Clarusway offers separate modules for Data Science, which includes Data Analytics and Machine Learning, Deep Learning, and NLP, allowing learners to specialize in specific areas based on their preferences and goals.

Natural Language Processing (NLP) is a branch of artificial intelligence (AI) and, more specifically, falls under the umbrella of machine learning and deep learning. It focuses on enabling computers to comprehend, interpret, and interact with human language.

The types of machine learning include:

  1. supervised learning (task-driven)
  2. unsupervised learning (data-driven)
  3. reinforcement learning (learn from errors)
More Courses


data analytics

Data Analytics