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Machine Learning

Machine Learning Engineer

Machine Learning Engineer

Embarking on a career as a Machine Learning Engineer is a transformative journey for those with a passion for data analysis and a desire to shape the future of technology. This career path offers a seamless progression for data analysts looking to harness the power of artificial intelligence. At Great Learning, we provide a comprehensive learning path with free courses tailored to aspiring Machine Learning Engineers, ensuring they acquire the essential skills and knowledge to excel in this dynamic field. Explore the world of machine learning engineer courses and chart your course towards a rewarding career in AI.

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Johns Hopkins University
Certificate Program in AI Business Strategy

10 weeks · Online

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Walsh College
MS in Artificial Intelligence & Machine Learning

2 Years · Online

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MIT Professional Education
No Code AI and Machine Learning: Building Data Science Solutions

12 Weeks · Online · Weekend

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McCombs School of Business at The University of Texas at Austin
PG Program in Artificial Intelligence and Machine Learning: Business Applications

7 months · Online · Weekend

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More About Machine Learning Engineer Path

Machine Learning (ML) engineering is a blend of computer science, data science, and software engineering. It involves creating systems that analyze data, make predictions, and automate tasks. Key responsibilities include developing and training ML models. Start your learning journey with Great Learning to become a successful ML Engineer.

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Begin your learning experience and become a machine learning engineer with certificate courses curated to land your dream job.

Skills Covered in this Path

NumPy Arrays
NumPy Operations
NumPy Math
Saving & Loading NumPy
Pandas Series
Pandas DataFrame
Pandas Functions (Mean
Median
Max
Min)
Data Manipulation
Supervised Learning
Unsupervised Learning
Machine Learning with Python
Marginal Probability
Bayes Theorem
Binomial Distribution
Normal Distribution
Poisson Distribution
Descriptive Statistics
Measures of Dispersion Range and IQR
Central Tendency and 3 Ms
The Empirical Rule and Chebyshev Rule
Correlation Analysis
Data Collection
Statistical Analysis
Probability
Central Limit Theorem
Hypothesis Testing
Chi-Square Test
ANOVA
Central Tendency
Measures of Variability
Measure of Skewness
Kurtosis
Frequency Distribution Table
Data Leakage
Data Balancing
K-fold Cross Validation
Model Building
Introduction to Machine Learning
Supervised Machine Learning
Linear Regression
Pearson's Coefficient
Coefficient of Determinant
Classification (Logistic Regression
Decision Trees
SVM)
Regression (Linear
Ridge
Lasso)
Clustering (K-means
Hierarchical)
model evaluation
cross validation
Types of Linear Regression
Regression analysis
Missing Value Detection
Data handling and prediction
Scikit Learn Library
Logistic Regression
Naïve Bayes
Entropy
Heterogeneity
Shannon's Entropy
Preventing Overfitting
Random Forest
Random Forest Regression
Hands-on
Logistic Regression vs Random Forest
Linear Regression vs Random Forest
Unsupervised Learning
Clustering
k-means Clustering
Introduction to Hierarchical Clustering
Agglomerative Hierarchical Clustering
Euclidean Distance
Manhattan Distance
Minkowski Distance
Jaccard Index
Cosine Similarity
Optimal Number of Clusters
Introduction to Machine Learning
Understanding the ML Pipeline
Data Preparation
Formatting Data
Data Transformation
Building ML models
Analyzing ML models
Jenkins
Continuous Integration
Version Control
Git
GitHub
Git Vs GitHub
Accessing GitHub
Touring GitHub
Creating Repository
Committing Changes
Branching
Merging
Pull Requests
Basic Git Workflow
Creating Git Profile
DevOps Fundamentals
Version Control With Git
Containerization Using Docker
Continuous Integration
Jenkins Setup
DevOps Lifecycle
Automation Practices
Software Deployment
Monitoring Tools
Collaboration In DevOps Teams
Docker
Docker Best practices
Optimizing Docker Files
Docker
Docker Storage
Docker Network
Docker Compose
Docker
grafana
prometheus
Docker Monitoring
Docker
Docker swarm
Orchestration
AWS ECR
AWS ECS
Docker
grafana
prometheus
Docker Monitoring
Spring boot
Deployment
Containerization
YAML files
Kubernetes Architecture
R programming fundamentals
variables
data types
data structures
control structures
functions
packages
importing data into R
manipulating data in R
performing statistical analysis in R
data cleaning and wrangling
statistical modeling
Big Data basics
Hadoop
HDFS
Hive basics
Hive querying
Hive data upload
Hive simple operations
Spark
RDDs
Hadoop

Free Courses

1

Python for Machine Learning

2

Probability and Probability Distributions for Machine Learning

3

Statistics for Machine Learning

4

Inferential Statistics

5

Introduction to Descriptive Statistics

6

Frequency Distribution

7

Data Preparation for Machine Learning

8

Basics of Machine Learning

9

Machine Learning Algorithms

10

Linear Regression

11

Supervised Machine Learning with Logistic Regression and Naïve Bayes

12

Decision Tree

13

Random Forest

14

Random Forest Regression

15

Unsupervised Machine Learning with K-means

16

Hierarchical Clustering

17

Machine Learning Pipeline

18

Jenkins Tutorial

19

GitHub Tutorial for Beginners

20

Introduction to DevOps

21

Docker Best Practices

22

Docker for Intermediate Level

23

Monitoring in Docker

24

Docker Orchestration

25

Docker Projects

26

Introduction to Kubernetes

Optional Free Courses

1

Introduction to R

2

Introduction to Big Data and Hadoop

3

Introduction to Apache Hive

4

Spark Basics

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Industry relevant skills

Upskill yourself with 1000+ courses across different domains and earn a certificate.

Stand out to recruiters

Earn certificates for each course you complete and share it online to get noticed by recruiters.

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Curated set of online courses

Relevant online courses that you can learn and complete at your convenience.
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We guide you through your entire learning journey, from the first course to learning new skills.
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Hand-picked curated courses in each path will help you fasttrack your journey and gain a new skill in just a few months.

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Frequently Asked Questions

What skills do you need to become a Machine Learning Engineer?

To become a Machine Learning Engineer, you need the following skills:

Applied Mathematics and Statistics: Mathematics and Statistics are the fundamental skills required for a Machine Learning Engineer. The topics include Linear Algebra, Statistics (Mean, Median, and Mode), Probability, Calculus, and a few other concepts.
Computer Science and Programming Fundamentals: The next step would be to learn and master various programming languages, like Python and R, SQL for database management, distributed computing with Apache Spark and Hadoop, and several other concepts. The aspirants must also master computer science fundamentals, such as data structures and algorithms, time and space complexity, and more.
Machine Learning Algorithms: An aspirant must understand and master various essential Machine Learning algorithms, such as Supervised, Unsupervised, and Reinforcement Learning. The learning techniques mentioned earlier include several sub-topics like Linear and Logistic Regression, Naive Bayes Classification, k-Means Clustering, Decision Trees, Random Forests, and many more.
Data Modeling and Evaluation: Data Modeling and Evaluation are critical concepts necessary for a Machine Learning Engineer to evaluate data, identify patterns, and predict the desired outcomes. The techniques include classification, regression, clustering, and dimension reduction.
Communication Skills: Communication skills and other soft skills are as critical as technical skills for an ML Engineer.

Which course is best for becoming a Machine Learning Engineer?

The Post Graduate Program in Machine Learning from the University of Texas at Austin is one of the best courses to learn Machine Learning. The University of Texas at Austin is one of the top-tier universities worldwide, focusing on cutting-edge technologies like AI and ML, Data Science, Business Analytics, and much more.

The renowned faculty from UT Austin and Great Lakes Executive Learning teach the program, and the course covers all the essential tools necessary. Furthermore, it provides personalized career support through GL (Great Learning) Excelerate platform after successfully completing the program and awards Post Graduate Certification in Machine Learning.

[Explore more about the Machine Learning Course]

How much does being a Machine Learning Engineer make?

A Machine Learning Engineer makes a handsome salary around the world. Let’s look at some average base salaries of ML Engineers from various countries:

United States: $77k to $154k with a median salary of $112k per annum (PayScale)
United Kingdom: £31,000 to £86,000 with a median wage of £51,000 per annum (Glassdoor)
Australia: AU$63k to AU$143k with a median salary of AU$104k per annum (LinkedIn)
India: ₹3.05L to ₹20L with a median wage of ₹7.32L per annum (PayScale)

Canada: CA$60k to CA$140k with a median salary of CA$90k per annum (LinkedIn)

How long does it take to become a Machine Learning Engineer after the 12th standard?

It depends on the prerequisites of an IT firm. Few companies require a Bachelor’s Degree of either 3-4 years. In contrast, some tech giants like Google, Apple, Microsoft, etc., don’t mandate any degree as they primarily focus on an aspirant’s skill set and talent.

Consequently, it usually takes 3-4 years to become a Machine Learning Engineer after the 12th standard as most firms require a Bachelor’s Degree in either Computer Science or Information Technology. Nevertheless, an aspirant can instantly become a Machine Learning Engineer at a beginner’s level by cracking the jobs in tech giants like Apple, Google, or Microsoft. But the aspirant must have tremendous expertise in programming languages, data structures and algorithms, a few ML algorithms, or implement any ML-related projects.

Machine Learning Engineer

Premium Degree & Certificate Programs

Start your journey for free

Free Courses

1

Python for Machine Learning

2

Probability and Probability Distributions for Machine Learning

3

Statistics for Machine Learning

4

Inferential Statistics

5

Introduction to Descriptive Statistics

6

Frequency Distribution

7

Data Preparation for Machine Learning

8

Basics of Machine Learning

9

Machine Learning Algorithms

10

Linear Regression

11

Supervised Machine Learning with Logistic Regression and Naïve Bayes

12

Decision Tree

13

Random Forest

14

Random Forest Regression

15

Unsupervised Machine Learning with K-means

16

Hierarchical Clustering

17

Machine Learning Pipeline

18

Jenkins Tutorial

19

GitHub Tutorial for Beginners

20

Introduction to DevOps

21

Docker Best Practices

22

Docker for Intermediate Level

23

Monitoring in Docker

24

Docker Orchestration

25

Docker Projects

26

Introduction to Kubernetes

Optional Free Courses

1

Introduction to R

2

Introduction to Big Data and Hadoop

3

Introduction to Apache Hive

4

Spark Basics

Earn a certificate

Stand out to recruiters

Earn a certificate

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