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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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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
  • Pandas
  • 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
  • Machine Learning Basics
  • Supervised and unsupervised learning
  • Algorithm basics
  • K-Nearest Neighbour Algorithm
  • Linear Regression Technique
  • Naive Bayes Algorithm
  • Support Vector Machines
  • Random Forest Algorithm
  • 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
  • GitHub
  • Committing
  • Merging
  • Branches
  • Creating Pull Requests
  • Version Control
  • Containerising
  • Continuous Integration
  • 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 Commands
  • R Packages
  • R Functions
  • R Datatypes
  • Operators in R
  • RStudio
  • Big Data basics
  • Hadoop
  • HDFS
  • Hive basics
  • Hive querying
  • Hive data upload
  • Hive simple operations
  • Spark
  • RDDs
  • Hadoop

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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.