https://github.com/hemansnation/machine-learning-engineer
Machine Learning Engineer Roadmap
https://github.com/hemansnation/machine-learning-engineer
Last synced: 8 months ago
JSON representation
Machine Learning Engineer Roadmap
- Host: GitHub
- URL: https://github.com/hemansnation/machine-learning-engineer
- Owner: hemansnation
- Created: 2022-02-06T18:18:05.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-07T03:31:56.000Z (over 3 years ago)
- Last Synced: 2024-12-31T07:30:31.478Z (9 months ago)
- Size: 9.77 KB
- Stars: 7
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine-Learning-Engineer
Machine Learning Engineer Roadmap
## Python Advance
- Python Core understanding
- Object Orientation
- Function as an Object
- Logic Building
- Decorators
- Generators
## Data Structures
- Time Complexity
- Big(O) Notation
- Binary Search
- Sorts: Bubble, Quick, Merge, Selection, Insertion
- Tree
- Graphs
- BFS and DFS
- Heap
## Mathematics
- Statistics
- Linear Algebra
- Probability
- What if I’m Not Good at Mathematics
- 5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics
- How do I learn machine learning?
## Practice Questions a lot
- Euler Project
- LeetCode
- HackerRank
## Operating System
- Process Scheduling
- Multi Threading
- Memory Management
- File Management
- Capacitors, Registor, Transistor
## Machine Learning
- Basics: Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning.
## In-Depth
- Overview, goals, learning types, and algorithms Link
- Data selection, preparation, and modeling Link
- Model evaluation, validation, complexity, and improvement Link
- Model performance and error analysis Link
- Unsupervised learning, related fields, and machine learning in practice Link
## What Responsibilities Are Part of a Machine Learning Engineer Job Description?
- Designing, developing, and researching Machine Learning systems, models, and schemes
- Studying, transforming, and converting data science prototypes
- Searching and selecting appropriate data sets
- Performing statistical analysis and using results to improve models
- Training and retraining ML systems and models as needed
- Identifying differences in data distribution that could affect model performance in real-world situations
- Visualizing data for deeper insights
- Analyzing the use cases of ML algorithms and ranking them by their success probability
- Understanding when your findings can be applied to business decisions
- Enriching existing ML frameworks and libraries
- Verifying data quality and/or ensuring it via data cleaning
## Characteristics of a Successful Machine Learning Engineer
Every great Machine Learning expert would seem to have a few traits in common. Here are the characteristics of a successful Machine Learning Engineer:
#### They’re Solid Computer Programmers
If you’re looking to pursue a career in AI and machine learning, you’ll need to learn to program. A programmer should understand frequently used languages including C++, Java, and Python, and it doesn’t end there. Languages like R, Lisp, and Prolog have also become important languages for machine learning. Still, not all successful machine learning engineers need to necessarily be experts in HTML or JavaScript.
#### They Have a Sturdy Foundation in Math and Statistics
You can’t master machine learning without at least a little bit of math. Whether you have a formal background in math and statistics or not, you’ll need to have at least a high-school level of math competency to keep up. At the heart of many machine learning algorithms is a formal characterization of probability and techniques derived from it. Closely related to this is the field of statistics, which provides various measures, distributions, and analysis methods that are necessary for building and validating models from observed data. Essentially, many machine learning algorithms are extensions of statistical modeling procedures.
#### Machine Learning Professionals are Creative Problem Solvers
The best ML Engineers are driven by curiosity. They don’t respond with frustration when a model or experiment fails, but instead, they’re curious to find out why.
But they also solve problems efficiently. The best machine learning pros develop generalized approaches to fixing bugs and misclassifications in their machine learning models because fixing individual bugs will be time-consuming while also making your models more difficult and complex to work with.
It’s also important to balance the determination to solve problems with the practical understanding that a lot of your models and experiments will fail. The best Machine Learning Engineers develop a sense of when it’s time to walk away.
#### They Love the Iterative Process
Machine learning is by its nature an iterative process. To be effective in this role, one needs to actually enjoy that style of development. Building a machine learning system means one builds a very simple model quickly, to begin with, then iterates on getting it better with each stage.
Again, though, a good Machine Learning Engineer can’t be too stubborn. You need to develop an understanding of when it’s time to stop. It’s always possible to improve the accuracy of any machine learning system by continuing to iterate on it, but one needs to learn to develop an intuition for when it’s no longer worth the time and effort.
#### They Have a Strong Intuition About Data
There is no machine learning without analyzing data. A good Machine Learning Engineer or Data Scientist needs to be able to quickly sift through large data sets, identify patterns, and know how to use that data to come to meaningful and actionable conclusions.
It’s almost like they have a sixth sense for data. Data management skills are crucial.
They should also be handy at building big data pipelines. And one needs to also understand the power of visualization. To ensure the insights you’ve unearthed are properly understood and appreciated by others, you must be handy with data visualization tools like Excel, Tableau, Power BI, Plotly and Dash.