Are you looking to become a machine learning engineer?
I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
SAP is Hiring for ASSOCIATE - DATA SCIENTIST"
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Qualifications:- GRADUATION
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Role:- ASSOCIATE - DATA SCIENTIST
Qualifications:- GRADUATION
Mode:- WORK FROM OFFICE
CTC:- 10 LPA
Location:- BANGALORE, KARNATAKA
Apply Now:- https://jobs.sap.com/job/Bangalore-Associate-Data-Scientist-KA-560066/1163936701/
🚀 Join the WhatsApp Group for more job updates and pass this information with your friends and groups 🚨
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Forwarded from Data Analyst Jobs
Citi is hiring!
Position: Data Scientist
Qualifications: Bachelor’s Degree
Salary: 12 - 18 LPA (Expected)
Experience: Freshers (0 - 2 Years)
Location: Bengaluru, India (Hybrid)
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Position: Data Scientist
Qualifications: Bachelor’s Degree
Salary: 12 - 18 LPA (Expected)
Experience: Freshers (0 - 2 Years)
Location: Bengaluru, India (Hybrid)
📌Apply Now: https://jobs.citi.com/job/-/-/287/76402677072
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AB InBev is hiring!
Position: Machine Learning Engineer
Qualification: Bachelor’s/ Master’s Degree
Salary: 12 - 20 LPA (Expected)
Experience: Freshers/ Experienced
Location: Work From Home/ office
📌Apply Now: https://ab-inbev-gcc.sensehq.com/careers/jobs/54769
Position: Machine Learning Engineer
Qualification: Bachelor’s/ Master’s Degree
Salary: 12 - 20 LPA (Expected)
Experience: Freshers/ Experienced
Location: Work From Home/ office
📌Apply Now: https://ab-inbev-gcc.sensehq.com/careers/jobs/54769
Job openings at AB InBev GCC
AB InBev GCC Services India Private Limited (“GCC”) is a group company of Anheuser-Busch InBev (“AB InBev”) incorporated in India on 10th December, 2014 to do business related to IT & ITES. As a background, AB Inbev is the world’s leading brewer bringing…
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1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
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Forwarded from Data Analyst Jobs
BCG is Hiring for DATA SCIENTIST
Role:- DATA SCIENTIST
Qualifications:- GRADUATION
Experience:- Fresher's and Experienced
Mode:- WORK FROM OFFICE
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Role:- DATA SCIENTIST
Qualifications:- GRADUATION
Experience:- Fresher's and Experienced
Mode:- WORK FROM OFFICE
Apply Now:- https://careers.bcg.com/global/en/job/BCG1US26309EXTERNALENGLOBAL/Data-Scientist-India-BCG-X
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
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Forwarded from AI Jobs | Artificial Intelligence
Google's Applied ML team is hiring:
Hiring for skilled ML engineers in Bangalore to work on edge optimization and acceleration of GenAI and non-GenAI models for google's Edge TPU.
For more information about the opportunity and application, please refer to the following link:https://www.google.com/about/careers/applications/jobs/results/76351911959110342
Hiring for skilled ML engineers in Bangalore to work on edge optimization and acceleration of GenAI and non-GenAI models for google's Edge TPU.
For more information about the opportunity and application, please refer to the following link:https://www.google.com/about/careers/applications/jobs/results/76351911959110342
Remote Data Systems Analyst
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Looking for a Data Scientist Intern
Location: Bangalore (Whitefield)
Duration: 6 months
Ready to kickstart your career in data science? share your CV careers@neewee.ai
Don't miss this opportunity to grow your skills and make an impact. Tag your friends or connections who might be interested!
Location: Bangalore (Whitefield)
Duration: 6 months
Ready to kickstart your career in data science? share your CV careers@neewee.ai
Don't miss this opportunity to grow your skills and make an impact. Tag your friends or connections who might be interested!
Infeneon Hiring !!
Role - Data Scientist
Exp - 1 year
Location- Bangalore
https://jobs.infineon.com/careers/job/563808957790879?domain=infineon.com#!source=400
Role - Data Scientist
Exp - 1 year
Location- Bangalore
https://jobs.infineon.com/careers/job/563808957790879?domain=infineon.com#!source=400
IQvia Hiring
Role - ML Engineer
Exp - 2 year
https://jobs.iqvia.com/job/-/-/24443/76813568224?source=LinkedIn_Slots
Role - ML Engineer
Exp - 2 year
https://jobs.iqvia.com/job/-/-/24443/76813568224?source=LinkedIn_Slots