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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

10 Must Have Machine Learning Engineer Skills in 2024

A
Analytics Vidhya Last Updated : 05 Feb, 2024
8 min read

Introduction

In today’s evolving landscape, organizations are rapidly scaling their teams to harness the potential of AI, deep learning, and ML. What started as a modest concept, machine learning, has now become indispensable across industries, enabling businesses to tap into unprecedented opportunities. A crucial catalyst behind this transformation is the skill set of machine learning engineers. These professionals wield the expertise to construct sophisticated algorithms and systems that can autonomously acquire knowledge and insights. As machine learning continues to reshape our world, the prowess of these engineers plays a pivotal role in driving innovation and unlocking new frontiers of possibility. Let’s explore top machine learning engineer skills to have in 2024!

Table of contents

What is a Machine Learning Engineer?

A Machine Learning Engineer specializes in designing, building, and implementing machine learning algorithms and models to solve complex problems. They bridge the gap between data science and software engineering, applying their expertise to develop predictive models, recommendation systems, and other AI-driven applications. Machine Learning Engineers work with large datasets, preprocess and clean data, select appropriate algorithms, and fine-tune models for optimal performance.

Their responsibilities include coding, training, and deploying machine learning models and collaborating with data scientists and domain experts to understand business requirements. Machine Learning Engineers also focus on optimizing models for scalability, reliability, and efficiency in production environments. They often work with frameworks like TensorFlow, PyTorch, and scikit-learn and possess a strong programming, mathematics, and data manipulation foundation. Overall, Machine Learning Engineers play a vital role in developing and deploying machine learning solutions across various industries.

Also Read: Machine Learning Engineer Salary in India and Abroad

Top 10 Machine Learning Engineer Skills

Here are the top ML skills that empower machine learning engineers to innovate and tackle complex AI and data science challenges:

  • Programming Languages
  • Mathematics and Statistics
  • Machine Learning Algorithms
  • Data Preprocessing
  • Data Visualization
  • Model Evaluation and Validation
  • Machine Learning Libraries and Frameworks
  • Big Data Tools
  • Version Control
  • Problem Solving and Critical Thinking

Programming Languages

Dealing with minimal kinds of tasks, such as writing basic programs, scripting web pages, etc., is quite different from working with machine principles. It requires significant programming skills and expertise. The fundamental and most significant skill for a career in machine learning is having an in-depth knowledge of a programming language like Python. Given that it is easy to learn and offers numerous uses than many other languages. Python is the basis of machine learning. Understanding C++ is useful for improving program speed, although Java is required to deal with Hadoop and Hive and other technologies required for a machine learning engineer.

Useful Resources

Mathematics and Statistics

Machine learning is built on mathematics. To fully comprehend machine learning, you should be familiar with key ideas in probability theory, mathematics, and linear algebra. If you have a solid background in mathematics, it will be relatively easy for you to comprehend many of the ML algorithms because they are applications developed from statistical modeling techniques.

Math skills for machine learning engineer
Source: Biotreetechnologies

We can see the significance of statistics for machine learning because machine learning algorithms are founded on statistical models. That means that statistics play an important part in the development of algorithms. Therefore, understanding statistical tools is essential if you want to advance your job prospects in machine learning.

Other subtopics like probability also play an important role in machine learning. Probability benefits in forecasting future results because most machine learning algorithms operate under uncertainty and must arrive at reliable findings.  Machine learning would be able to forecast future versions with the help of calculations in probability, such as derivative approaches, Bayes Nets, and Markov decisions.

Useful Resources

Machine Learning Algorithms

The algorithm and optimization approach utilized determines the efficiency and durability of a machine learning technology. As a machine learning engineer, you should be familiar with a wide range of machine learning algorithms, including regression, classification, clustering, and reinforcement learning. Additionally, you should be conversant with more complicated concepts like dimensionality reduction, ensembling, bagging, and boosting.

Machine learning algorithms in ML engineer skills
Source: Ilegra

Various API Packages and libraries are available, including Google TensorFlow, Apache Spark’s MLlib, and Microsoft’s CNTK, which are used in machine learning. Implementing these developments needs a thorough grasp of various concepts, how they connect with various systems and the potential catches that may develop along the way. 

Useful Resources

Data Preprocessing

Pre-processing is the notion used when referring to the changes made to our data before we send it to the algorithm. Data preparation is a method for transforming messy data into accurate data sets. Using data mining algorithms for raw data will yield negative outcomes since they would be unable to successfully detect trends. Therefore, data processing is essential for improving the overall level of quality of data. 

For data to be appropriate for analysis and building models, it must be possible to clean, preprocess, and transform it. This consists of operations like addressing missing values, transforming feature data types, feature engineering, feature scaling, and feature normalization.

Data preprocessing as machine learning engineer skills
Source: Medium

Useful Resources

Data Visualization 

An essential component of machine learning is data visualization. Data visualization can be made possible with the help of Python tools like Matplotlib, Seaborn, or Plotly. Data visualization facilitates an understanding of patterns, trends, and correlations. Tableau, Qlikview, Dash, and other well-known data visualization tools are accessible and commonly used in developing dashboards. It is a plus point if you have already worked with data wrangling and building dashboards in any of the data visualization technologies stated. 

Data Visualization 
Source: WPdownload

Useful Resources

Model Evaluation and Validation

Data modeling and evaluation skills are essential for machine learning engineers. The key objective of machine learning is the analysis of unstructured data models, which involves knowledge of data modeling. It includes discovering patterns that are hidden from plain sight and requires knowing the fundamental makeup of the data. To effectively help with data modeling and evaluation, you must be versed in all of the characteristics of various approaches. 

Someone who works with data should have a solid understanding of ensemble techniques, deep learning techniques, supervised and unsupervised learning algorithms, and more. Professionals are more equipped to choose the right model when they are mindful of the assumptions, limitations, and benefits of various models.

Data modeling in machine learning engineer skills
Source: Datatron

Useful Resources

Machine Learning Libraries and Frameworks

As an element of their day-to-day tasks, machine learning engineers might have to deal with many packages, libraries, and algorithms. A machine learning engineer needs to comprehend the different elements of machine learning algorithms and frameworks. You should be competent in various machine learning algorithms, such as classification, regression, reinforcement learning, and clustering. Along with that, you should also be proficient with advanced topics like the bagging process, dimensionality reduction, ensembling, and boosting. You must also be aware of python libraries like PyTorch, Scikit-Learn and TensorFlow to construct machine learning algorithms.

ML Libraries
Source: Dummies

Useful Resources

Big Data Tools

Big data and data access methods and tools are required to extract valuable insights from vast amounts of data. To effectively address the problems and benefit from the advantages of machine learning, one needs to have extensive knowledge of big data technology. Machine learning engineers must be knowledgeable in the platforms and technologies used to manage huge amounts of data, including SQL, Spark, Hadoop, Hive, and Pig. Data engineers that enhance their abilities and competence in big data technologies are better able to concentrate on the challenges presented by big data and maximize the effectiveness of machine learning in various kinds of fields.

Useful Resources

Version Control

Every software development ecosystem needs version control, but machine-learning environments require it even more. To address problems and prevent disagreements, you must keep track of every change made by the software team while developing an app or an ML model. You can employ a version control system to accomplish that.  Version control systems assist data scientists in reducing the load of manual versioning and facilitating team communication by dynamically versioning files and projects.

Version control skills f
Source: Caowei

New models are often introduced step by step until teams are confident they are operating as intended in order to ensure failure tolerance and perform proper testing. Versioning provides the tools necessary for sharing the appropriate model versions at a suitable moment.

Useful Resources

Problem Solving and Critical Thinking

The goal of machine learning is to address problems in real time. One must possess the skills necessary to analyze the advantages and disadvantages of an event, consider all the possibilities, and determine the best plan of action when trying to handle an issue efficiently. You must be capable of posing relevant questions, defining precise objectives, and establishing quantifiable success metrics. 

Critical thinking skills serve in breaking down challenging problems into smaller, more manageable components and developing effective solutions. Problem-solving abilities are essential in establishing optimal validation procedures, such as A/B testing, time-series splitting or cross-validation, to ensure the model applies effectively to unidentified information. 

Useful Resources

Conclusion

Machine learning happens to be a steadily growing trend and has applications in nearly every industry today as technology advances daily to new heights. As the need for qualified machine learning engineers increases, it becomes vital for budding machine learning engineers to acquire the necessary skills and keep up with the most recent Machine learning-driven tools and technologies. The Certified AI and ML Blackbelt Plus Course includes fundamental and advanced machine learning engineer skills. Explore the course today to acquire the right ML skills!

Frequently Asked Questions

Q1. What skills do you need for machine learning?

A. The basic machine learning engineer skills include the fundamentals of programming languages, mathematics and statistics, data processing and visualization skills, machine learning algorithms, etc.

Q2. What is machine learning as a skill?

A. Machine learning is a broad section of computer science that focuses on creating algorithms and programs that replicate how humans learn and perform tasks and develop skills as the computer gives more data. Machine learning as a skill includes handling large data sets with the help of proficiency in different programming languages, algorithms and data structures.

Q3. What are the best AI and ML skills.

A. Skills required for AI and ML include proficiency in programming languages, cloud computing, statistics and math, model assessment and more.

Q4. Is machine learning an in-demand skill?

A. Machine learning is a growing field of science and is an in-demand skill with which you can explore various job opportunities in various industries.

data visualizationmachine learningmachine learning algorithmsMachine Learning Engineer SkillsMLML skillsskills
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8 Ways to Improve Accuracy of Machine Learning Models (Updated 2024)
8 Ways to Improve Accuracy of Machine Learning Models (Updated 2024)
8 Must Have Skills to Become an AI Engineer in 2024
8 Must Have Skills to Become an AI Engineer in 2024

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