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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

8 Must Have Skills to Become an AI Engineer in 2025

D
Deepsandhya Shukla Last Updated : 21 Nov, 2024
4 min read

Introduction

The Artificial intelligence world is moving very fast, and AI engineers are at the forefront of this revolution. Companies of all stripes are embracing AI to gain a strategic advantage, creating a surge in demand for these skilled professionals. However, becoming an AI engineer isn’t just about having a technical mind; it requires a unique blend of expertise. In this article, we’ll examine the 8 essential skills you need as an AI engineer!

AI Engineer Skills

Table of contents

Problem Solving Skills

AI Engineers are basically the builders of artificial intelligence. Imagine building a house, but you never faced any unexpected issues – that’s what it would be like if problems never came up in AI! Because things can go wrong with data, algorithms, or even how the AI is used, AI Engineers need to be top-notch problem solvers. They have to figure out what’s wrong, why it’s happening, and come up with solutions to keep the AI running smoothly. So, strong problem-solving skills are like a toolbox for AI Engineers – essential to making sure their projects work perfectly.

Programming Skills

A strong foundation in programming is non-negotiable for an AI engineer! Programming languages like Python, R, and Java serve as the main instruments for crafting AI models and algorithms. Among these, Python stands out for its user-friendly nature and a comprehensive suite of libraries like TensorFlow and PyTorch, which streamline the development of AI solutions.

AI programming goes beyond memorizing code. You need to write clean, efficient code that can handle big and complex AI projects, even as they grow. Knowing SQL helps too, since AI projects rely on data from databases.

If you want to master all the AI Engineer skills, then we have a perfect course for you. Checkout our AI/ML BlackBelt Plus program and kickstart your career!

Mathematics Skills

AI and mathematics are two sides of the same coin. A strong foundation in math, especially calculus, linear algebra, and statistics, is crucial for any AI Engineer. These concepts are essential for understanding, developing, and optimizing the performance of AI models. Let’s explore each of these areas in more detail to see why they matter.

Statistics

Statistics is essential for understanding and interpreting data, crucial for training and evaluating AI models. It enables AI engineers to:

  • Identify patterns and trends within data.
  • Evaluate model performance using metrics like accuracy and precision.
  • Make informed decisions based on statistical analysis.

Linear Algebra

Linear algebra underpins many operations in AI, especially in handling and manipulating large data sets and in the workings of neural networks. It is key for:

  • Efficiently representing and processing data using vectors and matrices.
  • Performing transformations and operations critical for AI algorithms.

Calculus

Calculus is vital for modeling change and for the optimization processes in AI, helping to fine-tune models for better performance. Its role includes:

  • Facilitating the optimization of AI models through techniques like gradient descent.
  • Understanding how changes in model inputs affect outputs, crucial for model adjustment.

Also Read: How to Learn Mathematics for Machine Learning?

Machine Learning Skills

Machine learning is the foundation of artificial intelligence. As an AI Engineer, understanding machine learning concepts, algorithms (including neural networks and deep learning), is crucial. This knowledge empowers you to design, build, and train AI systems that learn and improve from data.

Furthermore, proficiency in machine learning frameworks like TensorFlow or PyTorch elevates your ability to craft innovative AI solutions. These frameworks help you solve real-world problems efficiently, ultimately contributing to the advancement and widespread application of AI across various industries.

Also Read: Top 10 Machine Learning Algorithms to Use in 2024

Data Analysis Skills

Data serves as the foundational element of artificial intelligence. Therefore, the ability to analyze and interpret complex data sets is critical. This involves preprocessing data, handling missing values, and understanding data visualization techniques to extract valuable insights. An AI engineer must be adept at using data analysis tools and libraries such as Pandas and NumPy to manipulate data and prepare it for modeling. These skills are crucial for identifying patterns, making predictions, and driving decision-making processes in AI projects.

Without meticulous analysis and preprocessing, even the most sophisticated machine learning algorithms can falter, unable to uncover meaningful patterns or make accurate predictions. Clean and usable data is the prerequisite for a model’s ability to learn effectively and adapt to new information, ultimately determining the success of AI applications. 

Communication Skills

The importance of communication skills cannot be overstated. AI engineers must effectively communicate complex technical concepts to non-technical stakeholders, such as project managers, clients, and team members. This includes writing clear documentation, presenting findings, and explaining the implications of AI technologies in an accessible manner. Strong communication skills ensure that ideas are conveyed clearly, feedback is incorporated efficiently, and projects are aligned with business objectives.

Checkout our course on Structured Thinking and Communications for Data Science Professionals!

Critical Thinking Skills

Critical thinking is what allows AI engineers to navigate the myriad of decisions and ethical considerations inherent in developing AI systems. It involves questioning assumptions, evaluating evidence, and synthesizing information from various sources to make informed decisions. An AI engineer with sharp critical thinking skills will be better equipped to anticipate potential problems, assess the viability of different approaches, and ensure the responsible use of AI technologies.

Domain Knowledge

Last but certainly not least, domain knowledge is what ties everything together. Understanding the specific industry or domain where AI is being applied (such as healthcare, finance, or transportation) allows for the development of tailored AI solutions that address real-world problems effectively. This knowledge aids in designing systems that are not only technically sound but also valuable and relevant to end-users.

Conclusion

Becoming an AI engineer requires a diverse skill set that spans technical, analytical, and interpersonal skills. From the intricate mathematics that underpin AI algorithms to the programming languages that bring them to life, and the soft skills needed to navigate the complexities of real-world applications—mastering these eight skills will put you on the path to a successful career in AI engineering. As the field continues to grow and evolve, so too will the opportunities for those ready to meet its challenges head-on.

Start building these skills today, and tomorrow, you could be shaping the future of AI.

Struggling to find the right resources? Enroll in our AI/ML BlackBelt Plus program today!

AI EngineerAI Engineer Skills
D
Deepsandhya Shukla

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