Machine Learning (ML) is more than just a buzzword; it's central to AI-driven innovation, societal progress, and business development in nearly every industry. This article guides you through the basics of how to learn machine learning, where to begin, and how to dig deeper into the subject if you're already familiar with the basics.
This machine learning roadmap will also help you customize your learning path, regardless of your background or prior knowledge. To start learning machine learning from scratch, read on.
Table of Contents
- Why Learn Machine Learning in 2024?
- Can I Learn Machine Learning on My Own?
- Who Is Machine Learning for?
- How To Get Started with Machine Learning
- Machine Learning Foundation: The Basics
- Machine Learning Skills: The Specifics
- Where to Start with Machine Learning: Resources and Next Steps
- Start Your ML Learning Journey
- FAQs
Why Learn Machine Learning in 2024?
With expansive job opportunities and sector-wide growth, 2024 presents an opportunity to master ML skills.
According to the Future of Jobs Report 2023, “Demand for AI and machine learning specialists is expected to grow by 40%, or 1 million jobs, as the usage of AI and machine learning drives continued industry transformation.”
The August 2023 Future of Work Report by LinkedIn equally cites a 21-fold increase in global job postings in English that mentioned GPT or ChatGPT since November 2022.
But the surge in machine learning and AI job opportunities is not limited to traditional technical roles like ML engineers and data scientists. Our analysis of the data engineer job market for 2024—a role that doesn't typically require ML skills—revealed that 30% of job postings listed ML as a required skill. This suggests that ML is becoming increasingly applicable across various fields.
Considering these facts, it’s clear that we are in the era of AI and machine learning, making machine learning self-study an excellent opportunity for job growth.
Consider these reasons why you should learn ML in 2024:
- There’s a high demand for machine learning skills among data and AI experts across various industries, such as retail, finance, healthcare, and technology.
- A basic understanding of machine learning can improve collaboration and communication between technical and multidisciplinary teams. This ensures effective dialogue and alignment between business goals and technical capabilities.
- Machine learning is pivotal to innovation initiatives across all industries. Data scientists, analysts, engineers, and project leaders who utilize this discipline will gain an edge in identifying opportunities for innovation, investment, and subsequent business growth.
Can I Learn Machine Learning on My Own?
Yes, you can absolutely learn machine learning on your own.
With the wealth of online resources—from comprehensive courses to practical project opportunities—self-guided learning has never been more accessible.
Anyone can undertake this learning journey by establishing a solid foundation in mathematics and programming—exploring machine learning concepts via structured courses and applying this knowledge through hands-on projects.
Out 365 Data Science platform offers numerous courses covering many aspects of ML, including processes, algorithms, and coding.
While staying updated with the rapidly evolving field requires dedication and a proactive approach, mastering machine learning independently is well-paved for those willing to take the initiative.
Let's explore the steps to navigate the machine learning path effectively.
Who Is Machine Learning for?
Artificial Intelligence (AI)—with machine learning as a significant component—is no longer only a subject for data and AI experts. It’s also evolving into a necessary discipline for professionals from various backgrounds.
Figure: Areas of AI with Machine Learning at the Forefront | Source: Iván Palomares
Anyone interested in understanding how data-driven insights can enhance decision-making and innovation across various fields should learn machine learning. Beyond ML engineering and data science, professionals in healthcare, finance, marketing, and even creative industries can leverage ML to uncover patterns, make predictions, and drive strategic decisions.
Learning ML can be immensely beneficial if you're keen to harness data for informed decision-making and problem-solving in your domain.
Given machine learning’s broad applicability across numerous sectors, AI and ML literacy is crucial—not just for tech professionals but all fields. This encompasses the competencies to understand and critically evaluate AI's value, capabilities, limitations, tools, and responsible use in various scenarios.
To learn how to get into machine learning, you must also understand how to utilize AI's potential responsibly and effectively.
How to Get Started with Machine Learning
Embarking on a journey to study machine learning can be exciting but daunting if you're unsure of where to begin. The method to get started with machine learning largely depends on your background, goals, and interests.
This machine learning guide aims to create a personalized learning roadmap tailored to your existing knowledge.
1. Identify Your Background and Prior Knowledge
If applicable, identify your current background and prior knowledge in statistics, mathematics, programming, AI foundations, or domain expertise.
2. Define Your Goals and Motivations
Clearly define your goals and motivations for learning machine learning: skills development, career progression, and personal interests.
3. Choose Your Starting Points
Given your background and goals, choose one or two starting points that align with your current knowledge, areas you wish to improve, and interests.
For instance, the machine learning roadmap for a finance expert with a solid statistics and math foundation who aims to acquire skills for training, testing, and evaluating stock market prediction models might have programming skills as a starting point.
We'll review the various ML skill subsets below, helping you identify your current strengths and areas for development.
4. Develop a Strong Foundation
Once you’ve identified your starting points, you must develop solid foundational knowledge by addressing gaps, which includes understanding the basics of programming, statistics, and linear algebra.
Proficiency in these areas allows you to understand the mechanics of machine learning algorithms and enables you to implement them effectively. This is a crucial step toward machine learning because it forms the bedrock of all subsequent learning and application in the field.
5. Get Started with Machine Learning Concepts
Once you've mastered the basics, you're ready to get started with machine learning, which covers understanding key algorithms and techniques, the differences between supervised, unsupervised, and reinforcement learning, and methods to evaluate and validate model performance.
6. Explore Specialized Areas of Machine Learning
The next step in studying machine learning involves ML specialization. Explore specialized areas of machine learning that align with your goals or needs. Get hands-on by training your machine learning models using datasets.
For instance, if you're interested in predicting stock prices, you can focus on regression and time series forecasting models instead of classification or clustering. You could also investigate deep learning—a subfield of machine learning that relies on artificial neural networks to tackle complex tasks where other models may fall short.
In a later section of this article, we'll examine a detailed machine learning roadmap from basic to specialized levels.
7. Adopt a Continuous Learning Mindset
The best way to learn machine learning is to adopt a continuous, proactive, and iterative learning approach. Machine learning is a rapidly evolving field marked by ongoing research and innovation. Staying informed about the latest developments and trends, engaging with online communities, and constantly refining your skills and expertise are crucial ways to stay current.
Machine Learning Foundation: The Basics
Once you’ve decided on your starting points, the next step in learning machine learning from scratch is to ensure a well-rounded understanding of the basics.
Machine learning integrates mathematics, statistics, data analysis, computer programming, and domain expertise. Its interdisciplinary scope can be challenging for beginners. Follow this step-by-step machine learning checklist to build a solid foundation.
Statistics for Machine Learning
If the first step of your ML learning journey is acquiring or refreshing a statistics background, it's essential to concentrate on mastering the fundamental principles of statistical analysis.
Descriptive Statistics
Start learning machine learning with descriptive statistics. Understand how to summarize and interpret data using such statistical measures as the mean, standard deviation, and median. Additionally, mastering commonly used data visualization methods in statistics can be beneficial.
Probability Theory
Next, explore probability theory by understanding and modeling probability distributions. These concepts are essential for comprehending datasets and machine learning techniques like Bayesian classifiers.
Statistical Inference
Once you understand probability theory, explore statistical inference, specifically hypothesis testing. These tools are essential for making decisions based on sample data and validating your machine-learning models' performance and appropriateness. Regression Analysis
Conclude by familiarizing yourself with regression analysis. This technique models the relationship between data variables and aids in making predictions. Understanding this concept will also provide insight into one of the most commonly used supervised machine learning techniques: regression models.
Mathematics for Machine Learning
Strengthening one's mathematical foundation is integral to mastering machine learning concepts. Your machine learning self-study should involve understanding linear algebra, differential and integral calculus, and numerical methods.
Linear Algebra
Linear algebra is the ideal start for machine learning—essential for understanding and manipulating high-dimensional data like scalars, vectors, matrices, and tensors. It supports such models and techniques as Singular Value Decomposition (SVD), which is vital for recommender systems in e-commerce.
Differential and Integral Calculus
After establishing a solid foundation in linear algebra, the focus should shift to differential and integral calculus. These areas are crucial because many powerful machine learning models are trained by optimization algorithms, which rely heavily on operations like derivatives and gradients of mathematical functions. These functions quantify a cost or error function to minimize during training.
Numerical Methods
Finally, understanding numerical methods is crucial for numerically solving mathematical problems, optimizing machine learning solutions, and streamlining computationally intensive model training processes.
Data Skills for Machine Learning
Before getting started with machine learning, it's crucial to build a solid base of critical data skills. These are essential for managing vast datasets and deriving meaningful insights that power machine learning algorithms.
Statistical Proficiency
A deep understanding of statistics is indispensable in machine learning. It aids in interpreting data, understanding the significance of algorithm outputs, and making informed decisions based on data. Mastering concepts like probability distributions, statistical testing, and regression analysis provides a solid groundwork for predictive modeling and result interpretation in machine learning.
Programming Acumen
Proficiency in programming languages like Python or R is a cornerstone of machine learning. These languages—particularly Python—are preferred for their rich ecosystem of libraries and frameworks tailored to data science and machine learning. Your machine learning path must include mastering these programming languages.
- Python Libraries: Learn to use essential data analysis libraries like Pandas for data manipulation, Matplotlib and Seaborn for visualization, and NumPy for numerical computations. These tools streamline data cleaning, transformation, and exploratory analysis.
- R Programming: For those inclined toward statistical analysis, R provides a robust environment with its comprehensive suite of packages for data analysis, visualization, and machine learning.
Data Manipulation and Visualization
Data manipulation and visualization should be one of your steps toward machine learning. They are crucial for transforming raw data into actionable insights. Effective data manipulation involves cleaning, aggregating, and transforming data, while visualization helps uncover patterns, trends, and anomalies in data.
- pandas: Essential for data manipulation tasks, pandas enables the easy handling and transforming of structured data.
- Matplotlib and Seaborn: These libraries are vital for creating insightful visualizations, helping to communicate findings, and understanding data better.
Database and SQL Knowledge
Understanding how to retrieve and manipulate database data is fundamental in the machine learning workflow. Structured Query Language (SQL) is the standard for querying and interacting with databases—making it essential for accessing and preparing data for machine learning models.
- SQL: Gain proficiency in SQL to retrieve, update, and manipulate data from databases—an essential skill for working with large, structured datasets often used in machine learning.
By mastering these data skills, you’ll learn the tools needed to get started with machine learning and set a solid foundation that enables you to drive insights and innovations across various machine-learning applications.
Machine Learning Skills: The Specifics
After gaining a firm understanding of the basics, it's time to dig deeper into the specifics of machine learning. This is the next crucial step in how to learn machine learning.
AI Fundamentals
A broad understanding of AI basics can give you a solid foundation for advanced study before beginning machine learning.
Broadening AI Knowledge
- AI Spectrum: Understand the spectrum of AI—recognizing how machine learning, deep learning, NLP, robotics, and computer vision interrelate and stand apart. This provides a well-rounded perspective on AI's possibilities.
- Interdisciplinary Insights: Explore how AI intersects with various fields, offering insights into machine intelligence and human cognition.
Embracing Responsible AI
- Ethical AI: Learn the importance of ethics in AI, focusing on fairness, privacy, and bias. This is essential for developing trustworthy AI systems.
- Societal Impact: Acknowledge AI's societal implications—understanding its effects across sectors and the significance of human-centric AI development.
By mastering these fundamentals, you’ll enhance your machine learning expertise and gain a holistic view of AI's role and responsibilities in today’s world. This will position you to make informed, ethical decisions in your AI-related endeavors.
Machine Learning Fundamentals
Grasping the core principles is crucial for anyone getting started with machine learning. This foundational knowledge sets the stage for more advanced machine learning exploration and application.
- Types of Machine Learning: Understand the distinctions and applications of supervised, unsupervised, and reinforcement learning.
- Data Preprocessing: Learn the importance of cleaning, normalizing, and splitting data into training, validation, and testing sets to ensure model reliability.
- Feature Engineering: Discover how to extract, select, and transform features to improve model performance.
- Bias-Variance Tradeoff: Explore the concepts of bias, variance, underfitting, and overfitting to understand model performance and predictability.
Domain Expertise in Machine Learning
When thinking about how to learn machine learning, you should also consider why and where you want to use it. For professionals from specific industries or subject areas, understanding how machine learning applies to their domain can have numerous benefits.
ML can address industry-specific challenges and drive innovation but can also have negative impacts.
Consider the following factors when incorporating machine learning into your work.
Real-World Applications and Case Studies
- Industry-Specific Insights: Explore case studies where machine learning is applied in your field. Understand the problems machine learning solves, the techniques used, the innovation it brings, and its challenges and limitations.
- Technique Application: Identify which machine learning methods are most relevant and effective in your domain, offering insight into practical applications and outcomes.
Data Mastery in Your Domain
- Data Handling: Learn about the nuances of data collection, preparation, and feature engineering specific to your industry to enhance your ability to leverage ML effectively.
- Unique Challenges: Recognize your domain’s unique challenges and opportunities when implementing ML—from data peculiarities to solution scalability.
Ethical Considerations
- Responsible Deployment: Understand the ethical dimensions of implementing machine learning in your field—ensuring that AI solutions are used responsibly and contribute positively to society.
Experts can utilize machine learning to transform their sectors by focusing on these aspects and making informed decisions that blend technical feasibility with ethical responsibility.
Expand your machine learning roadmap with skills specific to your field.
Machine Learning Algorithms
The next phase of our machine learning study guide focuses on algorithms—the essential tools for tackling various data-driven challenges. Grasping the subtleties of these algorithms is crucial for choosing and utilizing the appropriate method for each task.
- Linear Models: Linear and logistic regression allows for simplicity and efficacy in prediction and classification tasks.
- Tree-Based Models: Decision trees, random forests, and gradient-boosting machines handle non-linear data with high interpretability.
- Support Vector Machines: SVMs provide a robust margin-based framework for classification and regression tasks.
- Neural Networks: Neural networks, their architecture, activation functions, and how they learn to mimic complex patterns are crucial ML algorithms.
Model Deployment and Evaluation
The next step in self-taught machine learning should include model deployment and evaluation. Putting a machine learning model into a real-world application is critical to ensuring the model's practical utility and impact.
- Deployment Strategies: Understand the steps to transition a model from a development environment to production, ensuring it integrates smoothly with existing systems.
- Performance Metrics: Depending on the problem's nature, learn to apply and interpret key metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
- Cross-Validation: Utilize cross-validation techniques to assess model stability and generalizability across different data sets.
- Model Monitoring and Updating: Recognize the importance of continuously monitoring a deployed model for performance drift and implementing updates to maintain accuracy over time.
How to Learn Machine Learning: Roadmap
Now that we've outlined the fundamental and specific skills needed for machine learning, let's explore some potential learning paths.
Remember, these paths might not be an exact fit for you. Feel free to tailor them based on your experience and the skill breakdowns provided earlier. Depending on your prior knowledge of these concepts and techniques, you can begin your learning path at a beginner, intermediate, or advanced level.
Machine Learning for Beginners
Are you new to machine learning concepts? If so, these checkbox lists will help you grasp the basics quickly. While the descriptions are not exhaustive, they are designed to represent the most used concepts, techniques, and approaches for beginners in machine learning.
Beginner Machine Learning Concepts
☐ Definition and scope of supervised, unsupervised, and reinforcement learning
☐ Tasks under supervised learning, such as classification, regression, and forecasting
☐ Tasks under unsupervised learning, such as clustering, dimensionality reduction, and anomaly detection
☐ Regression models, including linear regression, multiple linear regression, and polynomial regression
☐ Classification models like nearest neighbor (KNN), logistic regression classifiers, decision trees, and support vector machines (SVMs)
☐ Clustering algorithms like k-means iterative clustering and hierarchical clustering
☐ Data pre-processing techniques such as splitting data into training, test, and validation sets, handling noisy data, missing values, categorical attributes, and scaling numerical attributes
☐ Feature engineering techniques
☐ Evaluation and metrics for machine learning models
Hands-on Practice
Supplement your understanding of beginner machine learning concepts with hands-on programming experience.
☐ Discover the scikit-learn library—a powerful resource for machine learning in Python.
☐ Get acquainted with supportive Python libraries, such as pandas for data processing and Seaborn and Matplotlib for data visualization.
☐ If R is your preferred language, become familiar with valuable packages like dplyr, caret, and vip.
☐ Learn to use functions for pre-processing datasets, training models, making predictions, and evaluating performance.
Intermediate Machine Learning Path
For those acquainted with basic machine learning models, the next logical step in learning machine learning is to explore more complex algorithms. Understanding how these models are trained by investigating their optimization processes is vital. Experimenting with strategies to construct optimally performing models is also crucial.
Intermediate Machine Learning Concepts
☐ Explore ensemble methods for classification and regression, such as random forests, XG-Boost, and gradient boosting.
☐ Begin to learn about artificial neural networks, their related concepts, and deep learning architectures based on feed-forward neural networks with multiple hidden layers.
☐ Discover typical intermediate machine learning applications, like personalized recommender systems.
☐ Understand the mathematical foundations and the training process behind most supervised and unsupervised learning approaches.
☐ Familiarize yourself with optimization methods like gradient descent and convex optimization that guide model training in many machine learning approaches.
☐ Compare and benchmark different algorithms and trained models for a specific problem and dataset.
☐ Comprehend such common problems as overfitting faced by machine learning models and strategies to prevent or mitigate them for different types of models.
☐ Grasp the core concepts of Machine Learning Operations (MLOps) to build end-to-end machine learning workflows from data ingestion to deployment and maintenance.
Hands-on Practice
Enhance your understanding of intermediate machine learning concepts through hands-on programming experience—a crucial part of how to learn machine learning.
☐ Implement advanced machine learning solutions with more extensive, real-world datasets.
☐ Explore top deep learning frameworks and libraries like PyTorch and TensorFlow.
☐ Experiment with hyperparameter tuning techniques for machine learning models to find optimal configurations, including grid and random search.
☐ Begin with open-source MLOps tools like MLflow or Streamlit.
☐ Exploring cloud-based machine learning development tools from providers like Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure.
Advanced Machine Learning Path
Now, it's time to specialize in emerging trends and advanced areas of machine learning. These include computer vision and natural language processing techniques, reinforcement learning, generative AI breakthroughs, and the latest research trends.
Students and professionals undertaking advanced study of machine learning should fully incorporate MLOps methodologies in their practices. They should also utilize cloud providers' broad capabilities to implement and operationalize their machine-learning solutions successfully.
Advanced Machine Learning Concepts
☐ Understand the principles of computer vision and Natural Language Processing (NLP), the tasks they can handle, and how they intersect with machine learning and deep learning.
☐ Study advanced deep learning architectures, such as convolutional and recurrent neural networks, GANs, variational autoencoders, and transformers for computer vision, NLP, and content generation.
☐ Understand transformer architectures and attention mechanisms in Large Language Models (LLMs), including models like BERT and GPT, and the distinction between pre-training and fine-tuning.
☐ Comprehend the concept of transfer learning and how it adapts existing pre-trained models to new problems and use cases without sufficient labeled training data.
☐ Study reinforcement learning's design, teaching agents to solve tasks from experience. Examine its use in optimizing the behavior of models like ChatGPT via RLHF.
☐ Enhance your knowledge of LLMs by familiarizing yourself with Retrieval Augmented Generation (RAG), which optimizes LLM outputs by incorporating information retrieval mechanisms.
☐ Read recent scientific papers on platforms like arXiv to stay updated with the latest research trends.
☐ Get well-acquainted with the MLOps lifecycle, covering the maintenance and monitoring of deployed systems and machine learning system design principles.
Hands-on Practice
Through hands-on programming experience, improve your advanced command of practical solution development—a critical step in machine learning.
☐ Implement deep learning architectures from scratch using TensorFlow or PyTorch.
☐ Build your own transformer architecture or load different transformer-based LLMs from repositories such as Hugging Face.
☐ Familiarize yourself with Langchain and similar tools to develop tailored LLMs for specific use cases.
☐ Explore cloud providers and open-source tools to implement a complete MLOps lifecycle at the enterprise level and research the operationalization of ML systems.
☐ Evaluate frameworks for reinforcement learning algorithm experimentation, such as OpenAI Gym.
Where to Start with Machine Learning: Resources and Next Steps
Whether your goal is mastering machine learning concepts or becoming proficient in machine learning technology, there are specific skills and knowledge to acquire at each level.
Below, we've provided links to various learning resources and machine learning training to support your learning journey in this exciting field.
These resources come in diverse forms and formats—allowing you to choose those that best suit your learning style.
Course Notes
Machine Learning with K-Nearest Neighbors
Machine Learning with Decision Trees and Random Forests
Flashcards
Infographics
World of Supervised Machine Learning
Coding Templates
K-means clustering of numerical data with sklearn in Python
Train and test split with sklearn in Python
Removing missing values with pandas in Python
Simple linear regression with sklearn in Python
Multiple linear regression with sklearn in Python
TensorBoard – confusion matrix in Python
Practice Exams
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How to Deploy Machine Learning Models with Python & Streamlit
Top Machine Learning Interview Questions and Answers (2024)
Best Public Datasets for Machine Learning in 2024
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YouTube Videos
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Machine Learning explained in 5 minutes
Start Your ML Learning Journey
The journey to mastering machine learning is challenging but rewarding. You can become proficient in this dynamic field with the right resources, dedication, and continuous practice.
Remember, every expert was once a beginner, so don't hesitate to start your journey today.
The world of machine learning is a supportive community that continually learns and grows together.
Our 365 Data Science platform provides a range of courses on fundamental and advanced topics referenced in this article—including certificates and projects to help showcase your ML skills to potential employers. Additionally, our forum enables you to engage with peers who are learning machine learning from scratch—offering mutual support throughout your learning experiences.
Refer to this article for information on how to learn machine learning and explore the potential of this expanding field.
FAQs