Learn Machine Learning in 9 Easy Steps..
1. Learn the Prerequisites
Before you can learn the theory and applications of machine learning, you’ll first need to understand its most essential concepts. This foundation will help you learn ML principles more quickly by creating a systematic learning path.
Programming
All machine learning algorithms are implemented with code. So programming skills in Python, R, Bash, or Java are a must for any aspiring ML engineer. In recent years, Python has emerged as the most popular programming language, especially for beginners. It has simple syntax, extensive built-in functions, the most-supported libraries, and wide package support.
Statistics and Probability
As machine learning engineers work almost exclusively with data and its predictions, both statistics and probability are fundamental topics in the ML field. All data-driven decisions are ultimately made based on the probabilities of compound events occurring. And descriptive statistics enable the transformation of raw data into meaningful information that is used to design and develop ML algorithms.
Mathematics
Linear algebra and calculus are both integral to machine learning. The algorithms used to manipulate datasets are built on the foundation of linear algebra, particularly vector spaces and matrix operations. Linear algebra also covers crucial concepts like linear transforms, tensor and tensor rank, notations, and matrix multiplication.
Data Tackling
Last but not least, you’ll need to gain experience working with large volumes of data. If you can handle data, you can greatly improve your code’s efficiency in terms of the memory and time consumed. Before you can design or select a model for data analysis, you’ll need to clean your data (data wrangling) and estimate the structure of the whole set.
2. Learn ML Theory From A to Z
Planning and Data Collection
When beginning any ML/AI project, start by planning for the type of events you are detecting and the metadata you will capture. The process generally starts by collecting a small amount of training data that can be used to validate the results in a proof of concept. Both continuous (status) events and discrete (trigger) events must be explicitly defined before data collection and validation can take place.
Data Assumptions
All machine learning models use certain assumptions to make predictions. Linear regression, logistic regression, decision tree, random forest, KNN (K-Nearest Neighbors), support vector machines (SVM), and all other ML models are based on some fundamental axioms or assumptions about the nature and behavior of the data. Data assumptions usually cover the relationship between the independent and dependent variables, their distribution, multicollinearity, autocorrelation, and outliers.
Data Preprocessing
Preprocessing, as the name suggests, is the process of formatting raw data to be processed by a machine learning model. It includes specific steps to transform or encode the data so that it can be easily parsed by the machine. Data preprocessing improves the accuracy and efficiency of the ML model by minimizing data noise, missing values, and system handling errors or unusable formats. Preprocessing steps also describe the appropriate splitting of the dataset into training and test sets.
Data Interpretation Along With Model Results
Model interpretation in machine learning seeks to understand the model’s decision-making process. It systematically evaluates the fairness, reliability, trust, and causality of the model results. Put simply, it helps answer questions like “How trustworthy are the model’s predictions?” and “Are they reliable enough to shape major policies and decisions?” The two main ways to interpret ML models are model-specific/model-agnostic methods and local/global scope methods.
Improving and Bettering Your Models
Improving the accuracy of the ML model is essential to the overall project’s success. The model’s predictions need to have a low error range and perform well with different problem variables. While some methods deal with cleaning and filtering the data more efficiently, others suggest using varied strategies to improve the performance of the model with new, unused data. Techniques like cross-validation, feature engineering, and algorithm/hyperparameter tuning are popular choices for boosting the model’s precision.
Contributing to Business Value
When you’re building machine learning models for a client or employer, the performance of the model directly equates to money spent. Decisions predicated on an inaccurate model can be very costly for organizations. Aside from the financial loss, a low-accuracy model can lead to devastating errors, like an incorrect medical diagnosis. For these reasons, it is crucial for ML engineers to understand, from a business perspective, the value of correct predictions and the cost of incorrect ones.
3. Deep Dive Into the Essential Topics
Once you’ve grasped the underlying concepts, you will start to understand the capabilities and reasoning behind machine learning models. The final step before you can start contributing to the ML/AI community is to gain an end-to-end understanding of the sequence of operations in data science. This will help you understand how the data, ML, and software code pipelines interact with each other to create a complete product.
Practice Machine Learning Workflow
Strictly speaking, the machine learning pipeline starts with clean, filtered data that has been optimized for ML parsing. In practice, ML engineers also spend a substantial amount of time wrangling data to ensure that incomplete or noisy data doesn’t damage the model’s accuracy. After the data has been appropriately split into training and test sets, the preliminary code for the model can be created. This includes model engineering, evaluation, and packaging.
4. Work on Projects
Working on your own projects is a must for any aspiring ML engineer. In the competitive ML marketplace, you need real-world experience to stand out from the sea of novice candidates. Employers look for professionals who have demonstrable first-hand experience with ML tools and applications. Taking up an independent project helps materialize your theoretical knowledge into a practical approach. The trial and error process of developing your project offers a great learning curve for beginners in the field.
Choose Based on Your Interest
While this might seem like an obvious or optional suggestion, you should select a topic you are truly interested in. When you start working on your first project, you are bound to make mistakes, especially if you’ve started from square one. These errors can become frustrating over time and you might not be able to make progress as quickly as you’d hoped. When this happens, it helps to have a topic that you genuinely care about.
5. Learn and Work With Different ML Tools
There’s an extraordinary variety of ML tools and packages available in the market today. From data collection and preparation to model building and application deployment, the possible combinations are practically infinite. As a beginner, your best bet is to stick with tried and tested software and packages with proven capabilities. Of course, the choice of tools will also depend on the kind of model and prediction you’re trying to create. But there is a whole range of verified tools that perform exceptionally well for the vast majority of use cases.
Some of the most popular ML/AI tools trusted by experienced professionals are:
- TensorFlow
- Auto-WEKA
- KNIME
- Google Cloud AutoML
- BigML
- Scikit-learn
- Azure Machine Learning Studio
- Amazon Machine Learning (AML)
- Accord.NET
6. Study ML Algorithms From Scratch
At this stage, you will have the expertise needed to grasp the overall implementation of ML algorithms. But you still need to dig into the workings of individual algorithms before you can truly master ML. The most thorough explanations will likely include highly advanced math. If you are not keen on math-intensive descriptions, you can stick to step-by-step tutorials written in Python, R, or any other programming language.
By the end of your study, you will understand the machine learning techniques used to load and prepare data, evaluate model skills, and implement a suite of linear, nonlinear, and ensemble algorithms.
You should ideally cover the complete spectrum of topics, such as data scaling, baseline models, algorithm test harness, linear regression, logistic regression, KNN, and bootstrap aggregation.
7. Opt For a Machine Learning Course
Following a structured program or course is one of the best ways to learn ML from scratch. High demand in the industry means that there are hundreds of in-person and online courses available. You can choose to take an ML/AI course at any stage of your learning process. Courses can help you gain momentum when you are first starting out, or help you hone specific skills in more advanced topics.
You should aim to select a course that has a state-of-the-art curriculum and focuses on in-demand skills. Other factors like community and mentor support and opportunities to work on capstone/portfolio projects should also be taken into consideration before making a decision.
8. Apply for an Internship
The last step before applying for ML jobs is to secure an internship. Employers and hiring managers always prefer candidates who have previously held an ML internship position. This is an opportunity to build connections through networking and get inside knowledge of the industry.
Try to apply for internships in the industry where you’d like to work. Indiscriminately applying for any and all open positions will only lead to more rejections. You should curate your professional resume and portfolio to the role you’re applying for. You can find ML internships by visiting dedicated websites like LetsIntern, Internshala, and AngelList, or by getting in touch with companies directly.
9. Secure a Job
Before you start sending out applications, take some time to go over the skills and qualifications mentioned in the job descriptions. This will give you a clear picture of what employers in the industry are looking for.
Make sure you can demonstrate your grasp of the theoretical and technical aspects of ML projects and big systems. Building a presence on community websites like Kaggle and GitHub will also help with your networking and as you design an appealing portfolio. If you have the expertise to convert a business problem to a machine learning system, you are ready to get in touch with ML recruiters!
Can You Learn Machine Learning on Your Own?
Absolutely. Although the long list of ML skills and tools can seem overwhelming, it’s definitely possible to self-learn ML. With the sheer amount of free and paid resources available online, you can develop a great understanding of machine learning all by yourself. Remember that going from linear algebra basics which is a mandatory requirement for ML Engineers and Data Scientists) all the way to deep learning algorithms will take a while, so keep going and you’ll have your dream ML/AI career in the end.
