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How to Learn Machine Learning
Table of Contents
Data Science and Machine Learning are two technologies that we never get tired of. Almost everyone knows that both are highly paid fields that offer a challenging and creative environment full of opportunities. Data science projects use Machine learning, a branch of Artificial Intelligence, to solve complex business problems and identify patterns in the data, based on which critical business decisions are taken.
Machine learning involves working with algorithms for classification or regression tasks. Machine learning algorithms are categorized into three main types, i.e., supervised, unsupervised, and reinforcement learning. Learn more about Machine learning types.
Machine learning will open you to a world of learning opportunities. As a machine learning engineer, you would be able to work on various tools and techniques, programming languages like Python/R/Java, etc., data structures and algorithms, and help you develop your skills for becoming a data scientist.
If you are a pro at math, statistics and love solving different technical and analytical problems, machine learning will be a rewarding career choice for you. Advanced machine learning roles involve knowledge of robotics, artificial intelligence, and deep learning as well.
As per Glassdoor, a Machine Learning engineer earns about $114k per year. Companies like Facebook, Google, Kensho Technologies, Bloomberg, etc., pay about 150k or more to ML engineers. It is a lucrative career, and there is never a shortage of demand for ML engineers, making it an excellent choice if you have the necessary skills. We will share all that is required for you to start your ML journey today!
To learn machine learning, you should know some fundamental concepts like:
- Computer Science Basics: ML is an entirely computer-related job, so you should know the basics of computer science
- Data Structure: ML algorithms heavily use data structures like Binary trees, arrays, linked lists, Sets, etc. Whether you use existing algorithms or create new ones, you will undoubtedly need data structure knowledge.
- Statistics and Probability: Classification and regression algorithms are all based on statistics and probability. To understand how these algorithms work, you should have a good grasp of statistics and probability. As a machine learning engineer, you must possess skills to analyze data using statistical methods and techniques to find insights and data patterns.
- Programming Knowledge: Most ML engineers need to know the basics of programming like variables, functions, data types, conditional statements, loops, etc. You need not particularly know R or Python; just knowing the basics of any programming language should be good enough.
- Working with Graphs: Familiarity in working with graphs will help you visualize machine learning algorithms' results and compare different algorithms to obtain the best results.
Integrated Development Environment (IDE)
The most preferred languages for machine learning and data science are Python & R. Both have rich libraries for computation and visualization. Some top IDE, including an online IDE, are:
- Amazon SageMaker: You can quickly build high-quality machine learning models using the SageMaker tool. You can perform a host of tasks, including data preparation, autoML, tuning, hosting, etc. It also supports ML frameworks like PyTorch, TensorFlow, mxnet.
- RStudio: If you like the R programming language, RStudio will be your best buddy for writing ML code. It is interactive, contains rich libraries, supports code completion, smart indentation, syntax highlighting, and most importantly, is free and easy to learn. RStudio supports Git and Apache Subversion.
- PyCharm: PyCharm is considered one of the best IDE platforms for Python. PyCharm comes with a host of profiling tools, code completion, error detection, debugging, test running, and much more. You can also integrate it with Git, SVN, and other major version control systems.
- Kaggle (Online IDE): Kaggle is an online environment by Google that requires no installation or setup. Kaggle supports both Python and R and has over 50k public datasets to work on. Kaggle has a huge community and provides 4 lakh public notebooks through which you can perform any analytics.
How to Learn Machine Learning
Machine learning is not just about theoretical knowledge. You have to know the basic concepts and then start working! But it is very vast and has a lot of fundamental concepts to learn. You should possess many statistics, probability, math, computer science, and data structures for programming language and algorithm knowledge.
Worry not. We will guide you to the best courses and tutorials to learn machine learning!
Here are the top 5 tutorials:
A-Z covers all about algorithms in both Python and R and is designed by data science experts. Udemy offers good discounts, especially during festive seasons, and you should look for the same. You will learn to create different machine learning models and understand more profound concepts like Natural Language Processing (NLP), Reinforcement Learning, and Deep Learning. The course focuses on technical and business aspects of machine learning to provide a wholesome experience.
An introductory course to Machine learning where you should be familiar with Python, probability, and statistics. It covers data cleaning, supervised models, deep learning, and unsupervised models. You will get mentor support and take up real-world projects with industry experts. This is a 3-month paid course.
ML Crash course by Google is a free self-study course covering a host of video lectures, case studies, and practical exercises. You can check interactive visualizations of the algorithms you learn as you learn. You will also learn TensorFlow API. You should know the basic math concepts like linear algebra, trigonometry, statistics, Python, and probability to enter this course. Before taking up this course, check out the complete prerequisites where Google also suggests other courses if you are a complete beginner.
It is an intermediate level course that takes about 7 months to complete. Coursera provides a flexible learning schedule. The specialization contains 4 courses, including machine learning foundations, regression, classification, and clustering and retrieval. Each course is detailed and provides project experience as well. You should know programming in at least one language and know basic math and statistics concepts.
A very beautifully explained introductory course by Manning, this basic course takes up concepts of classification, regression, ensemble learning, and neural networks. It follows a practical approach to build and deploy Python-based machine learning models, and the complexity of topics and projects increases slowly with each chapter.
The video series by Josh Gordon is a step by step approach and gives you a hands-on introduction to machine learning and its types. It is freely available on YouTube so that you can pace your learning as per your suitable timings.
Machine learning is best performed using R and Python. Read more about the packages and APIs of both from the below official documentation page:
Machine Learning Projects
Projects provide a wholesome learning experience and the necessary exposure to the real-world use cases. Machine learning projects are a great way to apply your learning practically. The important part is that there are no limitations to the number of use-cases you can take up, as data is prevalent in every domain. You can take everyday situations to create project ideas and build insights over them. For example, how many people in a community are more likely to visit a clothing stall over the weekend vs. weekdays, how many people might be interested in community gardening in the society, or whether an in-house food business will run for a long time in a particular gated community. You can try more exciting machine learning projects from our list of Machine Learning Projects.
Machine Learning Certifications
Learning machine learning with practice and projects is different from what you will be doing in the workplace. To practically experience real-time use cases and know the latest in the industry, you need to go for certifications to be on par with others of the same experience. Our comprehensive list of Machine learning Certifications will undoubtedly help you choose the right certifications for your level.
Machine Learning Interview Questions
As a final step to get the right job, you have to know what is frequently asked in interviews. After a thorough practice, projects, certifications, etc., you should know the answers to most questions; however, interviewers look for to-the-point answers and the right technical jargon. Through our set of frequently asked Machine learning interview questions, you can prepare for interviews effortlessly. Here are some of the questions, and for the complete list, check the link above.
- Define three stages of building a model in Machine Learning.
- Differentiate between Type I and Type II error.
- Explain Generalization, Overfitted, and Underfitted
- State the difference between classification and regression
- What is a probabilistic graphical model?
- How is KNN different from k-means clustering?
- Explain how a ROC curve works.
- How Do You Handle Missing or Corrupted Data in a Dataset?
- What does the term decision boundary mean?
- What is the usage of the F1 score?
To sum up, here is what we have covered about how to learn machine learning:
- Machine learning is a branch of AI used by data science to solve complex business problems.
- One must possess a strong technical background to enter machine learning, which is the most popular IT and data science industry.
- Machine learning engineers have a brilliant future scope and will have critical roles in shaping the future of data science and AI
- To learn Machine learning, you should be familiar with data structures, programming language, statistics, probability, different types of graphs, and plots.
- There are many online courses (free and paid) to learn machine learning from basic to advanced levels.
- There are many certifications, tutorials, and projects that you can take up to strengthen your skills.
- To apply for an interview, you should know the common questions and prepare your answers in a to-the-point and crisp manner. It is a good choice to read the commonly asked interview questions before going for the interview!
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