Is Machine Learning Hard to Learn- My Learning Journey [2024]

Do you want to learn Machine Learning but have a doubt Is Machine Learning Hard to Learn? If yes, fear not. I am here to guide you with the steps I took during my machine learning journey. I will also answer your question “Is Machine Learning Hard to Learn” based on my experience.

And I will not only try to answer your question but I will share the complete roadmap I took along with the resources I used while learning. So, give a few minutes to this article and understand everything related to the machine learning experience.

Now, without any further ado, let’s get started-

Is Machine Learning Hard to Learn

My Background

I studied Computer Science, but during my bachelor’s and master’s degrees, machine learning wasn’t really a big thing between 2012 and 2016. I only started learning about it when I began my Ph.D.

My Learning Journey

Before sharing my learning journey, I would like to mention the topics you need to learn in order to become a master in Machine Learning-

• Basic Math (Algebra, Calculus)
• Probability (Chance, Likelihood)
• Programming Skills (Python, R)
• Data Handling (Data Cleaning, Data Manipulation)
• Types of Learning (Supervised Learning, Unsupervised Learning)
• Testing Models (Model Evaluation, Validation)
• Improving Models (Parameter Tuning, Optimization)
• Spotting Mistakes (Error Analysis, Debugging)
• Making Projects (Real-world Applications, Practical Implementation)

I studied Computer Science, and luckily, I already knew how to program in Python, which made the programming part of machine learning easier for me. However, when it came to the math part, like algebra and calculus, I found it a bit tricky to understand.

I wasn’t too worried about statistics and probability though, as I found them easier to grasp. To learn more about machine learning, I decided to take some courses on Udemy and a specific course on ML algorithms. At first, I skipped over the math part and jumped straight into understanding ML algorithms with my Python skills. But soon enough, I hit a roadblock – some algorithms were tough to understand without knowing the math behind them.

Realizing my mistake, I took a step back and focused on learning the math. It took me some time and a lot of effort, and there were moments when I felt like giving up. But I kept going, and eventually, I got a better grasp of algebra and calculus.

Once I felt more confident in my math skills, I returned to learning ML algorithms. I decided to work on a sentiment analysis project, which sounded interesting. However, I quickly realized that collecting and cleaning the data were major challenges for me. Working with clean data was manageable, but doing it myself proved to be tough.

Despite the challenges, I found the process of testing and improving the models fascinating. Overall, my journey into machine learning has had its ups and downs, with some parts being tougher than others. But with perseverance and dedication, I’ve been able to overcome obstacles and continue learning and growing in this field.

Mistakes I Made That I Wouldn’t Recommend

1. Skipping Math: It was a mistake to dive into machine learning without understanding the math behind it. It made grasping advanced concepts harder.
2. Underestimating Data Prep: I didn’t realize how important collecting and cleaning data was. Spending enough time on this step is crucial for reliable results.
3. Ignoring Struggles: When I felt overwhelmed, I should’ve sought help or taken breaks instead of pushing through.
4. Rushing Learning: Trying to hurry through tough concepts only led to frustration. Taking time to understand each step would’ve been better.

Simple Tips for Learning Algebra and Calculus in Machine Learning

1. Focus on Useful Concepts: Start by learning algebra and calculus concepts that are commonly used in machine learning. Things like understanding patterns in data and how to manipulate them.
2. Try it Out with Machine Learning: Instead of just studying algebra and calculus on their own, apply these concepts to machine learning problems. See how you can use them to improve algorithms that learn from data.
3. Practice with Online Courses: Look for online classes that teach algebra and calculus with machine learning examples. Websites like Coursera or Khan Academy offer courses that explain these ideas in simple terms.
4. Use Machine Learning Tools: Get hands-on experience by using tools like TensorFlow or scikit-learn. These tools hide some of the complicated math, but understanding algebra and calculus will help you use them more effectively.
5. Work on Simple Projects: Start with small machine learning projects and gradually increase their complexity. This way, you can see how algebra and calculus apply to real-world problems.
6. Read Beginner-Friendly Books: Look for books that explain algebra and calculus in a way that’s easy to understand. Books like “Pattern Recognition and Machine Learning” or “Deep Learning” are good options.

By following these suggestions, you can gradually build your understanding of algebra and calculus for machine learning without feeling overwhelmed. Remember to take it one step at a time and practice regularly to improve your skills.

Practical Tips for Easier Data Prep in Machine Learning

1. Understand Your Data Sources: Before you start cleaning and labeling your data, it’s crucial to know where your data comes from, how it’s collected, and if there are any biases or inconsistencies.
2. Deal with Missing Values: When you spot missing values in your dataset, don’t panic! Use tools like pandas in Python to find and handle them. You can either fill in missing values with estimates or delete rows or columns with missing data.
3. Keep Data Formats Consistent: Make sure all your data follows the same format. This means converting dates to the same format and normalizing numerical values to make them easier to work with.
4. Remove Duplicate Entries: Duplicate entries can mess up your analysis, so it’s essential to detect and remove them. Tools like pandas or SQL queries can help you find and delete duplicate entries based on specific criteria.
5. Handle Categorical Data: If you have categorical data, like names or categories, you’ll need to convert them into numerical representations. Techniques like one-hot encoding or label encoding can help with this.
6. Spot Outliers: Outliers are data points that are significantly different from the rest. Use statistical methods or visualizations to identify outliers and decide whether to remove them or adjust them based on your analysis needs.
7. Check Data Quality Regularly: Throughout the data cleaning process, make sure to regularly check the quality of your data. Use descriptive statistics, visualizations, or domain-specific checks to ensure your data is reliable.
8. Automate Cleaning Tasks: Don’t waste time doing repetitive cleaning tasks manually. Leverage automation tools and scripts, like those found in Python libraries like pandas, scikit-learn, and TensorFlow, to streamline the process.

Helpful Learning Resources In Machine Learning

Programming Language (Python & R)

1. Introduction to Python Programming– Udacity
2. Python for Everybody– University of Michigan
3. Introduction To Python Programming– Udemy
4. Python Core and Advanced– Udemy
5. Crash Course on Python– Google
6. Python for Absolute Beginners!– Udemy
7. Python 3 Programming Specialization– University of Michigan
8. R Programming – Johns Hopkins University
9. Programming for Data Science with R– Udacity
10. R Programming A-Z™– Udemy

Mathematics

1. Mathematics for Machine Learning Specialization– Imperial College London
2. Mathematics for Data Science Specialization– Coursera
3. Data Science Math Skills– Duke University
4. Intro to Statistics– Udacity
5. Probability – The Science of Uncertainty and Data– MITx
6. Basic Statistics– University of Amsterdam
7. Probabilistic Graphical Models Specialization– Stanford University
8. Introduction to Calculus– The University of Sydney
9. Probability and Statistics– University of London

Machine Learning Algorithms

1. Become a Machine Learning Engineer (Udacity)
2. Machine Learning– Stanford University
3. Machine Learning with Python– IBM
4. Intro to Machine Learning with TensorFlow (Udacity)
5. Machine Learning A-Z™: Hands-On Python & R In Data Science -Udemy
6. Python for Data Science and Machine Learning Bootcamp– Udemy
7. Advanced Machine Learning Specialization– Coursera

TensorFlow

1. TensorFlow in Practice Specialization– deeplearning.ai
2. Intro to Machine Learning with TensorFlow– Udacity
3. Tensorflow 2.0: Deep Learning and Artificial Intelligence– Udemy
4. TensorFlow: Data and Deployment Specialization– deeplearning.ai
5. Machine Learning with TensorFlow on Google Cloud Platform Specialization– Google Cloud

Data Preprocessing

1. Applied Data Science with Python Specialization at the University of Michigan
2. Exploratory Data Analysis With Python and Pandas (Guided Project)
3. NumPy Tutorial by freeCodeCamp

Deep Learning

1. Deep Learning (Udacity)
2. Deep Learning Specialization (deeplearning.ai)
3. Deep Learning A-Z™: Hands-On Artificial Neural Networks– Udemy

Is Machine Learning Hard to Learn

Yes, learning machine learning is hard. Even with some background in Computer Science and knowing Python, understanding complex math like algebra and calculus was tough. But with effort, I managed to grasp the concepts and make progress. It wasn’t easy, but the rewards of gaining new skills made it worth it. Overall, while machine learning can be challenging, with determination, anyone can learn and succeed in this field.

My Suggested Roadmap

1. Start with the Basics:

• Pick Your Language: Begin by learning either Python or R, two common languages in data science and machine learning.
  • Avoid Mistakes: Don’t try to learn both at once. Focus on mastering one before moving on.

2. Understand the Fundamentals:

• Math Matters: Get a solid grasp of basic math concepts like algebra, calculus, statistics, and probability.
  • Avoid Mistakes: Don’t overlook the importance of math. It’s crucial for understanding data science and machine learning.

3. Learn Machine Learning:

• Learn Algorithms: Explore various machine learning algorithms and their applications.
  • Avoid Mistakes: Don’t rush through learning algorithms without understanding the basics.

4. Master TensorFlow and Deep Learning:

• Explore TensorFlow: Deepen your understanding of deep learning frameworks like TensorFlow.
  • Avoid Mistakes: Don’t jump into deep learning without grasping the basics of machine learning and TensorFlow.

5. Clean and Prep Data:

• Preprocess Data: Learn how to clean and prepare raw data for machine learning models.
  • Avoid Mistakes: Don’t underestimate the importance of data preprocessing. Poor-quality data leads to inaccurate models.

6. Work on Projects:

• Apply Your Knowledge: Gain practical experience by working on real-world projects and challenges.
  • Avoid Mistakes: Don’t avoid projects because they seem daunting. Projects solidify your understanding and build your portfolio.

Conclusion

So, I have shared everything related to my Machine Learning journey with you. I hope it will help you and clear your doubts about “Is Machine Learning Hard to Learn?“. If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

Thank YOU!

Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford