How to Become a Machine Learning Expert in 9 Months

Learning machine learning is critical because it opens the door to developing cutting-edge applications in cybersecurity, facial recognition, and other fields.

Stepping into the world of machine learning as a newbie programmer can be a bit challenging, if not overwhelming. Especially, if you have no idea where to start, how to proceed, and what is important. This article aims to guide you through the process of becoming a Machine Learning Expert in approximately 9 months.  

We will give you an understanding of the level of expertise you need to build at each stage of the learning process. But mind you, the transformation is not going to be easy. This field demands hard work, dedication, and hours of study and practice. For all that, machine learning is very fascinating and cool, and you will find plenty of help along the way to enjoy the ride to become machine learning expert!

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We will be covering the following topics: 

1. Understand the Basics 
2. Learn (or re-learn) Statistics and Probability 
3. Build your Programming Skills in Python 
4. Learn how to perform Exploratory Data Analysis 
5. Build Supervised Machine Learning Models 
6. Build Unsupervised Machine Learning Models 
7. Explore Deep Learning Models 
8. Get help from the right resources 
9. Undertake a Machine Learning Project 

Step 1 – Understand the Basics 

Before you delve into machine learning, you need to familiarize yourself with the basics of AIML (Artificial Intelligence & Machine Learning). You might already have some idea about the field, but a little preparation before the big game never hurts! So, you need to take out a week or two to understand the concepts to the point where you can explain them to the next person. 

Topics you need to cover: 

1. What is Artificial Intelligence? 
2. What is Machine Learning? 
3. What is Deep Learning? 
4. How are the above terms related to each other? 
5. Applications of machine learning in the real-world 
6. How is machine learning transforming businesses? 
7. When will machines take over the world? (Just kidding) 

Step 2 – Learn (or re-learn) Statistics and Probability  

Building a statistical foundation and understanding probability is a must if you wish to be able to do anything with machine learning – mathematics and programming are both extremely important for this domain. 

I understand that mathematics isn’t everyone’s favorite subject. In fact, many of us struggle with both math and statistics. However, you need not worry because nobody is asking you to become a mathematician or statistician here. All you need to do is dedicate around two weeks to understanding certain important concepts. This will help you get an idea about when and how they may be applied. Kudos if you can completely understand the theory behind these concepts. 

Topics you need to cover: 

1. What is Sampling? 
2. Basics of Probability 
3. Types of Probability 
4. Bayes’ Theorem 
5. Random Variables 
6. Probability Distributions 
7. Descriptive Statistics 
8. Inference for categorical and numerical data 
9. Hypothesis Testing 

Step 3 – Build your Programming Skills in Python 

Programming is the number 1 skill you must focus on to become an expert in this domain as it will influence everything you will work on. Unlike popular opinion, programming is extremely easy and fun to learn. While mastering any programming language can be an endless quest, you can gain enough expertise in the most useful skills and libraries to write well-structured codes if you put in a few hours every day for at least six weeks.  

Python and R are the most popular languages for machine learning, with Python clearly being the dominant choice for most ML jobs. Python offers a sea of libraries and packages to make your life easier. The minimum you can get away with is knowing the following: 

1. NumPy and Pandas – Python’s data analytics libraries. 
2. Matplotlib and Seaborn – Python’s data visualization libraries 
3. Scikit-learn and SciPy – Python’s libraries for Machine Learning  

Topics you need to cover: 

1. How to extract data from data sources using Python 
2. How to read and write files using Pandas 
3. Data Structures in Pandas 
4. Data preparation and cleaning using Pandas 
5. Data pre-processing using Pandas 
6. Data transformation using Pandas 
7. Array manipulation using NumPy 
8. Data Visualization using Matplotlib 
9. Data Visualization using Seaborn 

Step 4 – Learn how to perform Exploratory Data Analysis  

Exploratory Data Analysis (EDA) is about studying the data and making it speak to us. What this means is that all (relevant) data conveys information if only we know how to extract it. EDA is an approach for data analysis that employs a variety of techniques to gain insight into the data, uncover the underlying story, detect outliers and anomalies, etc.   

This is a fundamental step after data collection and pre-processing. It’s also arguably the most interesting part of the entire learning process and takes a couple of weeks to learn. 

Topics you need to cover: 

1. Single variable non-graphical EDA 
2. Single variable graphical EDA 
3. Multi-variate non-graphical EDA 
4. Multi-variate graphical EDA 
5. Different types of graphs and charts 
6. Visualizing data and storytelling using Tableau 

Step 5 – Build Supervised Machine Learning Models 

1. Could you predict if the patient might get diabetes based on their genes and lifestyle? 
2. How can you filter your spam e-mails? 
3. How does speech recognition work? 
4. Could you predict employee attrition at the workplace? 

These are a few problems that can be solved using supervised learning techniques. These techniques have prior knowledge of how the predicted output values should be. A supervised model is trained on known data and then used to make predictions when new data is fed into it. 

Supervised learning techniques can be of two types – Classification and Regression. All supervised algorithms belong to either of these two categories. You will need to dedicate multiple weeks to get through with all the important algorithms – how to build models, evaluate, and optimize them to predict the most accurate outcomes. 

Topics you need to cover: 

1. Supervised learning techniques 
2. What is Classification? 
3. What is Regression? 
4. Supervised learning algorithms
   • Linear Regression 
   • Logistic Regression 
   • K-Nearest Neighbours (KNN) 
   • Support Vector Machines (SVM) 
   • Naïve Bayes Classifier  
   • Decision Tree Classifier  
   • Decision Tree Regressor 
   • Random Forest Classifier 
   • Random Forest Regressor 
5. Ensemble models:
   • Bagging
   • Boosting

Step 6 – Build Unsupervised Machine Learning Models 

Unsupervised learning techniques work with data that isn’t explicitly labeled. Such models are suitable for machine learning problems that require an algorithm to identify some sort of underlying structure in the data.  

Unsupervised learning methods are commonly used during dimensionality reduction and data mining. Here too, you will need to dedicate multiple weeks to get through with Clustering and Association techniques – how to build models, evaluate, and optimize them to make good predictions/decisions. 

Topics you need to cover: 

1. Unsupervised learning algorithms 
2. What is Clustering?
   • K-means clustering 
   • KNN (k-nearest neighbors) 
   • Hierarchal clustering 
3. Association Rule Mining
   • Apriori algorithm 
4. Dimensionality Reduction
   • Principle Component Analysis (PCA) 
   • Linear Discriminant Analysis (LDA) 
5. Recommender Systems 

Step 7 – Explore Deep Learning Models 

Deep Learning is a subset of machine learning. Deep learning algorithms are inspired by the structure and functions of the human brain. Hence, they are called Artificial Neural Networks. These models have helped companies such as Apple and Amazon to create digital-voice assistants – Siri and Alexa. With the help of deep learning, machines can basically be taught how to listen, read, write, and speak. This alone can give you an idea of the impact this domain has in the real world. 

When you’re getting started with Deep Learning, you’ll create basic models that might differentiate lizards from alligators. Along the way, you will start seeing the path to creating much more complex models someday. 

Topics you need to cover to become machine learning expert 

1. What are Artificial Neural Networks? 
2. Natural Language Processing (NLP) 
3. Convolutional Neural Networks  
4. Keras and TensorFlow 
5. Open CV 

Step 8 – Get help from the right resources 

One of the best ways to learn the technical aspects of a domain is through first-hand experience from working on actual projects. This project-based learning will enable you to identify different features of your machine learning problem. You will be able to better comprehend all the steps in the pipeline.  

To help you take a project-based learning approach, many tutorials and online courses can provide you with the guidance you need.  These will also help you manage your timelines for learning the said technology. 

You can easily find tutorial videos on YouTube for each and every topic you wish to learn. I am listing down a few popular courses that you can enroll yourself in if you wish to learn from industry experts themselves in live and/or recorded  classes: 

1. Machine Learning Course offered by Coursera and Stanford University.  
2. Introduction to Machine Learning Course offered by Coursera and Duke University. 
3. Post Graduate Program in Artificial Intelligence & Machine Learning offered by Edureka and E & ICT Academy, NIT Warangal.  
4. Machine Learning Course Masters Program offered by Edureka. 

Step 9 – Undertake a Machine Learning Project 

Towards the end of your machine ‘learning’ journey, you’ll be almost ready to showcase the skills you have gained during 8-9 months. And what better way to do that than to create your own machine learning model, right?    

The internet will be your go-to for finding amazing projects for you to work on. You can find a plethora of datasets on platforms like Kaggle that also provide notebooks to enable you to run your machine learning codes seamlessly.  

Completing this project would help you demonstrate your knowledge and skills. If you have practiced your concepts diligently for the last few months, this step is going to be the most fun! 

Tasks you need to perform in your project to become machine learning expert. 

1. Data collection and preparation 
2. Data pre-processing 
3. Exploratory data analysis 
4. Build an ML model 
5. Evaluate and optimize your model 
6. Predict the best possible outcome 
7. Create a project report 

Endnotes 

Hope this article helped you find the ropes to begin your Machine Learning expert journey. AIML is an increasingly growing domain that has hugely impacted big businesses worldwide. Interested in being a part of this frenzy? Explore these related articles:

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