The Ultimate Path to Building a Great Career in Machine Learning

Machine Learning demand is rising day by day due to the high amounts of data being generated. We are also really fortunate to be at a time where there are lots of courses out there and even better most of them are freely available. However, this can do more harm than good as most of them skip the most important fundamentals hence you may end up wasting your time instead. How do you get started?

Getting Interested

This is a really demanding field and you have to develop perseverance. By Antoine de Saint-Exupery's words " If you want to build a ship, don't drum up the men to gather wood, divide the work and give orders. Instead, teach them to yearn for the vast and endless sea." In that regard, to get interested in the topic here is a talk by Laurence Moroney an AI lead at google. https://www.youtube.com/watch?v=VwVg9jCtqaU&t=1236s

Getting Started

Start by learning how to code. Python is the go to language in Machine Learning. This is a marathon and not a sprint hence just start small. You could take the Python course on Kaggle to get a taste of the python skills you'll need for Machine Learning. https://www.kaggle.com/learn/python. To get deeper you can take the Python for everybody by the University of Michigan at Coursera.https://www.coursera.org/specializations/python.

Now Get Started With Machine Learning

Machine Learning course taught by Dr Andrew Ng on Coursera https://www.coursera.org/learn/machine-learning is a great way to get you started with Machine Learning important concepts. Here you will learn machine learning techniques and gain practice implementing them via a rigorous set of programming assignments. The syllabus covers:

    1. Linear regression with one variable

    2. Linear Algebra Review

    3. Logistic Regression

    4. Neural Networks

    5. Machine Leaning System Design

    6. Support Vector Machines

    7. Unsupervised Learning

    8. Dimensionality Reduction

The only drawback is that the course is taught in Octave.

Making Progress Towards Human- level AI

In order to make progress towards human- level AI, you should now take the Deep Learning Specialization https://www.coursera.org/specializations/deep-learning. Again this is a really demanding specialization and requires you to commit quite some time but at the end of it it will be a really good use of your time. The specialization is divided into 5 courses which are:

    1. Neural Networks and Deep Learning

    2. Hyperparameter Tuning, Regularization and Optimization.

    3. Structuring Machine Learning projects

    4. Convolutional Neural Networks

    5. Sequence Models

Learning Implementation

By now, you have gained a lot of knowledge in Machine Learning. However, to implement the ideas learnt thus far you need to learn TensorFlow, an open source Deep Learning framework. There are two amazing courses taught by Lawrence Moroney on TensorFlow:

TensorFlow Developer Professional Certificate

Here you will learn: Best practices for TensorFlow, handling real-world image data, building natural language processing systems. This too will help you prepare for the Google TensorFlow Cerificate Exam. To learn more advanced concepts such as Object Detection and Neural Style Transfer you can take Tensorflow advanced techniques specialization.

Deploying your Models

To learn how you can make your models useful by putting them into people's hands by either deploying them into the web or onto devices you can take the TensorFlow Data and Deployment Specialization. This too will teach you how to handle data and build effective Machine Learning pipelines. You have really come a long way but sadly taking online courses is not the only thing needed to build a great career in Machine Learning as this is an ever evolving field. To keep up, read and implement Machine Learning papers and start working on real world problems that cover the entire Machine Learning  development cycle.