Remember all those goals you set at the start of the year? Chances are, there are at least a couple of those resolutions that you haven’t gotten around to. And time is running out. On September 29th the countdown begins: it’s day number 1 of the last 100 days of the year. What will you do to make this 2018 worthy? For me, this year was the best so far in my life. Why? Because I found my purpose, and that is helping people scale their business with data science and overall helping people live better by providing insights into data science.
I can say now, that I am a data scientist who delivers innovative and actionable data science solutions in business. I believe that information gleaned by data can create fascinating stories and offer invaluable guidance in making strategic business decisions. As may you know, I succeed to learn data science in 6 months. Now after 9 months, and 100 days left in this year I want to achieve more. I want to make this 2018 legendary and to prepare for big things coming in 2019. So, my next big challenge is to learn Machine Learning in 100 days. The countdown begins today. I will write about my journey every Friday. Also If you want to join me, please do.
I will now give you my roadmap for this rest 100 days. So let’s go guys.
1. Machine Learning A-Z™: Hands-On Python & R In Data Science
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.www.udemy.com
I am already halfway through, so I will finish this amazing course by Kirill Eremenko and Hadelin de Ponteves by the second week of October. I was learning this course from August. All that I learned so far in data science were from those two amazing guys. They explain everything simply and give a lot of practical examples. So I will continue to learn from them. I am so amazed that I have the opportunity to learn from them and ask them everything on my path. I can say it needs time to get familiar with Ml and to be comfortable to use it in daily work. Every week I will practice ML algorithms with case studies on https://www.superdatascience.com and Kaggle. The goal is to finish by the end of year 12 workshops in Ml.
2. MACHINE LEARNING COURSE FROM STANFORD UNIVERSITY
Machine Learning from Stanford University. Machine learning is the science of getting computers to act without being…www.coursera.org
I am going through this course at the same time. It will take up to 7 weeks to finish it. I was looking at all Ml courses and I saw that professor Andrew is the best.
3.Machine Learning Practical
New great course by Kirill Eremenko and Super Data Science team.
This course goes after ML A-Z. As someone of you know, I decided to master Python programming language. I don’t know R, and I am not planning to learn it. My plan is to know Python and SQL as an expert. I realize less is more. If you learn one programing language really good, after that every other will be easy. The key is in domain knowledge. Plan after will be learning Julia and d3.js for data visualization.
Machine Learning – Solving Real-World Challenges with Pythonwww.udemy.com
4. Deep Learning A-Z™: Hands-On Artificial Neural Networks
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates…www.udemy.com
5. Deep Learning and NLP A-Z™: How to create a ChatBot
Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Pythonwww.udemy.com
6. Artificial Intelligence A-Z™: Learn How To Build An AI
Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World…www.udemy.com
7. Read Andrew NG book- Machine Learning Yearning
Free draft copy of Andrew Ng’s book – Machine Learning Yearning!www.mlyearning.org
8. Read book Confident Data Skills by Kirill Eremenko
Why added this other Deep learning courses and AI? I think it is crucial to know the basics of deep learning and AI. After doing a couple of courses in ML it is normal to go ahead. The best way to learn something is learning by doing. So these courses are mostly practical and I will practice it in combination with real-world workshops.
I will give lectures in Python for data science, Tableau, and ML. Why? I realize the best way to learn something is learning by doing and then teaching others, sharing your knowledge. I will make online tutorials for the Balkan market on Serbian language and I will make Data Science Intro (3 Saturdays, 18 hours learning) and it will be in Belgrade for everyone who wants to learn Data Science. The first Data Science Intro will be probably in mid of November. For more information about it follow me for new news. This way I will learn and do more, and I will share and teach others. People who are the best will get paid internship in my company. After that, they will get the job in my company and an opportunity to learn on daily basis from me. Why? I realize that you can make a lot when you have a small team of smart people.
I will write every week about my journey in ML and I will begin to write some technical blogs in Python and ML. I will also make two short how-to tutorials on mine YouTube channel.
The 100 DAYS ML CODE CHALLENGE BEGINS NOW
Until next time,
Happy coding in Ml,
Source: Deep Learning on Medium