diff --git a/README.md b/README.md
index f21566c..85339b0 100644
--- a/README.md
+++ b/README.md
@@ -4,22 +4,31 @@ A complete study plan to become a Machine Learning Engineer with links to all FR
-Watch the video on YouTube for instructions:
+Watch the video on YouTube for instructions: [Link to Youtube Video](https://www.youtube.com/watch?v=dYvt3vSJaQA)
[](https://www.youtube.com/watch?v=dYvt3vSJaQA)
-[https://www.youtube.com/watch?v=dYvt3vSJaQA](https://www.youtube.com/watch?v=dYvt3vSJaQA)
+
-#### IMPORTANT:
+
+
+
+## IMPORTANT:
- This list is not sponsored by any of the mentioned links! I did a lot of the courses myself and can highly recommend them!
- This list takes a lot of time and effort to finish if you want to do it properly! The list does not look that long, but don't underestimate it.
-#### How to use the Plan:
+
+
+## How to use the Plan:
- For theory lectures: Follow along, take notes, and repeat the notes afterwards.
- For practical lectures/courses: Follow along, take notes. If they provide exercises, do them!!! Do not just google the answer, but try to solve it yourself first!
- For coding tutorials: Code along, and after the video try to code it on your own again.
- Step 3 is critical! Your theoretical knowledge is worthless if you don't know how to apply it to real world problems! Do as many personal projects and competitions as you can! You don't have to wait with step 3 until you finished the other parts, I recommend starting with a side project or kaggle competition after you finished part 1.1 (Andrew Ng's course).
+
+
## The Plan
+
+
### 0. Prerequisites
- [ ] Linear Algebra and Multivariate Calculus
- [ ] [Khan Academy - Multivariable Calculus](https://www.khanacademy.org/math/multivariable-calculus)
@@ -34,10 +43,12 @@ Watch the video on YouTube for instructions:
- [ ] [Advanced Python - Playlist on YouTube (Python Engineer)](https://www.youtube.com/watch?v=QLTdOEn79Rc&list=PLqnslRFeH2UqLwzS0AwKDKLrpYBKzLBy2)
- [ ] [Numpy - Free Udemy Course](https://www.udemy.com/course/deep-learning-prerequisites-the-numpy-stack-in-python/)
- [ ] Matplotlib
- - [ ] [sentdex - Playlist on YouTube](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF) or
+ - [ ] [Sentdex - Playlist on YouTube](https://www.youtube.com/watch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF) OR
- [ ] [Corey Schafer - Playlist on Youtube](https://www.youtube.com/watch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_)
- [ ] [Pandas Tutorial - Playlist on Youtube (Corey Schafer)](https://www.youtube.com/watch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)
+
+
### 1. Basics Machine Learning
- [ ] [Coursera Free Course by Andrew Ng](https://www.coursera.org/learn/machine-learning)
- [ ] [Machine Learning Stanford Full Course on YouTube](https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN)
@@ -48,6 +59,8 @@ Watch the video on YouTube for instructions:
- [ ] [Python Machine Learning - Sebastian Raschka](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/ref=sr_1_1?crid=L7PEHL95RXH4&keywords=python+machine+learning&qid=1584648438&sprefix=python+ma%2Caps%2C230&sr=8-1)
- [ ] [Introduction to Machine Learning with Python - Andreas Müller](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/ref=sr_1_1?crid=WAQPG9CEM3W&keywords=introduction+to+machine+learning+with+python&qid=1584648523&sprefix=introduc%2Caps%2C238&sr=8-1)
+
+
### 2. Deep Learning
- [ ] [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)
- [ ] Learn PyTorch (or Tensorflow)
@@ -57,18 +70,24 @@ Watch the video on YouTube for instructions:
- [ ] [Practical Deep Learning for Coders Part 1](https://www.fast.ai/)
- [ ] [Part 2](https://course.fast.ai/part2)
-Optional:
+**Optional:**
- [ ] [Stanford Lecture - Natural Language Processing with Deep Learning](https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)
- [ ] [Stanford Lecture- Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)
+
+
### 3. Competitions and Own Projects
- [ ] [Kaggle](https://www.kaggle.com/)
- [ ] Datasets (develop own projects)
- [ ] Competitions (start with Getting started section)
- [ ] [8 Fun Machine Learning Projects For Beginners](https://elitedatascience.com/machine-learning-projects-for-beginners)
+
+
### 4. Prep for Interviews
-- [ ] https://github.com/alexeygrigorev/data-science-interviews
+- [ ] [Data Science Interviews by Alexey Grigorev](https://github.com/alexeygrigorev/data-science-interviews)
+
+
## Next Level
- Make your own projects to show what you have learned.
@@ -77,6 +96,8 @@ Optional:
- Contribute to ML/DL related open source projects (sklearn, pytorch, fastai, ...).
- Get into Kaggle competitions.
+
+
## Further readings
- [The cold start problem: how to break into machine learning](https://towardsdatascience.com/the-cold-start-problem-how-to-break-into-machine-learning-732ee9fedf1d) (Towardsdatascience)
- [How to Start Learning Machine Learning?](https://www.geeksforgeeks.org/how-to-start-learning-machine-learning/) (GeekforGeeks)
@@ -88,10 +109,12 @@ Optional:
- [Getting Started with Applied Machine Learning](https://machinelearningmastery.com/start-here/#getstarted) (Machine Learning Mastery)
-GitHub:
-- https://github.com/ZuzooVn/machine-learning-for-software-engineers
-- https://github.com/Avik-Jain/100-Days-Of-ML-Code
-- https://github.com/yanshengjia/ml-road
+**GitHub:**
+- [Machine Learning for Software Development](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
+- [100 Days of ML Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code)
+- [ML Road](https://github.com/yanshengjia/ml-road)
+
+
## Further resources added by the community
Contributions are welcome! If you can recommend any other resources, feel free to open a pull request :)