Update README.md

Author: FTUHQ

README.md

@@ -13,20 +13,25 @@ by skipping the Math.
 
   1. [Probability & Statistics](https://www.khanacademy.org/math/probability) 
      Basic Probability and Stats will be helpful in understanding ML algorithms like Naive Bayes. 
+     
   2. [Statistics 101 - Udacity](https://www.udacity.com/course/intro-to-statistics--st101)
-     Taught by the founder of GoogleX it's full of exercises in Python so you won't get bored. 
-  3. MIT 18.06 Linear Algebra
+     Taught by the founder of GoogleX it's full of exercises in Python so you won't get bored.
+     
+  3. [MIT 18.06 Linear Algebra](https://www.youtube.com/watch?v=ZK3O402wf1c&list=PLE7DDD91010BC51F8)
      Prof. Strang is terrific! Not only he'll make you fall in love in Linear Algebra but you'll learn
      important concepts like SVD and matrix algebra. You might wanna grab this [PDF](http://www.math.hcmus.edu.vn/~bxthang/Linear%20algebra%20and%20its%20applications.pdf)
      as well. Be sure to also solve the exam question papers from here: [link](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/exams/)
-  4. MIT Single Variable Calculus
-     This is my personal favorite book, use it for SVC + MVC [link](gdrive link)
+     
+  4. [MIT Single Variable Calculus](https://www.youtube.com/watch?v=7K1sB05pE0A&list=PL590CCC2BC5AF3BC1)
+     This is my personal favorite book, use it for SVC + MVC [link](https://drive.google.com/open?id=0BwEXorNDIEnFc3VKN3RUOWdRdUE)
      Amazing course but it gets quite tedious in the middle, you might wanna skim some geometry, but the key is
      to understand how optimization works. Be sure to solve questions from here: [link](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/exams/)
-  5. MIT Multi Variable Calculus
+     
+  5. [MIT Multi Variable Calculus](https://www.youtube.com/watch?v=PxCxlsl_YwY&list=PL4C4C8A7D06566F38)
      Understanding vector calculus is necessary for algorithms like SVM, you might wanna skim some parts
      which are purely theoretical. Be sure to solve questions from here: [link](https://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/exams/)
-  7. (Optional) Stanford Convex Optimization
+     
+  7. (Optional) [Stanford Convex Optimization](https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/about)
      WARNING: Do this course only if you're very good at math. Convex Optimization will teach you numerous
      functions used in Machine Learning. But this course is extremely heavy on Math!
 
@@ -44,33 +49,39 @@ by skipping the Math.
       2. [Algotithms Stanford II](http://online.stanford.edu/course/algorithms-design-and-analysis-part-2)
 
 ### Introduction to Machine Learning
-  1. Machine Learning by Andrew Ng
+  1. [Machine Learning by Andrew Ng](https://www.coursera.org/learn/machine-learning)
      A must do course, best course of Introduction to Machine Learning so far, light on Math and focuses more on concepts.
 
   Complete one out of two
-    1. Machine Learning A-Z
-       Introductory course on ML focusing on not only Python but also R, one of the best sellers on Udemy. 
-    2. Introduction to Machine Learning - Udacity
+    1. [Machine Learning A-Z](https://www.udemy.com/machinelearning/)
+       Introductory course on ML focusing on not only Python but also R, one of the best sellers on Udemy.
+       
+    2. [Introduction to Machine Learning - Udacity](https://www.udacity.com/course/intro-to-machine-learning--ud120)
        Sebastian Thrun does an awesome job explaining various approaches in ML. It gets a little boring in the middle
        but overall it's very good. 
 
 ### Applied Machine Learning
   Two quick courses on applying the theory you learnt. They're short so I recommend doing both of them. 
-    1. Python for Data Science and Machine Learning Bootcamp
-    2. Machine Learning with Python - Hands On!
+    1. [Python for Data Science and Machine Learning Bootcamp](https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/)
+    2. [Machine Learning with Python - Hands On!](https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/)
 .
 ### Specializations
 
   * Deep Learning
-    1. Neral Networks by Geofrrey Hinton
-       Warning: Heavy on Math
+    1. [Neural Networks by Geofrrey Hinton](https://www.coursera.org/learn/neural-networks)
+       This guy is the creator of backpropagation algorithm! Warning: very heavy on Math.
+       
     2. [MIT Introduction to Deep Learning](http://introtodeeplearning.com/index.html)
-    2. Deep Learning A-Z
+    
+    2. [Deep Learning A-Z](https://www.udemy.com/deeplearning/)
+    
     3. [Creative Applications of Deep Learning with TensorFlow](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info)
+   
+    4. Must read book on Deep Learning: [Free HTML book](http://www.deeplearningbook.org/)
 
   * Big Data & Large Scale Machine Learning
-    1. Introduction to Big Data https://www.coursera.org/learn/big-data-introduction
-    2. Spark and Python for Big Data with PySpark https://www.udemy.com/spark-and-python-for-big-data-with-pyspark/
+    1. [Introduction to Big Data](https://www.coursera.org/learn/big-data-introduction)
+    2. [Spark and Python for Big Data with PySpark](https://www.udemy.com/spark-and-python-for-big-data-with-pyspark/)
     3. [Distributed Machine Learning with Apache Spark](https://www.edx.org/course/distributed-machine-learning-apache-uc-berkeleyx-cs120x)
 
   * NLP
@@ -79,7 +90,7 @@ by skipping the Math.
 
   * Self Driving Car
     1. [MIT Deep Learning for Self Driving Cars](http://selfdrivingcars.mit.edu/)
-    2. [Self Driving Car Nano Degree] 
+    2. [Self Driving Car Nano Degree](https://in.udacity.com/course/self-driving-car-engineer-nanodegree--nd013/)
 
    * Scientific Computing
    1. [Scientific Computing](http://academictorrents.com/details/6f7e43052129b95f470d3043cfce2bf5c15ae380)