Corrected Typo

README.md

@@ -6,7 +6,7 @@ This repository is a Python implementation of the different algorithms and probl
 The main idea is to provide a Python-based implementation in order to enable people who is not familiarized with R to play and learn Bayesian Statistics.
 
 <div  align="center">
-<img src="./assets/posterior.png" width="480" />
+<img src="./assets/posterior.png" width="640" />
 </div>
 
 **Note**: Quizes and projects are not shared in this repository, only examples given within the lectures. All the text, equations and explanations were directly taken from the course.
@@ -16,25 +16,25 @@ The main idea is to provide a Python-based implementation in order to enable peo
 The following table of content shows the different algorithms implemented in this repository.
 
  - [**Course 1, Bayesian Statistics: From Concept to Data Analysis**](https://github.com/MikeS96/bayes_statististics/tree/main/C1)  
-		 - **L4:** Plotting the Bernoully likelihood.
-		 - **L5:** Probability distributions in Python.
-		 - **L7:** Students problem with Bernoully and Binomial PMF.
-		 - **L12:** Frequentist Linear regression.
+	- **L4:** Plotting the Bernoully likelihood.
+	- **L5:** Probability distributions in Python.
+	- **L7:** Students problem with Bernoully and Binomial PMF.
+	- **L12:** Frequentist Linear regression.
  - [**Course 2, Bayesian Statistics: Techniques and Models**](https://github.com/MikeS96/bayes_statististics/tree/main/C2)
-		 - **L3A:** Markov Chains.
-		 - **L3B:** Monte Carlo.
-		 - **L4A:** Metropolis Hastings.
-		 - **L4B:** Personnel example using PyMC3.
-		 - **L5:** Gibbs sampler.
-		 - **L6:** Methods to assess convergence.
-		 - **L7:** Bayesian Linear Regression.
-		 - **L9:** Bayesian Logistic Regression.
-		 - **L10:** Poisson Regression.
-		 - **L11A:** Hierarchical modelling.
-		 - **L11B:** Hierarchical modelling - Linear Regression.
-		 - **L11H:** Mixture models.
+	- **L3A:** Markov Chains.
+	- **L3B:** Monte Carlo.
+	- **L4A:** Metropolis Hastings.
+	- **L4B:** Personnel example using PyMC3.
+	- **L5:** Gibbs sampler.
+	- **L6:** Methods to assess convergence.
+	- **L7:** Bayesian Linear Regression.
+	- **L9:** Bayesian Logistic Regression.
+	- **L10:** Poisson Regression.
+	- **L11A:** Hierarchical modelling.
+	- **L11B:** Hierarchical modelling - Linear Regression.
+	- **L11H:** Mixture models.
  - **Course 3, Bayesian Statistics: Mixture Models**
-		 - Coming soon :)
+	- Coming soon :)
 
 ## Installation