[MAINTENANCE] Various typographical error fixes at /examples (#326)

* [MAINTENANCE] Various typographical error fixes at `/examples`

* [MAINTENANCE] Various typographical error fixes at `/examples` 2

docs/examples/index.md

@@ -1,11 +1,11 @@
 💡 Hands-on examples are the best way to learn about a new framework.
 
-Here, you'll find several examples of how to build models with Ludwig for a variety of tasks with LLMs, neural networks, and tree-based models. We provide sample datasets, commands, scripts, and colab notebooks. Please [reach out](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) if you have any questions!
+Here, you'll find several examples of how to build models with Ludwig for a variety of tasks with LLMs, neural networks, and tree-based models. We provide sample datasets, commands, scripts, and Colab notebooks. Please [reach out](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) if you have any questions!
 
-💬 The **LLMs** section gives a broad overview of the full breadth of Ludwig's LLM capabilities like [zero-shot batch inference]() and [fine-tuning Llama-2-7b]().
+💬 The **LLMs** section gives a broad overview of the full breadth of Ludwig's LLM capabilities like [zero-shot batch inference](/examples/llms/llm_zero_shot_text_generation/) and [fine-tuning Llama-2-7b](/examples/llms/llm_finetuning/).
 
-🎯 The **Supervised ML** section has in-depth tutorials for how to use Ludwig's command line interface and Python API for machine learning in a supervised setting. Check out [Image Classification on MNIST](http://127.0.0.1:8000/examples/mnist/).
+🎯 The **Supervised ML** section has in-depth tutorials for how to use Ludwig's command line interface and Python API for machine learning in a supervised setting. Check out [Image Classification on MNIST](/examples/mnist/).
 
-🏛️ The **Use Cases** section illustrates how Ludwig can be applied to a variety of machine learning tasks, such as, natural language understanding, timeseries forcasting, multi-label classification to name just a few. Read about Ludwig models for [Sentiment Analysis]().
+🏛️ The **Use Cases** section illustrates how Ludwig can be applied to a variety of machine learning tasks, such as, natural language understanding, timeseries forecasting, multi-label classification to name just a few. Read about Ludwig models for [Sentiment Analysis](/examples/sentiment_analysis/).
 
 Have fun exploring!