chore: update roadmap content json (#8776)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>

public/roadmap-content/ai-data-scientist.json

@@ -2,22 +2,11 @@
   "aStaDENn5PhEa-cFvNzXa": {
     "title": "Mathematics",
     "description": "Mathematics is the foundation of AI and Data Science. It is essential to have a good understanding of mathematics to excel in these fields.",
-    "links": [
-      {
-        "title": "Mathematics for Machine Learning",
-        "url": "https://imp.i384100.net/baqMYv",
-        "type": "article"
-      },
-      {
-        "title": "Algebra and Differential Calculus",
-        "url": "https://imp.i384100.net/LX5M7M",
-        "type": "article"
-      }
-    ]
+    "links": []
   },
   "4WZL_fzJ3cZdWLLDoWN8D": {
     "title": "Statistics",
-    "description": "Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is used in a wide range of fields, including science, engineering, medicine, and social science. Statistics is used to make informed decisions, to predict future events, and to test hypotheses. It is also used to summarize data, to describe relationships between variables, and to make inferences about populations based on samples.\n\nLearn more from the resources given on the roadmap.",
+    "description": "Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is used in a wide range of fields, including science, engineering, medicine, and social science. Statistics is used to make informed decisions, to predict future events, and to test hypotheses. It is also used to summarize data, to describe relationships between variables, and to make inferences about populations based on samples.",
     "links": []
   },
   "gWMvD83hVXeTmCuHGIiOL": {
@@ -331,24 +320,8 @@
   },
   "kBdt_t2SvVsY3blfubWIz": {
     "title": "Machine Learning",
-    "description": "Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to \"learn\" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.\n\nLearn more from the following resources:",
-    "links": [
-      {
-        "title": "Advantages and Disadvantages of AI",
-        "url": "https://medium.com/@laners.org/advantages-and-disadvantages-of-artificial-intelligence-cd6e42819b20",
-        "type": "article"
-      },
-      {
-        "title": "Reinforcement Learning 101",
-        "url": "https://medium.com/towards-data-science/reinforcement-learning-101-e24b50e1d292",
-        "type": "article"
-      },
-      {
-        "title": "Understanding AUC-ROC Curve",
-        "url": "https://medium.com/towards-data-science/understanding-auc-roc-curve-68b2303cc9c5",
-        "type": "article"
-      }
-    ]
+    "description": "Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to \"learn\" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.",
+    "links": []
   },
   "FdBih8tlGPPy97YWq463y": {
     "title": "Classic ML (Sup., Unsup.), Advanced ML (Ensembles, NNs)",

public/roadmap-content/ios.json

@@ -2370,7 +2370,7 @@
   },
   "RuXuHQhMt2nywk43LgGeJ": {
     "title": "Static Library",
-    "description": "Static libraries in iOS development are collections of compiled code that are linked directly into an app's executable at build time. They contain object code that becomes part of the final application binary, increasing its size but potentially improving load time performance. Static libraries are typically distributed as .a files, often accompanied by header files that define their public interfaces. When using static libraries, the entire library code is included in the app, even if only a portion is used, which can lead to larger app sizes. However, this approach ensures that all necessary code is available within the app, eliminating runtime dependencies. Static libraries are particularly useful for distributing closed-source code or when aiming to minimize runtime overhead. They offer simplicity in distribution and version management but may require recompilation of the entire app when the library is updated. In iOS development, static libraries are gradually being replaced by more flexible options like dynamic frameworks and XCFrameworks, especially for larger or frequently updated libraries.\n\nLearn more from the following resources:",
+    "description": "Static libraries in iOS development are collections of compiled code that are linked directly into an app's executable at build time. They contain object code that becomes part of the final application binary, increasing its size but potentially improving load time performance. Static libraries are typically distributed as .a files, often accompanied by header files that define their public interfaces. Using static libraries ensures that all necessary code is available within the app, eliminating runtime dependencies. Static libraries are particularly useful for distributing closed-source code or when aiming to minimize runtime overhead. They offer simplicity in distribution and version management but may require recompilation of the entire app when the library is updated. In iOS development, static libraries are gradually being replaced by more flexible options like dynamic frameworks and XCFrameworks, especially for larger or frequently updated libraries.\n\nLearn more from the following resources:",
     "links": [
       {
         "title": "Static Library in iOS",