Update task headings

NumPy/Array Basics/Broadcasting/task.md

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-## Broadcasting
-
 
 A frequent situation is that we have 
 a smaller array and a larger array and we need to use the smaller one

NumPy/Array Basics/Create an Array from List/task.md

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-## Create an Array
-
 There are several ways you can create arrays.
 
 First, you can create an array from a regular Python list or tuple using the `array` function. 

NumPy/Array Basics/Create an Array from Range/task.md

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-## Create an Array from a Range
-
 To create arrays containing sequences of numbers, NumPy provides the [`arange`](https://numpy.org/doc/stable/reference/generated/numpy.arange.html?highlight=arange#numpy.arange) 
 function. It is analogous to Python's built-in [`range`](https://docs.python.org/3/library/functions.html#func-range) but returns an array.
 

NumPy/Array Basics/Create an Empty Array/task.md

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-## Create an Empty Array
-
 Sometimes, the size of an array is known, while its elements are originally unknown. 
 NumPy offers several functions to create arrays with initial placeholder content:
 

NumPy/Array Basics/Random Sampling/task.md

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-## Random Sampling
-
 Sometimes, you might need to fill an array with random numbers or sample them from 
 different statistical distributions. 
 Numpy's [`random`](https://numpy.org/doc/stable/reference/random/#module-numpy.random) module allows you to do this. It is a suite of functions based on 

NumPy/Array Basics/Random Shuffle/task.md

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-## Random Shuffle
-
 Sometimes, you need to shuffle the contents of an array. For instance, in machine learning tasks, 
 it is common to shuffle data and normalize it. 
 

NumPy/Array Basics/Reading and Writing Files/task.md

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-## Reading and Writing Files
-
 You will, at some point, want to save your arrays to disk and load them back without having to 
 re-run the code. Fortunately, there are several ways to save and load objects with NumPy.
 `ndarray` objects can be saved to and loaded from disk files with [`loadtxt`](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html#numpy.loadtxt) 

NumPy/Array Basics/Reshape/task.md

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-## Reshape
-
 Reshaping means changing the `shape` of an array without changing its data.
 As we mentioned earlier, the `shape` of an array is the number of elements in each dimension.
 By reshaping, we can add or remove dimensions, or change the number of elements in each dimension.

NumPy/Array Basics/ndarray/task.md

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-## ndarray
-
 At the core of the NumPy package is the [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) object – a homogeneous multidimensional array.
 `ndarray` is a shorthand for "N-dimensional array". An N-dimensional array is simply an array with any number of dimensions.
 It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. 

NumPy/Array Indexing and Slicing/Boolean Indexing/task.md

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-## Boolean Indexing
-
 Boolean arrays used as indices are treated differently from index 
 arrays. Boolean arrays must be of the same shape as the initial dimensions of the array being indexed.
 Such boolean arrays are often referred to as **masks**, and the process of using them in indexing other arrays

NumPy/Array Indexing and Slicing/Indexing Basics/task.md

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-## Indexing Basics
-
 Array indexing refers to any use of square brackets (`[]`) to index array values.
 
 ### Single element indexing

NumPy/Array Indexing and Slicing/Integer Array Indexing/task.md

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-## Integer Array Indexing
-
 NumPy arrays may be indexed with other arrays.
 For all cases of index arrays, what is returned is a copy of the original data, not a view as one gets for slices.
 

NumPy/Array Math/Basic Math Functions/task.md

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-## Basic Mathematical Functions
-
 The ease of implementing mathematical formulas that work on arrays is one of the things that make NumPy 
 so widely used in the scientific Python community.
 

NumPy/Array Math/Linear Algebra/task.md

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-## Linear Algebra
-
 Matrices and matrix operations are used in the description of many machine learning algorithms due
 to the efficiency of matrix solutions in ML problems.
 NumPy [`numpy.linalg`](https://numpy.org/doc/stable/reference/routines.linalg.html)

NumPy/Arrays of String and Unicode Values/Basic String Operations in NumPy/task.md

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-## Basic String Operations in NumPy
-
 There is a number of functions in NumPy that perform string operations on arrays of string and unicode values by calling 
 familiar Python string methods. Here are some examples:
 

NumPy/Arrays of String and Unicode Values/Translate/task.md

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-## Translate
-
 Sometimes, you might need to remove certain characters from your array of strings or replace them with 
 some other characters in accordance with some rule or translation table. For that purpose, you can use
 [`numpy.char.translate(a, table, deletechars=None)`](https://numpy.org/doc/stable/reference/generated/numpy.char.translate.html), 

NumPy/Arrays of String and Unicode Values/numpy.char Module/task.md

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-## `numpy.char` Module
-
 [`numpy.chararray`](https://numpy.org/doc/stable/reference/generated/numpy.char.array.html) used to provide a view of arrays of string and [unicode](https://docs.python.org/3/howto/unicode.html) values.
 However, the `chararray` class currently exists only for backwards compatibility, and
 it is not recommended for new development. Starting from numpy 1.4, if 

NumPy/Compare Search/Compare with a scalar/task.md

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-## Compare with a Scalar
-
 Each element of an array can be compared against a scalar using any of the operators:
 - greater than (`>`),
 - greater than or equal to (`>=`), 

NumPy/Compare Search/Element-wise Comparison/task.md

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-## Elementwise Comparison
-
 Elementwise comparison is when you compare each element of one array to an element of another array.
 [`numpy.equal`](https://numpy.org/doc/stable/reference/generated/numpy.equal.html) is a function that returns `(x1 == x2)` elementwise.
 It accepts two input arrays which must either have equal `shape` or be broadcastable to a common shape 

NumPy/Compare Search/Find Unique Values/task.md

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-## Find Unique Values
-
 The [`numpy.unique`](https://numpy.org/doc/stable/reference/generated/numpy.unique.html) function is pretty 
 straightforward – it finds unique elements in the input array and returns them as a sorted array:
 

NumPy/Compare Search/Find maximum/task.md

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-## Find Maximum (or Minimum)
-
 There are several ways to find the maximum value in an array. The most straightforward way is to 
 use [`numpy.amax`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html), which
 returns the maximum of an array or the maximum along an axis:

NumPy/Compare Search/Search/task.md

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-## Search
-
 You can search an array for a certain value or for values that satisfy a condition, 
 and return the **indices** that get a match.
 

NumPy/Compare Search/bincount/task.md

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-## bincount
-
 A use case somewhat related to finding unique values is counting the number of occurrences of each value in an array.
 [`numpy.bincount`](https://numpy.org/doc/stable/reference/generated/numpy.bincount.html) is the function that does just that for an array of non-negative ints. It returns 
 the result of binning the input array, which is the count of how many times every integer on the interval from 0 

NumPy/Introduction/Course View/task.md

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-## Course View
-
 <b>Course View</b> shows you the course syllabus: a list of lessons with tasks.
 
 <style>

NumPy/Introduction/Editor/task.md

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-## Editor
-
 The <b>Editor</b> is your playground where you will be programming. You can experiment here while you work on theoretical tasks and quizzes without being checked.
 
 For programming assignments, the Editor is where you’ll fix the existing code or write your own code from scratch. This code will be checked.

NumPy/Introduction/Getting to know you/task.md

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-## Getting to know you
 Thank you for taking our Python Libraries – NumPy course!
 We would be happy to get to know you a bit better, so we’re asking you to fill out [this brief form](https://surveys.jetbrains.com/s3/getting-to-know-the-student-mastering-python-libs-numpy-course).

NumPy/Introduction/Import NumPy/task.md

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-## Import NumPy
-
 To access NumPy and its functions, import it in your Python code like this:
 ```python
 import numpy as np

NumPy/Introduction/Navigating Around/task.md

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-## JetBrains Academy plugin overview
-
 This and the following three tasks will help you take your first steps with the [JetBrains Academy plugin](https://www.jetbrains.com/help/education/educational-products.html) and use it to learn Python.
 
 With the JetBrains Academy plugin, you can learn programming languages and tools by completing coding tasks and get instant feedback right inside the IDE.

NumPy/Introduction/Task Description/task.md

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-## Task Description
-
 The **Task Description** window gives you all the information you need to complete a task:
 
 For theoretical tasks, the description provides learning and reading materials.

NumPy/Introduction/Why NumPy/task.md

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-## Choosing NumPy: the Benefits
-
 [NumPy](https://numpy.org/) is the most widely used package for scientific computing in Python. It 
 provides a multidimensional array object and different derived objects (such as masked arrays and 
 matrices), as well as a wide variety of ways to manipulate arrays. These include mathematical, 

NumPy/Transposing Sorting Concatenating/Concatenate and Stack/task.md

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-## Concatenate
+### Concatenate
 
 Concatenation refers to joining two or more arrays of the same shape along a specified axis.
 While there’s [`numpy.concatenate()`](https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html), there are also several helper functions 

NumPy/Transposing Sorting Concatenating/Partial Sort/task.md

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-## Partial Sort (Partition)
+### Partition
 
 If you have some dataset in the form of a numpy array and you only want to get `k` smallest numbers from it instead of fully sorting it,
 this task can be performed using the [`numpy.partition()`](https://numpy.org/doc/stable/reference/generated/numpy.partition.html#numpy.partition) method.

NumPy/Transposing Sorting Concatenating/Sort/task.md

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-## Sort
-
 Methods [`numpy.sort()`](https://numpy.org/doc/stable/reference/generated/numpy.sort.html?highlight=sort#numpy.sort) 
 and [`numpy.ndarray.sort`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.sort.html#numpy.ndarray.sort) can both be used to sort an array, 
 the only difference being that the former returns a sorted copy of an array, while the latter is used 

NumPy/Transposing Sorting Concatenating/Split/task.md

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-## Split
-
 Splitting is a reverse operation of joining. While joining combines several arrays into a 
 single one, splitting breaks one array into several.
 Numpy functions [`split()`](https://numpy.org/doc/stable/reference/generated/numpy.split.html) and 

NumPy/Transposing Sorting Concatenating/Transpose 1D/task.md

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-## Transpose a 1D array
-
 In terms of programming 
 languages, it is not possible to transpose a 1D array: the transpose of a 1D array is still a 1D array.
 

NumPy/Transposing Sorting Concatenating/Transpose/task.md

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-## Transpose
-
 When you transpose an array, the order of the axes is reversed, and the indices of each element are reversed along each axis.
 Item `[0, 1]`, for example, becomes item `[1, 0]`.
 [`numpy.ndarray.transpose`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.transpose.html#numpy.ndarray.transpose) returns a view of the array with axes transposed.

Projects/SVD/Introduction/task.md

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-## Introduction
-
 #### Who is this tutorial for?
 This tutorial is for people who have a basic understanding of linear algebra and arrays in NumPy and 
 want to find out how n-dimensional (n >= 2) arrays are represented and manipulated. In particular,