Learning Python - 2nd Week

 2nd week of Python course to learn data structures, functions, and arguments:

Day 1

  • Introduction to Python
  • Data types
  • Variables
  • Operators
  • Conditional statements
  • Loops

Day 2

  • Functions
  • Arguments
  • Scope
  • Return statements
  • Exception handling

Day 3

  • Data structures
  • Lists
  • Tuples
  • Sets
  • Dictionaries

Day 4

  • Modules
  • Packages
  • Importing modules
  • Exporting functions

Day 5

  • Object-oriented programming
  • Classes
  • Instances
  • Methods
  • Encapsulation
  • Abstraction

Day 6

  • Testing
  • Unit testing
  • Integration testing
  • System testing

Day 7

  • Project
  • Create a project that uses the concepts you have learned throughout the course.

This is just a possible course outline. You may need to adjust it depending on your own learning style and goals. However, this should give you a good starting point for learning Python.

Here are some additional resources that you may find helpful:

  • Python Tutorial - This is a great resource for learning the basics of Python.
  • Data Structures and Algorithms in Python - This book provides a comprehensive introduction to data structures and algorithms in Python.
  • Python for Data Science - This book teaches you how to use Python for data science.
  • Python Programming - This book is a comprehensive guide to Python programming.

Comments

Post a Comment

Popular posts from this blog

Image Processing Using NumPy - Part 2

Image
NumPy can be used to perform image morphological operations. Here are some examples: Dilation : Dilation is an operation that expands the boundaries of objects in an image. It is often used to fill in holes or gaps in objects. import numpy as np import cv2 image = cv2.imread( 'image.jpg' , cv2.IMREAD_GRAYSCALE) # Create a structuring element structuring_element = np.ones(( 3 , 3 ), np.uint8) # Dilate the image dilated_image = cv2.dilate(image, structuring_element) cv2.imwrite( 'dilated_image.jpg' , dilated_image) Erosion : Erosion is an operation that shrinks the boundaries of objects in an image. It is often used to remove noise or small objects from an image. import numpy as np import cv2 image = cv2.imread( 'image.jpg' , cv2.IMREAD_GRAYSCALE) # Create a structuring element structuring_element = np.ones(( 3 , 3 ), np.uint8) # Erode the image eroded_image = cv2.erode(image, structuring_element) cv2.imwrite( 'eroded_image.jpg' , eroded...
Read more

Association Rule Data Mining

Image
Association rule mining is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Association rule mining is a type of unsupervised machine learning that discovers interesting relationships between variables in large datasets. It is a rule-based approach that finds association rules, which are if-then statements that describe the relationship between two or more items. An association rule has two parts: The antecedent (the "if" part) is a set of items that must occur together. The consequent (the "then" part) is an item that is likely to occur if the antecedent is present. For example, an association rule for a supermarket might be: If a customer buys diapers, then they are likely to also buy beer. This rule tells us that there is a strong association between the purchase of diapers and beer. This informati...
Read more

Policy Gradients

Image
Policy Gradients is a reinforcement learning algorithm that directly optimizes the policy, which is a function that maps from states to actions. The policy gradient algorithm works by estimating the gradient of the expected return with respect to the policy parameters, and then using gradient ascent to update the policy parameters. Policy Gradients is a powerful algorithm that has been used to achieve state-of-the-art results in a variety of games, including Atari, Go, and StarCraft. It is a versatile algorithm that can be used to solve a wide range of decision-making problems. Here are some of the key concepts in Policy Gradients: Policy : A policy is a function that maps from states to actions. The policy specifies the probability of taking each action in each state. Gradient : The gradient is a measure of the rate of change of a function. In the context of Policy Gradients, the gradient is used to measure the rate of change of the expected return with respect to the policy...
Read more