Python Projects
- Description
- Curriculum
- FAQ
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“Welcome to my comprehensive Python Projects Course, a journey that will take you from a beginner to an intermediate-level Python developer. In this carefully crafted course, we will delve into a diverse range of 15-20 Python projects, complemented by quizzes designed to reinforce your learning.
As your instructor, I understand that taking the first steps in programming can be both exciting and daunting. To ease your way, I will provide step-by-step instructions and guide you through the essential process of software installation. Rest assured that your learning experience is my top priority, and I wholeheartedly encourage you to ask questions and seek clarification whenever needed.
By the end of this course, you will have not only acquired hands-on experience in working with Python projects but also developed a strong foundation in Python programming. Our focus will primarily revolve around computer vision and data science coding, where we will explore crucial modules and packages with in-depth explanations.
This course is tailored to benefit you in various ways. Whether you are working on college projects, seeking to bolster your portfolio, or aiming to embark on a career as a Python developer, the skills you acquire here will prove invaluable.
Remember, you are never alone in your learning journey. This course is designed to foster a supportive community where you can collaborate, share insights, and seek assistance when facing challenges.
Join me in this exciting Python adventure, and let’s embark on this learning expedition together. Don’t hesitate – seize this opportunity to expand your horizons and dive into the course today!”
Find my technical blog on Dataiku which I explored in 2024. This tool is really amazing!
Stay tuned to get my next course about SQL(Basics)! I will attach some SQL clippings to this course!
If interested? I shall mail you once it gets launched. You can enroll for SQL.
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14
Unsupervised MLFrom the given "Iris" dataset, predict the optimum number of clusters and represent it visually.
Unsupervised ML is a type of ML that looks for previously undetected patterns in a dataset with no pre-existing labels and with a minimum of human supervision. In contrast to supervised ML which usually makes use of human - labelled data, unsupervised ML, also known as self-organisation allows for modelling of probability densities over inputs.
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15
Decision Tree - Supervised ML
Create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
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16
EDA Basic Understanding
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Linear Regression
In this section, we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.
In this task, we will predict the percentage of marks a student has based on their no. of study hours. We will solve this using a simple linear regression model as we are taking 2 variables.
Dataset link:-
https://bit.ly/w-data
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18
Support Vector Machine
Train SVM classifier using sklearn digits dataset (i.e. from sklearn.datasets import load_digits) and then,
Measure the accuracy of your model using different kernels such as rbf and linear.
Tune your model further using regularization and gamma parameters and try to come up with the highest accuracy score
Use 80% of samples as training data size
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19
Pandas
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20
Numpy
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21
Quiz 3Questions
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KNN
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23
Dummy Variables and One Hot Encoder
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Multiple Linear Regression
