Machine Learning & Data Science A-Z: Hands-on Python 2021
- Description
- Curriculum
- FAQ
- Reviews
Are you interested in data science and machine learning, but you don’t have any background, and you find the concepts confusing?
Are you interested in programming in Python, but you always afraid of coding?
I think this course is for you!
Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.
This course is completely categorized, and we don’t start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:
Chapter1: Introduction and all required installations
Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)
Chapter3: Preprocessing
Chapter4: Machine Learning Types
Chapter5: Supervised Learning: Classification
Chapter6: Supervised Learning: Regression
Chapter7: Unsupervised Learning: Clustering
Chapter8: Model Tuning
Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.
Remember! That this course is created for you with any background as all the concepts will be explained from the basic! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.
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1
Course Content -
2
What is Machine Learning? Some Basic Terms
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3
Python Installation -
4
Python IDE
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5
IDE Installation
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6
Installation of Required Libraries
Hi,
In this session, we are going to install the required libraries that we need during the course.
We are going to use pip installation. You can find the related codes for this installation below:
pip install numpy
pip install pandas
pip install -U scikit_learn
pip install scipy
Note1! If you receive an error with the above commands, just google the pip install (name of the package) and you should see the last updated pip installation commands. IT'S VERY EASY :)
Note2! If you are using anaconda distribution, please use the below commands
conda install numpy
conda install pandas
conda install -c conda-forge scikit-learn
conda install -c anaconda scipy
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7
Spyder Interface
Hi,
In case you do not have Python on your computer as we discussed, please go to the below link and download any version of the Python that you prefer and then select it as an interpreter as we discussed in the video:
https://www.python.org/downloads/
Thanks
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8
Python Source Codes
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9
NumPy1
Hi and Congrats on Finishing First Chapter!
As we promised, you can download all the source codes from the below link!
https://www.dropbox.com/sh/034svu06emp71r4/AAANXcqD-eUjjFI8YVzuwZyVa?dl=0
I strongly suggest to code with me during the course and this is just for your archive!
Enjoy the course!
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10
NumPy2
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11
NumPy3
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12
NumPy4
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13
NumPy5
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14
NumPy6
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15
Pandas1
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16
Pandas2
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17
Pandas3
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18
Pandas4
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19
Visualization with Matplotlib1
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20
Visualization with Matplotlib2
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21
Visualization with Matplotlib3
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22
Visualization with Matplotlib4
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23
Visualization with Matplotlib5
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24
Chapter 2 QuizNow that you finished the second chapter of this course, lets review some of the concepts with some questions. If you don't remember the answers, don't worry at all! Just go to the related session again and review it again! They are so easy, so let's go through them :)
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25
Reading and Modifying a Dataset
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26
Statistics1
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27
Statistics2
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28
Statistics3 - Covariance
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29
Missing Values1
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30
Missing Values2
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31
Outlier Detection1
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32
Outlier Detection2
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33
Outlier Detection3
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34
Concatenation
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35
Dummy Variable
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36
Normalization
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37
Chapter3 Quiz
Now that you finished the third chapter of this course (Good job!), let's review some of the concepts with some questions. If you don't remember the answers, don't worry at all! Just go to the related session again and review it again just like what you did in the previous chapter! They are so easy, so let's go through them :)
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40
Supervised Learning Models - Introduction and Understanding the Data
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41
k-NN Concepts
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42
k-NN Model Development
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43
k-NN Training-Set and Test-Set Creation
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44
Decision Tree Concepts
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45
Decision Tree Model Development
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46
Decision Tree - Cross Validation
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47
Naive Bayes Concepts
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48
Naive Bayes Model Development
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49
Logistic Regression Concepts
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50
Logistic Regression Model Development
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51
Model Evaluation Concepts
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52
Model Evaluation - Calculating with Python
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53
Chapter 5 Quiz
Now that you finished the fifth chapter of this course, let's review some of the concepts with some questions. If you don't remember the answers, don't worry at all! Just go to the related session again and review it again! They are so easy, so let's go through them :)
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54
Simple and Multiple Linear Regression Concepts
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55
Multiple Linear Regression - Model Development
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56
Evaluation Metrics - Concepts
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57
Evaluation Metrics - Implementation
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58
Polynomial Linear Regression Concepts
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59
Polynomial Linear Regression Model Development
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60
Random Forest Concepts
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61
Random Forest Model Development
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62
Support Vector Regression Concepts
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63
Support Vector Regression Model Development
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64
Chapter 6 Quiz
Now that you finished the sixth chapter of this course, let's review some of the concepts with some questions. If you don't remember the answers, don't worry at all! Just go to the related session again and review it again! They are so easy, so let's go through them :)
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65
Introduction
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66
K-means Concepts1
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67
K-means Concepts2
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68
K-means Model Development1
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69
K-means Model Development2
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70
K-means - Model Evaluation
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71
DBSCAN Concepts
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72
DBSCAN Model Development
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73
Hierarchical Clustering Concepts
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74
Hierarchical Clustering Model Development
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75
Chapter 7 Quiz
Now that you finished the seventh chapter of this course, let's review some of the concepts with some questions. If you don't remember the answers, don't worry at all! Just go to the related session again and review it again! They are so easy, so let's go through them :)
