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Machine Learning & Data Science in Python For Beginners

Name: Machine Learning & Data Science in Python For Beginners
Rating: 4.0 (55 reviews)

Learn Supervised & Unsupervised ML, Machine Learning Process, Models, Python, NumPy, Pandas, Seaborn, Data Visualisation

Created byPeter Alkema

Last updated 9/2022

English

What you'll learn

What is Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
Types of Supervised Learning: Classification
Regression
Types of Unsupervised Learning: Clustering
Association
Data Collection
Data Preparing
Selection of a Model
Data Training and Evaluation
HPT in Machine Learning
Prediction in ML
DPP in ML
Need of DPP
Steps in DPP
Python Libraries
Missing, Encoding, and Splitting Data in ML
Python, Java, R,and C ++
How to install python and anaconda?
Interface of Jupyter Notebook
Mathematics in Python
Euler's Number and Variables
Degree into Radians and Radians into Degrees in Python
Printing Functions in Python
Feature Scaling for ML
How to Select Features for ML
Filter Method
LDA in ML
Chi Square Method
Forward Selection
Training and Testing Data Set for ML
Selection of Final Model
ML Applications
Practical Skills in ML: Mastery
Process of ML
What is Extension in ML
ML Tradeoff
ML Variance Error
Logistic Regression
Data Visualization
Pandas and Seaborn-Library for ML

Course content

9 sections • 74 lectures • 9h 3m total length

Introduction4:04
Welcome to this Machine Learning, Data Science and Python course. This is an amazing opportunity for you to learn top rated skills that will boost your career and ensure you stay at the cutting edge of data science.
Introduce Yourself to Your Fellow Students And Tell Everyone What are Your Goals0:17
This is your opportunity to share something about yourself with the rest of the students in this course. Tell us all about your goals and what you want to achieve. You can come back to this board and add more thoughts as you go through the course and achieve your goals. Seeing all the other students in the course will also motivate you and keep you going as you participate in this community of learning.
Machine Learning Workbook0:15
In this lesson, you can download the master workbook that you get with this course. It contains all the reference material which you can easily access and download for your journey with machine learning. Each of the pages is also attached as worksheets to the individual lessons where they are taught, which means you can also download them individually depending on your requirements.
All code files for this course0:13
Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!1:20
Throughout this course, we will celebrate your progress at 25%, 50%, 75%, and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

What is Machine Learning3:57
In this lesson, we shall learn that what is machine learning. We shall describe this concept by giving various example and elaborate the concept in a unique way.
Supervised Machine Learning2:24
In this lesson, we shall learn that what is supervised machine learning. We shall explain that there are three types for machine learning. However, here we will discuss only the supervised machine learning.
Unsupervised Machine Learning3:18
In this lesson, we shall learn that what is unsupervised machine learning. We shall explain it by giving the useful example in a unique way.
Semi-Supervised Machine Learning1:32
In this lesson, we shall learn that what is semi-supervised machine learning. We shall give an example to illustrate the concept in a comprehensive way.
Example of Supervised Machine Learning0:44
In this lesson, we shall explain an example of supervised machine learning. The example will clarify your key concept of supervised machine learning.
Example of Un-Supervised Machine Learning0:25
In this lesson, we shall explain an example of un-supervised machine learning. The example will clarify your key concept of un-supervised machine learning.
Example of Semi-Supervised Machine Learning0:50
In this lesson, we shall explain an example of semi-supervised machine learning. The example will clarify your key concept of semi-supervised machine learning.
Types of Supervised Learning: Classification2:52
In this lesson, we shall explain the types of supervised machine learning. We shall explain the classification as it is one of the type of machine learning. We shall explain what is classification with an example.
Regression1:33
In this lesson, we shall explain the type of supervised machine learning. We shall explain the regression as it is one of the type of machine learning. We shall explain what is regression with an example.
Types of Unsupervised Learning: Clustering1:57
In this lesson, we shall explain the types of un-supervised machine learning. We shall explain the clustering as it is one of the type of machine learning. We shall explain what is clustering with an example.
Association1:17
In this lesson, we shall explain the types of un-supervised machine learning. We shall explain the Association as it is one of the type of machine learning. We shall explain what is Association with an example.
You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>0:24
Throughout this course, we will celebrate your progress at 25%, 50%, 75%, and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>
Types of Machine Learning

Data Collection7:30
In this lesson, we shall explain the steps of machine learning. We shall explain the first step of machine learning which is data collection. We shall also give the example to illustrate the concept in a detailed way.
Data Preparation4:35
In this lesson, we shall explain the steps of machine learning. We shall explain the second step of machine learning which is data preparing. We shall also give the example to illustrate the concept in a detailed way
Selection of a Model3:17
In this lesson, we shall explain the steps of machine learning. We shall explain the third step of machine learning which is selection of model. We shall also give the example to illustrate the concept in a detailed way
Data Training and Evaluation3:43
In this lesson, we shall explain the steps of machine learning. We shall explain the 4th step of machine learning which is data training and evaluation. We shall also give the example to illustrate the concept in a detailed way.
HPT in Machine Learning3:25
In this lesson, we shall explain the steps of machine learning. We shall explain the 5th step of machine learning which is HTTP in machine learning. We shall also give the example to illustrate the concept in a detailed way
Prediction in ML2:14
In this lesson, we shall explain the steps of machine learning. We shall explain the last step of machine learning which is about prediction. We shall also give the example to illustrate the concept in a detailed way
DPP in ML3:03
In this lesson, we shall talk about what is data preprocessing and we shall understand it by selection a suitable example.
Need of DPP2:51
In this lesson, we shall discuss that why there is a need of data preprocessing and this need can be fulfilled and have impact in machine learning.
Steps in DPP5:14
In this lesson, we shall explain the steps of data preprocessing in machine learning. We shall explain the six steps of data preprocessing in machine learning. We shall also give the examples to illustrate the each step and concept in a detailed way
Python Libraries9:19
In this lesson, we shall explain about python libraries like Numpy, Matplotlib, and Pandas. However, this lecture is all about the Numpy. We shall explain that what is NumPy and where it can be used while working with Python.
Missing, Encoding, and Splitting Data in ML7:07
In this lesson, we shall explain that what is missing data and how to find the missing data in ML. We shall also explain that what is categorical dat and how to split the data in ML.
Data Collection

Feature Scaling for ML3:29
In this lesson, we shall talk about the machine learning models. We shall explain the features to scale a model. The model selection is important in ML. So, We shall explain that how to scale a model while choosing to developed a ML algorithm.
How to Select Features for ML8:43
In this lesson, we shall talk about the machine learning models. We shall explain the selection of features for an ML model. The model selection is important in ML. So, We shall explain that how to select a model while choosing to developed a ML algorithm
Filter Method6:15
In this lesson, we shall talk about the machine learning models. We shall explain the filter method to develop an ML algorithm. The filter method is important in ML. So, We shall explain that how to use a filter method while choosing to developed a ML algorithm.
LDA in ML4:11
In this lesson, we shall talk about the LDA machine learning models. We shall explain the LDA features to select an ML model. The LDA model selection is important in ML. So, We shall explain that how to use LDA model while choosing to developed a ML algorithm.
Chi Square Method4:00
In this lesson, we shall talk about the chi-square based machine learning models. We shall explain the chi-square features technique for a ML model. So, we shall explain that how to use a chi-square model technique while choosing to developed a ML algorithm.
Forward Selection7:24
In this lesson, we shall talk about the forward selection machine learning technique. The forward selection machine learning technique is important in ML. So, We shall explain that how to use a forward selection machine learning technique while choosing to developed a ML algorithm.
Training and Testing Data Set for ML7:24
In this lesson, we shall talk about the Training and Testing Data Set for ML technique. The Training and Testing Data Set for ML is important in ML. So, We shall explain that how to use a Training and Testing Data Set for ML technique while choosing to developed a ML algorithm.
Selection of Final Model7:14
In this lesson, we shall talk about the Selection of Final Model for ML. The Selection of Final Model for ML is important in ML. So, We shall explain that how to use a Selection of Final Model while choosing to developed a ML algorithm.
You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>0:29
Throughout this course, we will celebrate your progress at 25%, 50%, 75%, and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>
ML Applications6:02
In this lesson, we shall explain that what are the applications of ML. We shall give the practical examples of machine learning where it has its vast use. We shall get full insight of ML uses and applications in this lesson.
Practical Skills in ML: Mastery6:24
In this lesson, we shall explain that what are the practical skills we needed before working and choosing an accurate machine learning algorithm. We shall explain that the master is important as a practical skill for ML.
Process of ML10:08
In this lesson, we shall explain that what is the process to get a practical skill of machine learning. We shall give a comprehensive example to illustrate the core concept of machine learning.
What is Extension in ML9:10
In this lesson, we shall explain that what is extension in machine learning. We shall explain the various steps of extension. We shall give the example while explaining the each step.
ML Tradeoff6:53
In this lesson, we shall explain that what is tradeoff variance in ML and how it works while choosing a suitable machine learning algorithms.
ML Variance Error2:31
In this lesson, we shall explain that what is machine learning variance error. We shall give a detailed overview of ML variance error.
What is Regression7:21
In this lesson, we shall explain that what is regression in ML. The regression and its types. However, we have an example in this lesson to explain the major concept of machine learning.
Logistic Regression2:16
In this lesson, we shall explain that what is logistic regression. We shall explain this concept in an easy way. However, the examples of logistic regression are very important to understand about logistic regression.
Regression Analysis

Code files for this section0:02
How to install python and anaconda?6:04
In this lesson, we shall explain that how to install python and anaconda. We shall give a complete overview of installation process. However, this lecture is very important in practical sense.
Interface of Jupyter Notebook8:06
In this lesson, we shall explain that what is Jupyter notebook and how to use the Jupyter notebook. We shall explain the complete features of Jupyter notebook.
Mathematics in Python13:34
In this lesson, we shall explain the core concepts of mathematics in python. W shall explain that how to add, subtract, multiply, and divide the numbers in python.
Euler's Number and Variables12:19
In this lesson, we shall explain the core concepts of Euler's number and variables in python. We shall explain the Euler's number in a detailed way by using the python. However, the lecture contains the concept of variables as well in python.
Degree into Radians and Radians into Degrees in Python13:36
In this lesson, we shall explain the core concepts of degrees and radians in python. We shall explain that how to convert the angles from radians into degrees and degrees into radians.
Printing Functions in Python12:31
In this lesson, we shall explain the core concepts of printing the functions in python. We shall explain that how to writ the code while printing a number in Python. We shall select the various examples for the printing of function in python.
You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>0:31
Throughout this course, we will celebrate your progress at 25%, 50%, 75%, and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>
Code Quiz

Code files for this section0:02
Random Selection15:43
In this lesson, we shall explain the core concepts of random selection of arrays or numbers in python. We shall write the code of random section in python. The lesson contain the various examples for best explanation.
Random Array in Python13:26
In this lesson, we shall explain the core concepts of random selection of numbers. This lecture is similar to the previous lector. However, have different examples and variation in python.
Random Array and Scattering15:33
In this lesson, we shall explain the core concepts of random array and scattering plot. We shall explain the previous lecture and give a further detail about scattering of plotting.
Scattering Plot15:13
In this lesson, we shall learn about scattering. We shall explain that what is scattering and how to plot scatter graph while using the python. Example and discussing have been made to clear the concept.
Jupyter Notebook Setup and Problem15:20
This lecture is very different. In this lecture, we have explained that how you can trap from the way while working in Jupyter notebook. Actually, I was trapped in this lecture and finally get the right way to use Jupyter notebook.
Random Array in Python16:00
In this lecture, we shall explain that who to choose the random zeros in python. We shall explain that what are the ways to choose the random zeros in python. We shall write the code for the random selection of zeros in python.
Printing Several Function in Python15:42
In this lesson, we shall explain that how to print the several functions at the same time while coding in python. We shall give the various examples and print the various function at the same time.
Exponential and Logarithmic Function in Python17:38
In this lesson, we shall write the code for the exponential, logarithmic, and trigonometric functions. We shall explain that who to find the solution of these functions while coding in python.
Matplotlib Quiz

Code files for this section0:02
Simple Line Graph with Matplotlib18:59
Learn to create simple line plots for data visualization with matplotlib and numpy, and explore sine, cosine, exponential, and logarithmic graphs with adjustable parameters.
Color Scheme with Matplotlib23:52
Explore data visualization with matplotlib by plotting sine, cosine, tangent, exponential, and logarithmic functions. Learn how to build color schemes and color codes to compare multiple graphs using plt.plot.
Dot and Dashed Graph18:37
Learn to create line graphs in Python for data visualization using matplotlib, choosing solid, dashed, dash-dot, and dotted styles, and customize x/y limits and colors.
Scattering 1-Data visualization14:38
In this lesson, we shall talk about the scattering graph for the data visualization. We shall explain a comprehensive code for the data visualization. Moreover, we shall explain how this data can be useful while working Matplotlib and NumPy.
Labelling-Data Visualization24:48
In this lesson, we shall talk about the Labelling graph for the data visualization. We shall explain a comprehensive code for the data visualization to label the graph in different ways.. Moreover, we shall explain how labeling is useful while working Matplotlib and NumPy.
Color Processing-Data Visualization21:27
In this lesson, we shall talk about the color processing for the data visualization. We shall explain a comprehensive code for the color processing in data visualization. Moreover, we shall explain how color processing is useful while working Matplotlib and NumPy.
Data Visualization Quiz

Code files for this section0:02
Seaborn Scatter Plot29:23
Explore creating a Seaborn scatter plot to visualize wine data, loading a dataset, selecting features, assigning colors, labels, and legends, and displaying the plot.
Import DataFrame by Pandas11:30
In this lesson, we shall explain that how to import and generate the DataFrame by using the seaborn and pandas library in a detailed and comprehensive way. We shall write the code of different cities and the temperature variation on those cities along with other factors like humidity and etc.
You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!1:04
Throughout this course, we will celebrate your progress at 25%, 50%, 75%, and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>
Pandas Quiz

Requirements

No requirements, you will learn everything from scratch
Internet connection, laptop, or mobile phone
Passion towards learning data science and Machine learning

Description

Get instant access to a 69-page Machine Learning workbook containing all the reference material

Over 9 hours of clear and concise step-by-step instructions, practical lessons, and engagement

Introduce yourself to our community of students in this course and tell us your goals

Encouragement & celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificate

What will you get from doing this course?

This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses.

You don’t need to have any technical knowledge to learn these skills.

What will you learn:

What is Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
Types of Supervised Learning: Classification
Regression
Types of Unsupervised Learning: Clustering
Association
Data Collection
Data Preparing
Selection of a Model
Data Training and Evaluation
HPT in Machine Learning
Prediction in ML
DPP in ML
Need of DPP
Steps in DPP
Python Libraries
Missing, Encoding, and Splitting Data in ML
Python, Java, R,and C ++
How to install python and anaconda?
Interface of Jupyter Notebook
Mathematics in Python
Euler's Number and Variables
Degree into Radians and Radians into Degrees in Python
Printing Functions in Python
Feature Scaling for ML
How to Select Features for ML
Filter Method
LDA in ML
Chi-Square Method
Forward Selection
Training and Testing Data Set for ML
Selection of Final Model
ML Applications
Practical Skills in ML: Mastery
Process of ML
What is Extension in ML
ML Tradeoff
ML Variance Error
Logistic Regression
Data Visualization
Pandas and Seaborn-Library for ML
...and more!

Contents and Overview

You'll start with the What is Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Semi-Supervised Machine Learning; Example of Supervised Machine Learning; Example of Un-Supervised Machine Learning; Example of Semi-Supervised Machine Learning; Types of Supervised Learning: Classification; Regression; Types of Unsupervised Learning: Clustering; Association.

Then you will learn about Data Collection; Data Preparation; Selection of a Model; Data Training and Evaluation; HPT in Machine Learning; Prediction in ML; DPP in ML; Need of DPP; Steps in DPP; Python Libraries; Missing, Encoding, and Splitting Data in ML.

We will also cover Feature Scaling for ML; How to Select Features for ML; Filter Method; LDA in ML; Chi Square Method; Forward Selection; Training and Testing Data Set for ML; Selection of Final Model; ML Applications; Practical Skills in ML: Mastery; Process of ML; What is Extension in ML; ML Tradeoff; ML Variance Error; What is Regression; Logistic Regression.

This course will also tackle Python, Java, R,and C ++; How to install python and anaconda?; Interface of Jupyter Notebook; Mathematics in Python; Euler's Number and Variables; Degree into Radians and Radians into Degrees in Python; Printing Functions in Python.

This course will also discuss Random Selection; Random Array in Python; Random Array and Scattering; Scattering Plot; Jupyter Notebook Setup and Problem; Random Array in Python; Printing Several Function in Python; Exponential and Logarithmic Function in Python.

Next, you will learn about Simple Line Graph with Matplotlib; Color Scheme with Matplotlib; Dot and Dashed Graph; Scattering 1-Data visualization; Labelling-Data Visualization; Color Processing-Data Visualization; Seaborn Scatter Plot; Import DataFrame by Pandas.

Who are the Instructors?

Allah Dittah from Tech 100 is your lead instructor – a professional making a living from his teaching skills with expertise in Machine Learning. He has joined with content creator Peter Alkema to bring you this amazing new course.

We can't wait to see you on the course!

Enrol now, and master Machine Learning!

Peter and Allah

Who this course is for:

For beginners and professional as well
Searching jobs in data science and machine learning
For those who want to practice python, data science, and machine learning at the same time

Machine Learning & Data Science in Python For Beginners

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 6min

Introduction to Machine Learning12 lectures • 21min

Machine Learning Steps11 lectures • 52min

Machine Learning Models17 lectures • 1hr 40min

Languages for ML1 lecture • 8min

Python8 lectures • 1hr 7min

Introduction to NumPy9 lectures • 2hr 5min

Data Visualization with Matplotlib7 lectures • 2hr 2min

Pandas and Seaborn-Library for ML4 lectures • 42min

Requirements

Description

Who this course is for: