Machine Learning with Python from Styrish AI

A course that describes the basics of machine learning, along with the downloadable coding exercises on each topic.

Created byStyrish AI

Last updated 7/2024

English

What you'll learn

Describe various types of machine learning algorithms and their usages.
Understanding supervised (classification & regression) and unsupervised learning (clustering) and their applications.
Learn various supervised & unsupervised learning algorithms, such as, KNN, SVM, Simple and Multiple linear regressions, k-means clustering etc.
Various evaluation metrics used for classification and improvising the performance of your model.
Lab exercises with each video topic to write python code that implements these algorithms..

Course content

5 sections • 14 lectures • 1h 12m total length

Introduction to Machine Learning2:39
This lesson will provide you with a brief introduction about machine learning, and prepare you to pursue the later topics about it.

Introduction to Classification1:30
This lesson will facilitate you with the introduction to classification techniques in supervised learning and their usages. As a part of this course, you will learn various prominent classification techniques in machine learning, such as,

K-nearest neighbours
Logistic Regression
Decision Trees
Support Vector Machine (SVM)
K-Nearest Neighbors5:09
In this lesson, you will learn about the classification technique k-nearest neighbors, and how the algorithm classifies the data by identifying the nearest neighbors to it.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your knowledge (K-Nearest Neighbors)
Logistic Regression6:30
In this lesson, you will learn about the classification technique Logistic Regression that how the algorithm is useful for binary classification tasks, and how it is different from linear regression.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your Knowledge (Logistic Regression)
Decision Trees10:30
In this lesson, you will learn about the classification technique Decision Trees, and how the algorithm splits the data into various root to leaf nodes to construct the decision tree and predicts the class.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your Knowledge (Decision Trees)
Support Vector Machines (SVM)4:41
In this lesson, you will learn about the classification technique SVM (Support Vector machine), its detailed functioning, and how SVM algorithms classify the data by constructing an optimal hyperplane.
Kernels in SVM3:33
In this lesson, you will learn about the kernel functions used in SVM, and how those are helpful for non-linear data separation. Also, you will understand the usage of kernel trick.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your Knowledge (SVM)
Evaluation Metrics in Classification6:05
In this lesson, you will learn about the various metrics used in classification to identify the performance and accuracy of a model, and how to optimize it.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your Knowledge (Evaluation Metrics in Classification)

Introduction to Regression1:42
This lesson will facilitate you with the introduction to regression techniques in supervised learning and their usages. As a part of this course, you will learn various prominent regression techniques in machine learning, such as,

Simple Linear Regression
Multiple Linear Regression
Simple Linear Regression5:45
In this lesson, you will learn about Simple Linear Regression, and how the target value is predicted using a single feature in the data.
Multiple Linear Regression8:38
In this lesson, you will learn about Multiple Linear Regression, and how the target value is predicted using multiple features in the data. This lesson will also give you the understanding about choosing between simple and multiple linear regression.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your Knowledge (Regression)

Introduction to Clustering1:28
This lesson will facilitate you with the introduction to clustering techniques in unsupervised learning and their usages. As a part of this course, you will learn,

K-means clustering
K-Means Clustering7:59
In this lesson you will learn about the unsupervised learning technique k-means clustering, and how it helps in grouping or segmenting the data in different clusters based on its similarities.
Lab exercise is also attached to this lecture, which you can download on your computer. This contains:

A notebook file (.ipynb), which you can open using Jupyter Notebook.
A python file(.py) which you can open locally on your computer. Python must be installed on your computer.
Check your Knowledge (Clustering)

Requirements

You should know basics of any programming language, but python is preferred.

Description

This course will begin with a brief introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning and more.

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, Logistic Regression and Support Vector Machines(SVM). You’ll also learn about the importance and different types of regression techniques, like simple and multiple linear regression, and how those are helpful to make the predictions. Also, you would learn k-means clustering, a widely used unsupervised learning algorithm.

The lab exercises attached to each lecture will help you to digest the information you received via video lessons. To practice the lab exercises is highly recommendable to completely fit the topic into your mind. These exercise materials are downloadable and comprise a notebook file which you can import in Jupyter notebooks or Google Colab, and a python file.

A quiz associated to each topic will help you to assess yourself about the level of expertise you achieved on specific topic while pursuing this course.

This course will definitely help you to start your new journey into the world of artificial intelligence, which is one of the fastest-growing technologies and undoubtedly a cornerstone of humanity's future.

Who this course is for:

Machine Learning and Data Science enthusiastic.

Machine Learning with Python from Styrish AI

What you'll learn

Explore related topics

Course content

Introduction to Machine Learning1 lecture • 3min

Supervised vs. Unsupervised Learning1 lecture • 6min

Classification7 lectures • 38min

Regression3 lectures • 16min

Clustering2 lectures • 9min

Requirements

Description

Who this course is for: