Machine Learning in Python

Name: Machine Learning in Python Course
Price: 36 USD

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Master advanced statistical techniques and predictive modeling with Python. Acquire the essential skills for aspiring data scientists.

7 hours of content 45819 students

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What you get:

7 hours of content
92 Interactive exercises
12 Coding exercises
133 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Machine Learning in Python

A course by Iliya Valchanov

Start for Free

What you get:

7 hours of content
92 Interactive exercises
12 Coding exercises
133 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

$99.00

Lifetime access

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Start for Free

What you get:

7 hours of content
92 Interactive exercises
12 Coding exercises
133 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Master foundational machine learning techniques that will take your data analysis skills to the next level
Build a strong foundation with in-depth understanding of linear regression and set the stage for advance machine learning models
Gain hands-on experience by performing linear regression with sklearn
Master logistic regression, a critical analysis tool for binary ML problems
Implement K-means clustering and learn how to leverage clustering techniques in a real-world environment
Integrate math concepts with hands-on programming skills

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Machine Learning in Python builds upon the statistical knowledge you gained earlier in the program. This course focuses on predictive modelling and enters multidimensional spaces which require an understanding of mathematical methods, transformations, and distributions. We will introduce these concepts, as well as complex means of analysis such as clustering, factoring, Bayesian inference, and decision theory, while also allowing you to exercise your Python programming skills.

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1.1 Course Introduction

1 min

1.3 The linear regression model

6 min

1.5 Correlation vs regression

2 min

1.6 Geometrical representation of the Linear Regression Model

1 min

1.8 Setting up the Environment

1 min

1.9 Python packages installation

5 min

Curriculum

1. Linear Regression

23 Lessons 85 Min

In this part of the course, we will discuss what the course covers, why you need to learn advanced statistics, what’s the differences are with machine learning, and how to get the most out of this training. In this section, you will also expand on what you learned in our statistics training with additional concepts and will apply all the theory in Python. This section serves two purposes: 1) a useful refresher of regression, and 2) a great way to reinforce what you have learned, applying it in practice while coding.

Course Introduction
1 min
The linear regression model
6 min
Correlation vs regression
2 min
Geometrical representation of the Linear Regression Model
1 min
Setting up the Environment Read now
1 min
Python packages installation
5 min
First regression in Python
7 min
Using Seaborn for graphs
1 min
How to interpret the regression table
6 min
Decomposition of variability
4 min
What is the OLS?
3 min
R-squared
6 min
Multiple linear regression Theory
3 min
Adjusted R-squared Theory
6 min
F-test
2 min
OLS assumptions
2 min
A1: Linearity
2 min
A2: No endogeneity
4 min
A3: Normality and homoscedasticity
6 min
A4: No autocorrelation
4 min
A5: No multicollinearity
3 min
Dealing with categorical data - Dummy variables
7 min
Making predictions with the linear regression
3 min
2. Linear Regression with sklearn

15 Lessons 57 Min

While there are many libraries that can compute a regression model, the most numerically stable one is sklearn. It is also the preferred choice of many machine learning professionals. In this section, we implement all we know about regressions in this amazing library.

What is sklearn
2 min
Game plan for sklearn
2 min
Simple linear regression
6 min
Simple linear regression - summary table
5 min
A Note on Normalization Read now
1 min
Multiple linear regression
3 min
Adjusted R-squared
5 min
Feature Selection through p-values
5 min
Creating a summary table
2 min
A Note on Calculation of P-Values with sklearn Read now
1 min
Feature Scaling
6 min
Feature Selection through standardization
5 min
Making predictions with standardized coefficients
4 min
Underfitting and overfitting
3 min
Training and testing
7 min
3. Linear Regression Practical Example

6 Lessons 38 Min

An all in one practical example, which will test your understanding of each of the concepts that we have discussed so far. We will focus on a used cars dataset and create a linear regression model to predict the prices of cars. At the end you will have a big assignment where you can dive deep into the optimization of a machine learning model.

Linear Regression Practical Example (Part 1)
12 min
Linear Regression Practical Example (Part 2)
6 min
A note on multicollinearity Read now
1 min
Linear Regression Practical Example (Part 3)
3 min
Linear Regression Practical Example (Part 4)
8 min
Linear Regression Practical Example (Part 5)
8 min
4. Logistic Regression

11 Lessons 41 Min

Data scientists use logistic regressions when the dependent variable is binary (0 and 1, true and false, etc.). This type of data is encountered on a daily basis when working as a data scientist, and here, you will learn how to build a logistic regression, understand tables, interpret the coefficients of a logistic regression, calculate the accuracy of the model, as well as how to test. We will introduce under and overfitting, and will teach you how to test your models.

Introduction to logistic regression
1 min
A simple example in Python
5 min
Logistic vs logit function
4 min
Building a logistic regression
3 min
An invaluable coding tip
2 min
Understanding the tables
4 min
What do the odds actually mean
5 min
Binary predictors in a logistic regression
5 min
Calculating the accuracy of the model
3 min
The concept of overfitting
4 min
Testing the model
5 min
5. Cluster Analysis (Basics and Prerequisites)

4 Lessons 15 Min

Cluster analysis is the most intuitive and important example of unsupervised learning. However, to be able to understand cluster analysis, we must first explore the mathematics behind it.

Introduction to Cluster Analysis
4 min
Some examples of clusters
5 min
Difference between classification and clustering
3 min
Math prerequisites
3 min
6. K-Means Clustering

10 Lessons 50 Min

In this section, you will learn how to do Cluster analysis. Cluster analysis consists in dividing your data into separate groups based on an algorithm. Clustering is an amazing technique often employed in data science. But what’s more, it makes much more sense to study patterns observed in a particular group rather than trying to find patterns in the entire dataset. We will provide several practical examples that will help you understand how to carry out cluster analysis and the difference between classification and clustering.

K-means clustering
5 min
A simple example of clustering
8 min
Clustering categorical data
3 min
How to choose the number of clusters
6 min
Pros and Cons of K-means and clustering
3 min
To standardize or to not standardize
5 min
Relationship between clustering and regression
2 min
Market Segmentation with Cluster Analysis (Part 1)
6 min
Market Segmentation with Cluster Analysis (Part 2)
7 min
How is clustering useful
5 min
7. Other Types of Clustering

3 Lessons 14 Min

In previous sections, we focus extensively on k-means clustering, as it is the fastest and most efficient method for clustering. In this section, we explore other approaches that are less common.

Types of clustering
4 min
Dendrogram
5 min
Heatmaps using Seaborn
5 min

Topics

Machine LearningProgrammingPythonTheoryData AnalysisMachine and Deep LearningData Preprocessing

Tools & Technologies

Course Requirements

Highly recommended to take the Intro to Python, Statistics, Math, and Probability courses first
You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Intermediate

Aspiring data scientists and ML engineers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Iliya Valchanov

Co-founder at

7 Courses

28002 Reviews

439103 Students

Iliya Valchanov is a co-founder of 365 Data Science and 3veta. He is a Finance graduate with a wide range of expertise in the fields of mathematics, statistics, programming, machine learning, and deep learning. In his courses, Iliya shares his extensive experience in predictive modeling, complex analysis techniques, and optimization algorithms. He has a BA in International Economics, Management and Finance from Bocconi University, where he was Founder and President of the Bocconi Students Mathematics and Logics Association. In 2016, he created his first online course (Statistics) and realized he enjoyed the process of content creation so much that he co-founded 365 Data Science together with a group of friends from university.