Kleio T.
After 365:
Bioinformatics and Neuroinformatics student
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Online Course
free
Machine Learning in Python
with
Iliya Valchanov
Master advanced statistical techniques and predictive modeling with Python. Acquire the essential skills for aspiring data scientists.
47235 students already have enrolled
Skill level:
Intermediate
Duration:
7 hours
-
Lessons (5 hours)
-
Practice exams (1.5 hours)
- Lessons (5 hours)
- Practice exams (1.5 hours)
CPE credits:
11
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master foundational machine learning techniques for data analysis.
- Build a strong base in linear regression for advanced ML models.
- Perform linear regression with sklearn in hands-on exercises.
- Master logistic regression for binary classification problems.
- Implement K-means clustering for real-world applications.
Topics & tools
machine learningprogrammingpythontheorydata analysismachine and deep learningdata preprocessing
Your instructor
CEO of Team-GPT | AI Educator | Co-founder of 365 Data Science & 3veta
Track record
Bringing real-world expertise from leading global companies
Academic background
Bachelor's degree, International Economics, Management, and Finance
Course OVERVIEW
Description
CPE Credits: 11
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
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.
Prerequisites
- Python (version 3.8 or later), Pinecone account and API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
- Intermediate Python skills are required.
- Familiarity with basic statistics and linear algebra is helpful but not mandatory.
Advanced preparation
Curriculum
72 lessons
104 exercises
5 exams
- 2. Linear Regression with sklearn57 MinWhile 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 FreeGame plan for sklearn FreeSimple linear regression FreeSimple linear regression - summary table FreeCoding exercise FreeA Note on Normalization FreeExercise FreeMultiple linear regression FreeCoding exercise FreeAdjusted R-squared FreeCoding exercise FreeFeature Selection through p-values FreeCreating a summary table FreeCoding exercise FreeA Note on Calculation of P-Values with sklearn FreeExercise FreeFeature Scaling FreeCoding exercise FreeFeature Selection through standardization FreeCoding exercise FreeMaking predictions with standardized coefficients FreeCoding exercise FreeUnderfitting and overfitting FreeTraining and testing FreeExercise Free
- 3. Linear Regression Practical Example38 MinAn 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) FreeLinear Regression Practical Example (Part 2) FreeA note on multicollinearity FreeLinear Regression Practical Example (Part 3) FreeLinear Regression Practical Example (Part 4) FreeLinear Regression Practical Example (Part 5) FreePractice exam Free
- 4. Logistic Regression41 MinData 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 FreeA simple example in Python FreeLogistic vs logit function FreeExercise FreeBuilding a logistic regression FreeAn invaluable coding tip FreeUnderstanding the tables FreeExercise FreeWhat do the odds actually mean FreeBinary predictors in a logistic regression FreeCalculating the accuracy of the model FreeThe concept of overfitting FreeTesting the model FreeExercise FreePractice exam Free
- 5. Cluster Analysis (Basics and Prerequisites)15 MinCluster 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.
- 6. K-Means Clustering50 MinIn 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 FreeA simple example of clustering FreeClustering categorical data FreeHow to choose the number of clusters FreeExercise FreePros and Cons of K-means and clustering FreeTo standardize or to not standardize FreeRelationship between clustering and regression FreeExercise FreeMarket Segmentation with Cluster Analysis (Part 1) FreeMarket Segmentation with Cluster Analysis (Part 2) FreeHow is clustering useful Free
- 7. Other Types of Clustering14 MinIn 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.
- 8. Course exam110 Min
Free preview
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
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of AI and data science graduates
successfully change
or advance their careers.
96%
of our students recommend
365 Data Science.
$29,000
average salary increase
after moving to an AI and data science career
ACCREDITED certificates
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