Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.
This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.
You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.
With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.
By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.
Overview
Syllabus
- Introduction to Machine Learning
- In this module, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.
- Regression
- In this module, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.
- Classification
- In this module, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.
- Linear Classification
- Clustering
- In this module, you will learn about clustering specifically k-means clustering. You learn how the k-means clustering algorithm works and how to use k-means clustering for customer segmentation.
- Final Exam and Project
- In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.
Taught by
SAEED AGHABOZORGI
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Reviews
4.2 rating, based on 4 Class Central reviews
4.7 rating at Coursera based on 16394 ratings
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I really liked this one. One of the best IBM data science courses available. It introduces broad list of subjects and provides some simple code to help you start building your own solutions. Recommend!
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The ML course in Python offers a comprehensive introduction to machine learning, covering essential topics such as data preprocessing, model selection, and evaluation metrics. With hands-on coding exercises and real-world projects, it effectively bridges the gap between theory and practice. The course structure is well-organized, progressively building on concepts to enhance understanding. The instructors provide clear explanations and are responsive to questions, ensuring a supportive learning environment. Overall, it's an excellent resource for anyone looking to gain practical skills in machine learning using Python.
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Teerath Kumar MenghwarAmazing course. Well organised. Need to add more things.
I have not got certificate. But let's see, when I can get certificate. -
Mehdi EbrahimiInstructor is clear and know what he is doing. course videos covers basic info and techniques of machine learning, further instructions are taught in lab assignments.
