Machine Learning Classification Bootcamp in Python
Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?.
You came to the right place.
Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale. The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020.
This course provides students with knowledge, hands-on experience of state-of-the-art machine learning classification techniques such as
Logistic Regression
Decision Trees
Random Forest
Naïve Bayes
Support Vector Machines (SVM)
In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 10 projects from scratch using real world dataset, here’s a sample of the projects we will be working on:
Build an e-mail spam classifier.
Perform sentiment analysis and analyze customer reviews for Amazon Alexa products.
Predict the survival rates of the titanic based on the passenger features.
Predict customer behavior towards targeted marketing ads on Facebook.
Predicting bank client’s eligibility to retire given their features such as age and 401K savings.
Predict cancer and Kyphosis diseases.
Detect fraud in credit card transactions.
Key Course Highlights:
This comprehensive machine learning course includes over 75 HD video lectures with over 11 hours of video content.
The course contains 10 practical hands-on python coding projects that students can add to their portfolio of projects.
No intimidating mathematics, we will cover the theory and intuition in clear, simple and easy way.
All Jupyter noteboooks (codes) and slides are provided.
10+ years of experience in machine learning and deep learning in both academic and industrial settings have been compiled in this course.
Students who enroll in this course will master machine learning classification models and can directly apply these skills to solve real world challenging problems.
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| Rating | 4.1★ based on 67 ratings |
|---|---|
| Length | 11.5 total hours |
| Starts | On Demand (Start anytime) |
| Cost | $14 |
| From | Udemy |
| Instructors | Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, Mitchell Bouchard, SuperDataScience Team, Ligency I Team |
| Download Videos | Only via the Udemy mobile app |
| Language | English |
| Subjects | Data Science Business |
| Tags | Data Science Business Development Data & Analytics |
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What people are saying
machine learning
Great Course to Begin with but you have to learn much more before thinking to land for a job in this field of machine learning Nice course with detailed explanation looking forward for other courses The course explains all the details which involves the learner in the very exact way....
Great course to practice Machine Learning classification models!
Machine learning is about how to build these modules from scratch, understanding every single detail and mathematics explanations behind it, not just using the tools of other people created without understanding it completely.
Very good explanation of Machine Learning The second project in each section could be better.
Read more
very good
All in all, very good course and would love to see a dedicated NLP course from this teacher.
Very good to explain about Classification Model in Python They have a clean instruction how the course will be going forward and how everything will be done.
Well explained with very good practical examples.
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so far
very clear explanation so far Good Course for Beginners Very clear, very practical.
So far going good.. let me continue...
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well explained
choice of hyper-parameter, feature engineering) it is well explained and gives a very general idea of the model.
Key concepts are well explained and only the essential amount of detail is given to move forward.
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logistic regression
Why scale the training set and test set together (Logistic regression)?
The teacher gives a very good picture of artificial intelligence at the beginning, allowing students to understands most of the buzz words related to this area, such as machine learning, deep learning, KNN, Naive bayes algorithm, logistic regression, ... Also, what my favorite thing in this course was that the teacher spent a lot of time to explain the intuition behind each algorithm, so that when we dive into code, we actually know what we are doing, at least at an abstract level (we don't go too far into mathematics, but we get the idea in simple terms).
So far in the logistic regression section, I have not really seen any specific methods to the tune the models or implement any robust feature engineering to really make an impact.
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dr. ryan
Amazing job of Dr. Ryan Ahmed.
I wait more course form Dr. Ryan Ahmed.
Read more
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Research Scientist-Machine Learning $55k
Cloud Architect - Azure / Machine Learning $75k
Watson Machine Learning Engineer $81k
Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist - Machine Learning -... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
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| Rating | 4.1★ based on 67 ratings |
|---|---|
| Length | 11.5 total hours |
| Starts | On Demand (Start anytime) |
| Cost | $14 |
| From | Udemy |
| Instructors | Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, Mitchell Bouchard, SuperDataScience Team, Ligency I Team |
| Download Videos | Only via the Udemy mobile app |
| Language | English |
| Subjects | Data Science Business |
| Tags | Data Science Business Development Data & Analytics |
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