Applied Machine Learning With Python
Applied Machine Learning With Python, available at $19.99, has an average rating of 5, with 17 lectures, based on 4 reviews, and has 23 subscribers.
You will learn about Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Clustering: K-Means, Hierarchical Clustering Deep Learning: Artificial Neural Networks, Convolutional Neural Networks This course is ideal for individuals who are Just some high school mathematics level and Working professionals also It is particularly useful for Just some high school mathematics level and Working professionals also.
Enroll now: Applied Machine Learning With Python
Summary
Title: Applied Machine Learning With Python
Price: $19.99
Average Rating: 5
Number of Lectures: 17
Number of Published Lectures: 17
Number of Curriculum Items: 17
Number of Published Curriculum Objects: 17
Original Price: $27.99
Quality Status: approved
Status: Live
What You Will Learn
- Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Clustering: K-Means, Hierarchical Clustering
- Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Who Should Attend
- Just some high school mathematics level and Working professionals also
Target Audiences
- Just some high school mathematics level and Working professionals also
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:
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Part 1 – Data Preprocessing
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Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
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Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
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Part 4 – Clustering: K-Means, Hierarchical Clustering
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Part 5 – Association Rule Learning: Apriori, Eclat
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Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
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Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
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Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
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Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
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Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Important updates (June 2020):
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CODES ALL UP TO DATE
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DEEP LEARNING CODED IN TENSORFLOW 2.0
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TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Mindset
Lecture 1: Mindset
Lecture 2: Approach to ML Building
Chapter 3: Machine Learning in MS Excel
Lecture 1: Linear Regression
Lecture 2: Linear Regression in MS Excel
Lecture 3: Common Video for Logistic Regression (MS Excel, R Lang., PYTHON)
Lecture 4: Limitations of Machine Learning in MS Excel
Chapter 4: Machine Learning in Python
Lecture 1: Introduction to Machine Learning with Python
Lecture 2: Linear Regression With Python
Lecture 3: Logistic Regression In Python
Lecture 4: KNN With Python
Lecture 5: SVM With Python
Lecture 6: Tree based Algorithm Theory
Lecture 7: DT With Python
Lecture 8: RF With Python
Lecture 9: GBM With Python
Lecture 10: Important Evaluation Metrics
Instructors
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Anand Kumar
Senior Data Scientist
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 0 votes
- 5 stars: 4 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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