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.

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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:

Part 1 – Data Preprocessing

Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 – Clustering: K-Means, Hierarchical Clustering

Part 5 – Association Rule Learning: Apriori, Eclat

Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA

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):

CODES ALL UP TO DATE

DEEP LEARNING CODED IN TENSORFLOW 2.0

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

Anand Kumar
Senior Data Scientist

Rating Distribution

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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!