Machine Learning in Python for Professionals

Machine Learning in Python for Professionals, available at $49.99, has an average rating of 5, with 77 lectures, 6 quizzes, based on 2 reviews, and has 109 subscribers.

You will learn about Learn professional machine learning and data science tools Learn the foundation algorithms for supervised and unsupervised learning Learn to build recommendation systems Learn reinforcement learning from ground up This course is ideal for individuals who are Anyone who wants to learn real world machine learning will find this course very useful It is particularly useful for Anyone who wants to learn real world machine learning will find this course very useful.

Enroll now: Machine Learning in Python for Professionals

Summary

Title: Machine Learning in Python for Professionals

Price: $49.99

Average Rating: 5

Number of Lectures: 77

Number of Quizzes: 6

Number of Published Lectures: 77

Number of Published Quizzes: 6

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 83

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Learn professional machine learning and data science tools

Learn the foundation algorithms for supervised and unsupervised learning

Learn to build recommendation systems

Learn reinforcement learning from ground up

Who Should Attend

Anyone who wants to learn real world machine learning will find this course very useful

Target Audiences

Anyone who wants to learn real world machine learning will find this course very useful

Do you want to learn advanced Python algorithms used by professional developers?

We have created a complete and updated advanced program in machine learning who want to build complex machine learning solutions. This course covers advanced Python algorithms, which will help you learn how Python allows its users to create their own Data Structures enables to have full control over the functionality of the models.

Let’s Have A Look At The Major Topics That This Course Will Cover!

Supervised Learning – Advanced Classification Models

Unsupervised Learning

Explainable Artificial Intelligence

Dimensionality Reduction

Recommendation Systems

Reinforcement Learning

We’ll be explaining each concept using real examples and easy coding techniques in Python using a Jupyter notebook and different environments. In this course, we’ll be covering topics that will help you learn how to use open-source packages, tools, and data sets to build supervised and unsupervised models.

At the end of this course, you’ll be having complete knowledge starting from the fundamentals of unsupervised techniques to advancing unsupervised techniques and supervised algorithms. These techniques will help you build efficient and reliable models. With this expert-curated course, you’ll surely be going to learn important tips that will help you become a complete data scientist.

Make your move now! Enroll in this course today and learn advanced algorithms to boost your career.

See You In The Class!

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Course Introduction

Chapter 2: Supervised Learning – Advanced Classification models

Lecture 1: Introduction

Lecture 2: Introduction to Ensemble Model

Lecture 3: Types of Ensemble Models – Bagging Model

Lecture 4: Types of Ensemble Models – Boosting Model

Lecture 5: Difference betweeen Bagging and Boosting Model

Lecture 6: Implementing Gradient Boosting Techniques

Lecture 7: Implementing Adaptive Boosting Technique

Lecture 8: Summary

Chapter 3: Unsupervised Learning

Lecture 1: Section Introduction

Lecture 2: Introduction to Unsupervised Learning

Lecture 3: Types of Clustering Techniques

Lecture 4: Introduction to K-means Clustering-1

Lecture 5: Introduction to K-means Clustering-2

Lecture 6: Determine the K-value in K-means Clustering

Lecture 7: Methods to Select K-value in K-means Clustering

Lecture 8: Implementing K-means Clustering Algorithm-1

Lecture 9: Implementing K-means Clustering Algorithm-2

Lecture 10: Optimizing K-means Algorithm

Lecture 11: Introduction to Hierarchical Clustering

Lecture 12: Compare Hierarchical Clustering

Lecture 13: Introduction to Divisive Hierarchical Clustering

Lecture 14: Summary

Chapter 4: Explainable Artificial Intelligence

Lecture 1: Section Introduction

Lecture 2: Introduction to Explainable Artificial Intelligence

Lecture 3: Need for Explainable AI

Lecture 4: Value of Explainable AI

Lecture 5: Techniques of Explainable

Lecture 6: Pros, Cons and Application – Shapley And Lime

Lecture 7: Challenges of Explainable AI

Lecture 8: Implementing XAI on Unsupervised Model

Lecture 9: Real Time Application of XAI

Lecture 10: Summary

Chapter 5: Dimensionality Reduction

Lecture 1: Section Introduction

Lecture 2: Introduction to Dimensionality Reduction

Lecture 3: Dimensionality Reduction – When and How

Lecture 4: Curse of Dimensionality

Lecture 5: Linear Methods of Dimensionality Reduction

Lecture 6: Introduction to Principal Component Analysis

Lecture 7: Principal Component Analysis – Advantages and Disadvantages

Lecture 8: Implementing PCA in Python

Lecture 9: Non-Linear Dimensionality Reduction – MDS

Lecture 10: Non-Linear Dimensionality Reduction – ISOMAP

Lecture 11: Non-Linear Dimensionality Reduction – t-SNE

Lecture 12: t-SNE – Pros, Cons and Application

Lecture 13: Summary

Chapter 6: Recommendation Systems

Lecture 1: Section Introduction

Lecture 2: What is Recommender System?

Lecture 3: Need for Recommender Systems

Lecture 4: Types of Recommender Models

Lecture 5: Content Based Recommendation System

Lecture 6: Working of Content Based Recommendation System – 1

Lecture 7: Working of Content Based Recommendation System – 2

Lecture 8: Types of Similarities – Content Based System

Lecture 9: Advantages and Disadvantages – Content Based System

Lecture 10: Implementing Content Based Recommender

Lecture 11: Collaborative Filtering Based Recommendation System

Lecture 12: Different Approaches in Collaborative Filtering

Lecture 13: Item Based Collaborative Filtering

Lecture 14: Matrix Factorization in Collaborative Filtering

Lecture 15: Advantages and Disadvantages – Collaborative Filtering

Lecture 16: Implementing Collaborative Filtering

Lecture 17: Difference Between Content and Collaborative Filtering

Lecture 18: Challenges with Recommendation System

Lecture 19: Summary

Chapter 7: Reinforcement Learning

Lecture 1: Section Introduction

Lecture 2: Introduction to Reinforcement Learning

Lecture 3: Need of Reinforcement Learning

Lecture 4: Components of Reinforcement Learning – 1

Lecture 5: Components of Reinforcement Learning – 2

Lecture 6: Q Learning Method – 1

Lecture 7: Q Learning Method – 2

Lecture 8: Types and Methods of Reinforcement Learning

Lecture 9: Advantages and Disadvantages of Reinforcement Learning

Lecture 10: Application of Reinforcement Learning

Lecture 11: Future of Reinforcement Learning

Lecture 12: Summary

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

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