Machine Learning with Python- The Complete Guide

Machine Learning with Python: The Complete Guide, available at $34.99, has an average rating of 3.85, with 65 lectures, based on 10 reviews, and has 333 subscribers.

You will learn about Learn core concepts of machine learning with python Learn to implement Ml algorithms Learn to craft ML models and solutions for real world problems This course is ideal for individuals who are Anyone who wants to get started on Machine learning and AI will find this course very useful It is particularly useful for Anyone who wants to get started on Machine learning and AI will find this course very useful.

Enroll now: Machine Learning with Python: The Complete Guide

Summary

Title: Machine Learning with Python: The Complete Guide

Price: $34.99

Average Rating: 3.85

Number of Lectures: 65

Number of Published Lectures: 65

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 65

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Learn core concepts of machine learning with python

Learn to implement Ml algorithms

Learn to craft ML models and solutions for real world problems

Who Should Attend

Anyone who wants to get started on Machine learning and AI will find this course very useful

Target Audiences

Anyone who wants to get started on Machine learning and AI will find this course very useful

Machine learning is on the rise with the explosion of technologies. As more people are drawn to this field, the outcomes are diversifying immensely.

Machine learning as stated by Tom M. Mitchell from Carnegie Mellon University is- “The study of computer algorithms that improve automatically through experience”. The major difference between the two is that AI focuses on the overall aspect of a subject while machine learning narrows it down and focuses on any of it and over time, improves on it.

People are enticed by this field and they are huddling together to learn in depth about it. One of the key essentials to get accustomed to its features by using Python. Python is the easiest and the most popular programming language by far and learning it couldn’t be easier! Keeping in mind these factors, we have developed a course that addresses the growing need for machine learning enthusiasts.

Why Should I Choose this Course?

I couldn’t emphasize enough on the opportunities that awaits you! This course explains machine learning with all the fundamentals. If you are unaware of the basic terminologies for ML then don’t worry, we got you covered. Our course covers the basics of the ML as well as all the advanced concepts. Unlike a vast amount of courses, we also teach the crucial aspects of Python. Machine learning without knowing Python is of as much use as a hammer made of glass.

What makes this course so valuable?

The course is inclusive of all the topics you need to know to become proficient. This guide unfolds with the basic introduction to machine learning and its applications. Furthermore, you’ll also get to know how Python plays the role of a catalyst and also learn the subject closely. Also, get yourself known to the best practices of data sciences such as validation techniques and understanding over/under-fitting.

The Course contains:

Introduction of machine learning

Important concepts related to machine learning

Types of machine learning

Detailed analysis of types of machine learning

Get to know the concepts of supervised and unsupervised learning, neural networks, reinforced learning, etc

and Much More!

So, if you envision a career in machine learning, this course is the perfect match for you!

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: Course Introduction

Lecture 2: Installing Dependencies

Lecture 3: Introduction to Supervised Learning

Lecture 4: Introduction to Unsupervised and Reinformcement Learning

Lecture 5: Introduction to Deep Learning

Chapter 2: Linear Regression with Machine Learning

Lecture 1: Introduction to Linear Regression using Machine Learning

Lecture 2: Understanding the Linear Regression Process

Lecture 3: Coding a Linear Regression with Machine Learning Model

Lecture 4: Visualizing Linear Regression with Machine Learning

Lecture 5: Problem

Lecture 6: Answer

Chapter 3: Random Forest Modeling

Lecture 1: Introduction to Random Forest Models

Lecture 2: Understanding Decision Trees

Lecture 3: Coding a Random Forest Model

Lecture 4: Visualizing a Random Forest Decision Tree

Lecture 5: Problem

Lecture 6: Answer

Chapter 4: Support Vector Machines

Lecture 1: Introduction to Support Vector Machines

Lecture 2: Understanding the SVM Kernel

Lecture 3: Coding a SVM Model

Lecture 4: Visualizing Classification Boundaries

Lecture 5: Problem

Lecture 6: Answer

Chapter 5: Naive Bayes

Lecture 1: Introduction to Naive Bayes

Lecture 2: Understanding Bayesian Probability

Lecture 3: Coding with Natural Language Processing

Lecture 4: Building Naive Bayes Classifier with NLP

Lecture 5: Problem

Lecture 6: Answer

Chapter 6: Validation Techniques

Lecture 1: Over and Under fitting

Lecture 2: Cross Validation Techinques

Lecture 3: Coding Cross Validation techniques

Lecture 4: Problem

Lecture 5: Answer

Chapter 7: K-Nearest Neighbors

Lecture 1: Introduction to KNNs

Lecture 2: Distance Measurements and KNNs

Lecture 3: Building a KNN Model

Lecture 4: Calculating Squared Error and Learning with KNN

Lecture 5: Problem

Lecture 6: Answer

Chapter 8: K-Means Clustering

Lecture 1: Introduction to Unsupervised Learning and K-Means Clustering

Lecture 2: Introduction to Heirarchical K-Means Clustering

Lecture 3: Building a K-Means Clustering Model

Lecture 4: Visualizing a Dendrogram

Chapter 9: Hidden Markov Models

Lecture 1: Introduction to Markov Chains

Lecture 2: Introduction to Latent Variables and HMM

Lecture 3: Coding a simple HMM

Chapter 10: Gaussian Mixture Models

Lecture 1: Introduction to GMMs and Distributions

Lecture 2: GMMs and Joint Probability Distributions

Lecture 3: Building a Simple GMM

Lecture 4: Visualizing Boundary Spaces with GMMs

Lecture 5: Problem

Lecture 6: Answer

Chapter 11: Collaborative Filtering

Lecture 1: Introduction to Collaborative Filtering

Lecture 2: Introduction to Model-Based CFs and Matrix Factorization

Lecture 3: Building a Memory Based CF Model

Lecture 4: Building a Model Based CF Model

Lecture 5: Problem

Lecture 6: Answer

Chapter 12: Project 1

Lecture 1: Problem

Lecture 2: Answer

Chapter 13: Project 2

Lecture 1: Problem

Lecture 2: Answer

Chapter 14: Project 3

Lecture 1: Problem

Lecture 2: Answer

Instructors

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

Eduonix-Tech .

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2 stars: 1 votes

3 stars: 0 votes

4 stars: 6 votes

5 stars: 2 votes

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