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:
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Introduction of machine learning
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Important concepts related to machine learning
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Types of machine learning
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Detailed analysis of types of machine learning
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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
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Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses -
Eduonix-Tech .
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 0 votes
- 4 stars: 6 votes
- 5 stars: 2 votes
Frequently Asked Questions
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