Machine Learning Unleashed: Mastering Algorithms and Models

Machine Learning Unleashed: Mastering Algorithms and Models, available at $39.99, has an average rating of 3.8, with 30 lectures, 1 quizzes, based on 117 reviews, and has 24760 subscribers.

You will learn about Deep dive into the world of Machine Learning (ML) Apply Python for Machine Learning programs Understand what is ML, need for ML, challenges & application of ML in real-life scenarios Types of Machine Learning Components of Python ML Ecosystem Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn Regression analysis scikit-learn Library to implement Simple Linear Regression Multiple Linear Regression and Polynomial Regression Logistic Regression What is Classification, Classification Terminologies in Machine Learning What is KNN? How does the KNN algorithm work? What is a Decision Tree and Implementation of Decision Tree SVM and its implementation What is Clustering and Applications of Clustering Clustering Algorithms K-Means Clustering and K-Means Clustering algorithm example Hierarchical Clustering Agglomerative Hierarchical clustering and how does it work Woking of Dendrogram in Hierarchical clustering Implementation of Agglomerative Hierarchical Clustering Association Rule Learning Apriori algorithm and Implementation of Apriori algorithm Introduction to Recommender Systems Content-based Filtering Collaborative Filtering Implementation of Movie Recommender System This course is ideal for individuals who are Data Scientists and Senior Data Scientists or Machine Learning Scientists or Python Programmers & Developers or Machine Learning Software Engineers & Developers or Computer Vision Machine Learning Engineers or Beginners and newbies aspiring for a career in Data Science and Machine Learning or Principal Machine Learning Engineers or Machine Learning Researchers & Enthusiasts or Anyone interested to learn Data Science, Machine Learning programming through Python or AI Specialists & Consultants or Python Engineers Machine Learning Ai Data Science or Data, Analytics, AI Consultants & Analysts or Machine Learning Analysts It is particularly useful for Data Scientists and Senior Data Scientists or Machine Learning Scientists or Python Programmers & Developers or Machine Learning Software Engineers & Developers or Computer Vision Machine Learning Engineers or Beginners and newbies aspiring for a career in Data Science and Machine Learning or Principal Machine Learning Engineers or Machine Learning Researchers & Enthusiasts or Anyone interested to learn Data Science, Machine Learning programming through Python or AI Specialists & Consultants or Python Engineers Machine Learning Ai Data Science or Data, Analytics, AI Consultants & Analysts or Machine Learning Analysts.

Enroll now: Machine Learning Unleashed: Mastering Algorithms and Models

Summary

Title: Machine Learning Unleashed: Mastering Algorithms and Models

Price: $39.99

Average Rating: 3.8

Number of Lectures: 30

Number of Quizzes: 1

Number of Published Lectures: 30

Number of Published Quizzes: 1

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Deep dive into the world of Machine Learning (ML)
Apply Python for Machine Learning programs
Understand what is ML, need for ML, challenges & application of ML in real-life scenarios
Types of Machine Learning
Components of Python ML Ecosystem
Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn
Regression analysis
scikit-learn Library to implement Simple Linear Regression
Multiple Linear Regression and Polynomial Regression
Logistic Regression
What is Classification, Classification Terminologies in Machine Learning
What is KNN? How does the KNN algorithm work?
What is a Decision Tree and Implementation of Decision Tree
SVM and its implementation
What is Clustering and Applications of Clustering
Clustering Algorithms
K-Means Clustering and K-Means Clustering algorithm example
Hierarchical Clustering
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
Association Rule Learning
Apriori algorithm and Implementation of Apriori algorithm
Introduction to Recommender Systems
Content-based Filtering
Collaborative Filtering
Implementation of Movie Recommender System

Who Should Attend

Data Scientists and Senior Data Scientists
Machine Learning Scientists
Python Programmers & Developers
Machine Learning Software Engineers & Developers
Computer Vision Machine Learning Engineers
Beginners and newbies aspiring for a career in Data Science and Machine Learning
Principal Machine Learning Engineers
Machine Learning Researchers & Enthusiasts
Anyone interested to learn Data Science, Machine Learning programming through Python
AI Specialists & Consultants
Python Engineers Machine Learning Ai Data Science
Data, Analytics, AI Consultants & Analysts
Machine Learning Analysts

Target Audiences

Data Scientists and Senior Data Scientists
Machine Learning Scientists
Python Programmers & Developers
Machine Learning Software Engineers & Developers
Computer Vision Machine Learning Engineers
Beginners and newbies aspiring for a career in Data Science and Machine Learning
Principal Machine Learning Engineers
Machine Learning Researchers & Enthusiasts
Anyone interested to learn Data Science, Machine Learning programming through Python
AI Specialists & Consultants
Python Engineers Machine Learning Ai Data Science
Data, Analytics, AI Consultants & Analysts
Machine Learning Analysts

A warm welcome to the Machine Learning with Pythoncourse by Uplatz.

Machine learning is a branch of artificial intelligence (AI) that allows computers to learn without having to be explicitly programmed. Machine learning is concerned with the creation of computer programs that can adapt to new data. In this post, we’ll go through the fundamentals of machine learning and how to use Python to construct a simple machine learning algorithm. Many modules have been built by the Python community to assist programmers in implementing machine learning. The NumPy, SciPy, and scikit-learn modules will be used in this course.

Machine learning entails training a computer with a particular data set and then using that training to predict the characteristics of incoming data. Specialized algorithms are used in the training and prediction phase. The training data is sent into an algorithm, which then utilizes the training data to make predictions on fresh test data. Machine Learning (ML) is a branch of computer science that allows computers to make sense of data in the same manner that humans do. In simple terms, machine learning (ML) is a form of artificial intelligence that uses an algorithm or method to extract patterns from raw data. The goal of machine learning is to allow computers to learn from their experiences without having to be explicitly programmed or requiring human involvement.

Course Objectives

Recognize the range and depth of machine learning applications and use cases in real-world applications
Using Python libraries, import and wrangle data, then partition it into training and test datasets
Understand Machine Learning concepts and types of ML
Techniques for preparing data, such as univariate and multivariate analysis, missing values and outlier treatment, and so on
Learn Machine Learning algorithms – regression, classification, clustering, association
Implement various types of classification methods such as SVM, Naive bayes, decision tree, and random forest
Interpret unsupervised learning and learn to use clustering algorithms
Implement linear and polynomial regression, understand Ridge and lasso regression, and implement various types of classification methods such as SVM, Naive bayes, decision tree, and random forest
Overfitting avoidance, Bias-variance tradeoff, Minibatch, and Shuffling, ML solution tuning
Understand various types of Recommender Systems and start building your own!

Uplatzprovides this end-to-end training on Machine Learning using Python programming.

You’ll understand what machine learning is and what are the most prevalent approaches in the field are at the conclusion of this learning route. You’ll be able to construct genuine machine learning systems in Python thanks to hands-on lessons. With this Machine Learning course you will become proficient in Python and will see a gradual transition to data science. You will gain a firm grasp of what machine learning is, what the various approaches are, and what machine learning can really do. With this machine learning python training, you can learn how to deal with this new technology.

Graduates, postgraduates, and research students who are interested in this subject or have it as part of their curriculum can benefit from this lesson. The reader may be a novice or a seasoned student. This Machine Learning course has been designed to help students and professionals get up to speed fast. The Machine Learning with Python training serves as a starting point for your Machine Learning adventure.

Machine Learning with Python (beginner to guru) – Course Syllabus

1. Introduction to Machine Learning

What is Machine Learning?
Need for Machine Learning
Why & When to Make Machines Learn?
Challenges in Machines Learning
Application of Machine Learning

2. Types of Machine Learning

Types of Machine Learning

a) Supervised learning

b) Unsupervised learning

c) Reinforcement learning

Difference between Supervised and Unsupervised learning
Summary

3. Components of Python ML Ecosystem

Using Pre-packaged Python Distribution: Anaconda
Jupyter Notebook
NumPy
Pandas
Scikit-learn

4. Regression Analysis (Part-I)

Regression Analysis
Linear Regression
Examples on Linear Regression
scikit-learn library to implement simple linear regression

5. Regression Analysis (Part-II)

Multiple Linear Regression
Examples on Multiple Linear Regression
Polynomial Regression
Examples on Polynomial Regression

6. Classification (Part-I)

What is Classification
Classification Terminologies in Machine Learning
Types of Learner in Classification
Logistic Regression
Example on Logistic Regression

7. Classification (Part-II)

What is KNN?
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Implementation of KNN classifier
What is a Decision Tree?
Implementation of Decision Tree
SVM and its implementation

8. Clustering (Part-I)

What is Clustering?
Applications of Clustering
Clustering Algorithms
K-Means Clustering
How does K-Means Clustering work?
K-Means Clustering algorithm example

9. Clustering (Part-II)

Hierarchical Clustering
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering

10. Association Rule Learning

Association Rule Learning
Apriori algorithm
Working of Apriori algorithm
Implementation of Apriori algorithm

11. Recommender Systems

Introduction to Recommender Systems
Content-based Filtering
How Content-based Filtering work
Collaborative Filtering
Implementation of Movie Recommender System

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Chapter 2: Types of Machine Learning

Lecture 1: Types of Machine Learning – part 1

Lecture 2: Types of Machine Learning – part 2

Chapter 3: Components of Python ML Ecosystem

Lecture 1: Components of Python ML Ecosystem – part 1

Lecture 2: Components of Python ML Ecosystem – part 2

Lecture 3: Components of Python ML Ecosystem – part 3

Lecture 4: Components of Python ML Ecosystem – part 4

Chapter 4: Regression Analysis (I)

Lecture 1: Regression Analysis (I) – part 1

Lecture 2: Regression Analysis (I) – part 2

Lecture 3: Regression Analysis (I) – part 3

Lecture 4: Regression Analysis (I) – part 4

Chapter 5: Regression Analysis (II)

Lecture 1: Regression Analysis (II) – part 1

Lecture 2: Regression Analysis (II) – part 2

Lecture 3: Regression Analysis (II) – part 3

Chapter 6: Classification (I)

Lecture 1: Classification (I) – part 1

Lecture 2: Classification (I) – part 2

Chapter 7: Classification (II)

Lecture 1: Classification (II) – part 1

Lecture 2: Classification (II) – part 2

Lecture 3: Classification (II) – part 3

Lecture 4: Classification (II) – part 4

Chapter 8: Clustering (I)

Lecture 1: Clustering (I) – part 1

Lecture 2: Clustering (I) – part 2

Chapter 9: Clustering (II)

Lecture 1: Clustering (II) – part 1

Lecture 2: Clustering (II) – part 2

Chapter 10: Association Rule Learning

Lecture 1: Association Rule Learning – part 1

Lecture 2: Association Rule Learning – part 2

Chapter 11: Recommender Systems

Lecture 1: Recommender Systems – part 1

Lecture 2: Recommender Systems – part 2

Lecture 3: Recommender Systems – part 3

Lecture 4: Recommender Systems – part 4

Chapter 12: End of Course Quiz

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 3 votes
2 stars: 9 votes
3 stars: 27 votes
4 stars: 29 votes
5 stars: 49 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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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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