Introduction To Machine Learning with Python for beginners

Introduction To Machine Learning with Python for beginners, available at $49.99, has an average rating of 4.1, with 33 lectures, 7 quizzes, based on 31 reviews, and has 131 subscribers.

You will learn about Fundamentals of Python Fundamentals of Machine Learning Learn machine learning, its algorithms and application Machine Learning Workflow Learn about Python Packages for Machine Learning Classification, regression, clustering, anomaly detection Exploratory Data Analysis and Visualization How machines learn from data Different types of machine learning models and how to choose among them Supervised, unsupervised, reinforcement, and transfer learning How to collect and prepare data suitable for training and testing machine learning models This course is ideal for individuals who are Anyone who wants to learn about Machine Learning and Python or Software Engineers or IT operations or Technical managers It is particularly useful for Anyone who wants to learn about Machine Learning and Python or Software Engineers or IT operations or Technical managers.

Enroll now: Introduction To Machine Learning with Python for beginners

Summary

Title: Introduction To Machine Learning with Python for beginners

Price: $49.99

Average Rating: 4.1

Number of Lectures: 33

Number of Quizzes: 7

Number of Published Lectures: 33

Number of Published Quizzes: 7

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Fundamentals of Python
Fundamentals of Machine Learning
Learn machine learning, its algorithms and application
Machine Learning Workflow
Learn about Python Packages for Machine Learning
Classification, regression, clustering, anomaly detection
Exploratory Data Analysis and Visualization
How machines learn from data
Different types of machine learning models and how to choose among them
Supervised, unsupervised, reinforcement, and transfer learning
How to collect and prepare data suitable for training and testing machine learning models

Who Should Attend

Anyone who wants to learn about Machine Learning and Python
Software Engineers
IT operations
Technical managers

Target Audiences

Anyone who wants to learn about Machine Learning and Python
Software Engineers
IT operations
Technical managers

This Complete Beginners Machine Learning Course – is a carefully designed course for absolute beginners to intermediate level audiences. The course is designed visually with interesting and clear code examples that anybody can take this course even without any prior programming experience. First few modules are designed to enable audiences to understand the foundational topics of Machine Learning (i.e., ML tools, techniques, Maths behind ML). Once students get the grip on ML, then they are taken to the Python and ML world. You can learn the course at your pace and practice the exercises provided at the end of the topics

Each section of the course is linked to the previous one in terms of utilizing what was already learned and each topic is supplied with lots of examples which will help students in their process of learning.

Throughout the course, the code examples are demonstrated using the popular tool Jupyter Notebook.

We recommend you to download the latest version (3.6) of Python from the Anaconda Distribution website covered in this course.

If you have any suggestions on topics that have not been covered, you can send them via private message. I will do my best to cover them as soon as possible.

Course Curriculum

Chapter 1: INTRODUCTION TO MACHINE LEARNING

Lecture 1: What is Machine Learning?

Lecture 2: Traditional Approach vs. Machine Learning

Lecture 3: Machine Learning Workflow

Lecture 4: Applications of Machine Learning

Chapter 2: TYPES OF MACHINE LEARNING

Lecture 1: Supervised Learning

Lecture 2: Unsupervised Learning

Lecture 3: Reinforcement Learning

Lecture 4: Artificial Neural Network (ANN)

Chapter 3: ML MODELS AND APPLICATIONS

Lecture 1: Supervised Learning

Lecture 2: Unsupervised Learning

Lecture 3: Reinforcement Learning

Chapter 4: SETTING UP PYTHON ENVIRONMENT

Lecture 1: Installing Python on Windows

Lecture 2: Installing Python on Linux

Lecture 3: Installing Python on Mac

Lecture 4: Installing Anaconda

Chapter 5: DATA ACQUIRING

Lecture 1: Data Acquiring

Lecture 2: Data Formats

Lecture 3: Importing Data Sets from Public Sources

Chapter 6: EXPLORATORY DATA ANALYSIS AND DATA CLEANING (LAB)

Lecture 1: What is Exploratory Data Analysis (EDA)?

Lecture 2: Understanding Data through Visualization

Lecture 3: Handling Missing Values

Lecture 4: Handling Outliers

Lecture 5: Data Standardization

Lecture 6: Data Normalization

Lecture 7: Lab – Section 6

Chapter 7: MODEL SELECTION AND TRAINING (LAB)

Lecture 1: Model Selection

Lecture 2: Training Model

Lecture 3: Lab – Section 7

Chapter 8: EVALUATING MODEL

Lecture 1: K-fold

Lecture 2: Accuracy and Precision

Lecture 3: Lab – Section 8

Chapter 9: DEPLOYING MODEL

Lecture 1: Deploying ML Model

Chapter 10: CAPSTONE PROJECT

Lecture 1: Lab – Capstone Project

Instructors

SKILL CURB
TECHNOLOGY MADE EASY

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 3 votes
4 stars: 12 votes
5 stars: 15 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!

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