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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
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- Instructor Chat Support
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This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- You Will learn basic concept of Machine Learning, Types of Machine Learning
- You Will learn basic concept of Linear Regression With One Variable & Multiple
- You Will learn basic concept of Logistic Regression
- You Will learn basic concept of Regularization (Linear and Logistic Regression)
- You Will learn basic concept of Neural Network
Course Overview
Steps of Machine Learning that you Will learn:
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Import the data.
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Split data into Training & Test.
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Create a Model.
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Train The Model.
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Make Predictions.
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Evaluate and improve.
Machine Learning Course Contents:
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What is Machine Learning - Types of Machine Learning (Supervised & Unsupervised).
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Linear Regression with One Variable.
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Linear Regression with One Variable (Cost Function - Gradient Descent).
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Linear Regression with Multiple Variable.
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Logistic Regression (Classification).
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Logistic Regression (Cost Function - Gradient Descent).
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Logistic Regression (Multiclass).
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Regularization Overfitting.
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Regularization (Linear and Logistic Regression).
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Neural Network Overview.
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Neural Network (Cost Function).
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Advice for Applying Machine Leaning.
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Machine Learning Project 1
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Machine Learning Project 2
Python Basics Course Contents:
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How to print
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Variables
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Receive Input from User
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Type Conversion
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String
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Formatted String
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String Methods
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Arithmetic Operations
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Math Functions
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If Statement
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Logical Operators
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Comparison Operators
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While
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For Loops
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Nested Loops
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List
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2D List
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List Methods
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Tuples
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Unpacking
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Dictionaries
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Functions
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Parameters
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Keyword Arguments
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Return Statement
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Try - Except
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Comments
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Classes
Notes:
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You will Learn the basics of Machine Learning.
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You will learn the basics of python.
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You will need to setup Anaconda.
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You will need to setup python & PyCharm
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This course is considered as first step for the Machine Learning.
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You can ask anytime.
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No Programming Experience Needed for this course.
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Python for Data Science and Machine Learning is a great course that you can take to learn the implementation of ML models in Python.
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This course considered as step one in the Machine Leaning, You will learn the concept of the Machine Learning with python basics.
Pre-requisites
- Python Setup
- Anaconda
Target Audience
- Beginner in Machine Learning
Curriculum 50 Lectures 01:46:17
-
Section 1 : Machine Learning with Python Basics
- Lecture 2 :
- Linear Regression With One Variable
- Lecture 3 :
- Linear Regression With One Variable (Cost Function - Gradient Descent)
- Lecture 4 :
- Linear Regression with Multiple Variable
- Lecture 5 :
- Logistic Regression (Classification)
- Lecture 6 :
- Logistic Regression (Cost Function - Gradient Descent)
- Lecture 7 :
- Logistic Regression (Multiclass)
- Lecture 8 :
- Regularization Overfitting
- Lecture 9 :
- Regularization (Linear and Logistic Regression)
- Lecture 10 :
- Neural Network Overview
- Lecture 11 :
- Neural Network (Cost Function)
- Lecture 12 :
- Advice for Applying Machine Leaning
- Lecture 13 :
- Unsupervised Machine Learning
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Section 2 : Machine Learning Projects
- Lecture 1 :
- Anaconda Setup
- Lecture 2 :
- Machine Learning Project 1
- Lecture 3 :
- Machine Learning Project 2
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Section 3 : Python Basics
- Lecture 1 :
- How to print
- Lecture 2 :
- Variables
- Lecture 3 :
- Receive Input from User
- Lecture 4 :
- Type Conversion
- Lecture 5 :
- String
- Lecture 6 :
- Formatted String
- Lecture 7 :
- String Methods
- Lecture 8 :
- Arithmetic Operations
- Lecture 9 :
- Math Functions
- Lecture 10 :
- If Statement
- Lecture 11 :
- If Statement Another Example
- Lecture 12 :
- Logical Operators
- Lecture 13 :
- Comparison Operators
- Lecture 14 :
- While
- Lecture 15 :
- For Loops
- Lecture 16 :
- Nested Loops
- Lecture 17 :
- List
- Lecture 18 :
- 2D List
- Lecture 19 :
- List Methods
- Lecture 20 :
- Tuples
- Lecture 21 :
- Unpacking
- Lecture 22 :
- Dictionaries
- Lecture 23 :
- Functions
- Lecture 24 :
- Parameters
- Lecture 25 :
- Keyword Arguments
- Lecture 26 :
- Return Statement
- Lecture 27 :
- Try - Except
- Lecture 28 :
- Comments
- Lecture 29 :
- Classes
- Lecture 30 :
- General Review 1
- Lecture 31 :
- General Review 2
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Section 4 : Python Projects
- Lecture 1 :
- Python & PyCharm Setup
- Lecture 2 :
- Quiz Game
- Lecture 3 :
- Guessing Game
Our learners work at
Frequently Asked Questions
-
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It's simple. When you sign up, you'll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can learn and you can cancel at any time. -
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Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic. -
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Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on. -
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