Machine Learning Mastery (Integrated Theory+Practical HW)

Machine Learning Mastery (Integrated Theory+Practical HW), available at $19.99, has an average rating of 4.2, with 64 lectures, based on 13 reviews, and has 361 subscribers.

You will learn about Have an in-depth understanding of the concepts of Machine Learning Be able to grasp, understand, and write machine learning code from scratch Use Builtin Libraries available to build machine learning models Be able to analyze, build, and assess models on any dataset Be able to interpret and understand the black box behind model Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them. This course is ideal for individuals who are Curious about Data Science or People wishing to learn Machine Learning from scratch or People of different domains – Business Analyst, Marketing, etc or Seeking job in the areas of machine learning It is particularly useful for Curious about Data Science or People wishing to learn Machine Learning from scratch or People of different domains – Business Analyst, Marketing, etc or Seeking job in the areas of machine learning.

Enroll now: Machine Learning Mastery (Integrated Theory+Practical HW)

Summary

Title: Machine Learning Mastery (Integrated Theory+Practical HW)

Price: $19.99

Average Rating: 4.2

Number of Lectures: 64

Number of Published Lectures: 64

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 64

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will Learn

Have an in-depth understanding of the concepts of Machine Learning
Be able to grasp, understand, and write machine learning code from scratch
Use Builtin Libraries available to build machine learning models
Be able to analyze, build, and assess models on any dataset
Be able to interpret and understand the black box behind model
Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.

Who Should Attend

Curious about Data Science
People wishing to learn Machine Learning from scratch
People of different domains – Business Analyst, Marketing, etc
Seeking job in the areas of machine learning

Target Audiences

Curious about Data Science
People wishing to learn Machine Learning from scratch
People of different domains – Business Analyst, Marketing, etc
Seeking job in the areas of machine learning

Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset.�

Students will learn the theories, techniques, and tools they need to deal with various datasets. We will start with Regression, one of the basic models, and progress as we evaluate and assessing different models. We will start from the initial stages of data science and advance to higher levels where students can write their own algorithm from scratch to build a model. We will see end to end and work with practical datasets at the end of each module. Students will be issued with tutorials and explanation of all the exercises to help you learn faster and enable you to link theory using hands on exercises.�

This course teaches advanced theory including some mathematics with practical exercises to promote deeper understanding.

Learning Outcomes

At the end of the course the students will:

Have an in-depth understanding of the concepts of Machine Learning
Be able to grasp, understand, and write machine learning code from scratch�
Use Builtin Libraries available to build machine learning models
Be able to analyze, build, and assess models on any dataset
Be able to interpret and understand the black box behind model
Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.

What is the working system of this course?

Strong concepts and theory linked to practical at the end of each module
Easy Lectures for those starting from scratch
Illustration and examples
Hands-on exercises with tutorials
Detailed explanations of how models work

What does this course cover?

Introduction to machine learning: Overview of supervised and unsupervised learning
Regression from scratch – Gradient Descent, Cost Function , Modelling
Using Machine learning builtin library
Feature Scaling
Multivariate Regression�
Polynomial Regression
Over-fitting, Under-fitting and Generalization
Bias Variance Tradeoff
Cross Validation Strategy and Hyper-parameter tuning
Grid Search�
Learning Curves
Decision Trees and introduction to other algorithms including neural network
Exercises after each module

After completing the course, you will have enough knowledge and confidence to code machine learning algorithms from scratch and to use built-in library. This course is for all interested in learning data science and machine learning, there is no such pre req. This course is different from other courses in a manner that it teaches to code algorithms and also exposes you to the mathematics behind machine learning, this even includes tutorials at the end of each module so that students can do side by side practice with the instructor. It exposes you to practical real world datasets to work on and get started with new problems.

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: 1. What is Supervised Learning with Examples

Lecture 2: 2. What is Unsupervised Learning with Examples

Chapter 2: Linear Regression with One Variable

Lecture 1: Introduction to Correlation Analysis

Lecture 2: Correlation Analysis

Lecture 3: Introduction to Linear Regression

Lecture 4: LinearRegression Equation

Lecture 5: Evaluation of Linear Regression (Sum of Squared Error)

Lecture 6: 8. Minimizing Cost/Error Function Overview

Lecture 7: Example Minimizing Cost/Error

Lecture 8: Cost/Error Function

Lecture 9: Cost Function and Idea of NormalEquation

Lecture 10: Intuition of Gradient Descent

Lecture 11: Gradient Descent Training

Lecture 12: Learning Rate

Lecture 13: Plotting Cost vs Number of Iterations

Lecture 14: Plot for different Learning Rate

Lecture 15: Summarizing Hypothesis Cost Function and Derivative Cost Function

Lecture 16: Derivative Cost Function

Lecture 17: Overview of Assignment 1

Lecture 18: Linear Regression in Python Overview – Housing Dataset

Lecture 19: Linear Regression in Python -Train Test Splitting and Data Pre

Lecture 20: Linear Regression in Python – Defining Hypothesis

Lecture 21: Linear Regression in Python – Defining Cost Function

Lecture 22: Linear Regression in Python – Defining Gradient Descent Vectorized Code

Lecture 23: Linear Regression in Python-Finding Best Parameter and fitting nice Line on Data

Lecture 24: Linear Regression in Python – Checking performance on unseen data

Lecture 25: Linear Regression in Python using scikit-learn Library

Chapter 3: Basic Machine Learning Pipeline

Lecture 1: Machine Learning Pipeline

Lecture 2: Next Part

Chapter 4: Multivariate Linear Regression

Lecture 1: Introduction to Multivariate Linear Regression

Lecture 2: Hypothesis – Vectorized Concept for Multivariate Linear Regression

Lecture 3: Just a Revision – Concept of Vectorized Code, Derivative Cost Function Pattern

Lecture 4: Overview of Feature Scaling

Lecture 5: Feature Scaling- MinMax Scaling

Lecture 6: Feature Scaling- Standardization

Lecture 7: Feature Scaling- Data after Standardization

Lecture 8: Feature Scaling- Summary

Lecture 9: Assignment 2: Multivariate Linear Regression in Python – Getting Started

Lecture 10: Multivariate Linear Regression in Python – Building Model

Chapter 5: Polynomial Regression and Dividing Dataset for Model Assessment

Lecture 1: Making Model Complex

Lecture 2: Introduction to Polynomial Regression

Lecture 3: Intuition of Polynomial Regression – Python

Lecture 4: Overfitting Underfitting and Generalization

Lecture 5: Using builtin Library in Python – Overfitting Underfitting and Generalization

Lecture 6: Choosing Polynomial – Training, Test Dataset and Introducing Validation Set

Lecture 7: Validation Dataset

Chapter 6: Model Assessment and Cross Validation

Lecture 1: Overview of Cross Validation

Lecture 2: Bias Variance Tradeoff

Lecture 3: Bias Variance Explained using Simple Diagram

Lecture 4: K Fold Cross Validation

Lecture 5: K Fold Cross Validation in Python using Sklearn Library

Lecture 6: Grid Search in Python to Find Best Combination of Parameters

Lecture 7: Intuition of Learning Curve

Lecture 8: Learning Curve – High Bias Case

Lecture 9: Learning Curve – High Variance Case

Lecture 10: Learning Curve Demonstration in Python

Lecture 11: Summary So Far

Chapter 7: Other Models

Lecture 1: Decision Tree Regressor

Lecture 2: Decision Tree Examples and Applications

Lecture 3: Intuition of Neural Networks

Chapter 8: Working on the Dataset to apply all the Concepts

Lecture 1: Working on Housing Dataset in Python

Lecture 2: Understanding Regression, Metrics, Learning Curve on Housing Dataset

Lecture 3: Decision Trees in Python- Working with Dataset

Lecture 4: Predictions using Decision Tree Regressor – Working with Dataset

Instructors

SaifAli Kheraj
Instructor

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 4 votes
5 stars: 7 votes

Frequently Asked Questions

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