HOT

LEARNING PATH: Statistics for Machine Learning

Development

Rating3.2
Enroll Now

LEARNING PATH: Statistics for Machine Learning, available at $19.99, has an average rating of 3.22, with 36 lectures, 2 quizzes, based on 9 reviews, and has 75 subscribers.

You will learn about Introduces statistical terminology and machine learning Offers practical solutions for simple linear regression and multi-linear regression Implement Logistic Regression using credit data Compares logistic regression and random forest using examples Implement statistical computations programmatically for unsupervised learning through K-means clustering Understand artificial neural network concepts Introduce different types of Unsupervised Learning This course is ideal for individuals who are This Learning Path is intended for developers with little to no background in statistics who want to implement machine learning in their systems. It is particularly useful for This Learning Path is intended for developers with little to no background in statistics who want to implement machine learning in their systems.

Enroll now: LEARNING PATH: Statistics for Machine Learning

Summary

Title: LEARNING PATH: Statistics for Machine Learning

Price: $19.99

Average Rating: 3.22

Number of Lectures: 36

Number of Quizzes: 2

Number of Published Lectures: 36

Number of Published Quizzes: 2

Number of Curriculum Items: 38

Number of Published Curriculum Objects: 38

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Introduces statistical terminology and machine learning
  • Offers practical solutions for simple linear regression and multi-linear regression
  • Implement Logistic Regression using credit data
  • Compares logistic regression and random forest using examples
  • Implement statistical computations programmatically for unsupervised learning through K-means clustering
  • Understand artificial neural network concepts
  • Introduce different types of Unsupervised Learning

Who Should Attend

  • This Learning Path is intended for developers with little to no background in statistics who want to implement machine learning in their systems.

Target Audiences

  • This Learning Path is intended for developers with little to no background in statistics who want to implement machine learning in their systems.

Machine learning worries a lot of developers when it comes to analyzing complex statistical problems. Knowing that statistics helps you build strong machine learning models that optimizes a given problem statement. This Learning Path will teach you all it takes to perform complex statistical computations required for machine learning. So, if you are a developer with little or no background in statistics and want to implement machine learning in their systems, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

 The highlights of this Learning Path are:

  • Learn Machine learning terminology for model building and validation
  • Explore and execute unsupervised and reinforcement learning models

You will start off with the basics of statistical terminology and machine learning. You will perform complex statistical computations required for machine learning and understand the real-world examples that discuss the statistical side of machine learning. You will then implement frequently used algorithms on various domain problems, using both Python and R programming. You will use libraries such as scikit-learn, NumPy, random Forest and so on. Next, you will acquire a deep knowledge of the various models of unsupervised and reinforcement learning, and explore the fundamentals of deep learning with the help of the Keras software. Finally, you will gain an overview of reinforcement learning with the Python programming language.

By the end of this Learning Path, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.

Meet Your Expert:

We have the best works of the following esteemed author to ensure that your learning journey is smooth:

  • PratapDangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master’s degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies

Course Curriculum

Chapter 1: Fundamentals of Statistical Modeling and Machine Learning Techniques

Lecture 1: The Course Overview

Lecture 2: Machine Learning

Lecture 3: Statistical Terminology for Model Building and Validation

Lecture 4: Bias Versus Variance Trade-Off

Lecture 5: Linear Regression Versus Gradient Descent

Lecture 6: Machine Learning Losses

Lecture 7: Train, Validation, and Test Data

Lecture 8: Cross-Validation and Grid Search

Lecture 9: Machine Learning Model Overview

Lecture 10: Compensating Factors in Machine Learning Models

Lecture 11: Simple Linear Regression from First Principles

Lecture 12: Simple Linear Regression Using Wine Quality Data

Lecture 13: Multi-Linear Regression

Lecture 14: Linear Regression Model – Ridge Regression

Lecture 15: Linear Regression Model – Lasso Regression

Lecture 16: Maximum Likelihood Estimation

Lecture 17: Logistic Regression

Lecture 18: Random Forest

Lecture 19: Variable Importance Plot

Chapter 2: Advanced Statistics for Machine Learning

Lecture 1: The Course Overview

Lecture 2: Artificial Neural Networks

Lecture 3: Forward Propagation and Back Propagation

Lecture 4: Optimization of Neural Networks

Lecture 5: ANN Classifier Applied on Handwritten Digits

Lecture 6: Introduction to Deep Learning

Lecture 7: K-means Clustering

Lecture 8: Principal Component Analysis

Lecture 9: Singular Value Decomposition

Lecture 10: Deep Autoencoders

Lecture 11: Deep Autoencoders Applied on Handwritten Digits

Lecture 12: Introduction to Reinforcement Learning

Lecture 13: Reinforcement Learning Basics

Lecture 14: Markov Decision Process and Bellman Equations

Lecture 15: Dynamic Programming

Lecture 16: Monte Carlo Methods

Lecture 17: Temporal Difference Learning

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 1 votes
  • 3 stars: 2 votes
  • 4 stars: 2 votes
  • 5 stars: 2 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!

You may also like

Google Shopping Ads Digital Marketing Course

Complete Copywriting System : write to sell with ease

Product Positioning Masterclass: Unlock Market Traction