LEARNING PATH: Statistics for Machine Learning

Harness the statistical fundamentals and terminology for model building and validation

3.22 (9 reviews)
Udemy
platform
English
language
Data Science
category
74
students
4 hours
content
Mar 2018
last update
$19.99
regular price

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

Description

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

Content

Fundamentals of Statistical Modeling and Machine Learning Techniques

The Course Overview
Machine Learning
Statistical Terminology for Model Building and Validation
Bias Versus Variance Trade-Off
Linear Regression Versus Gradient Descent
Machine Learning Losses
Train, Validation, and Test Data
Cross-Validation and Grid Search
Machine Learning Model Overview
Compensating Factors in Machine Learning Models
Simple Linear Regression from First Principles
Simple Linear Regression Using Wine Quality Data
Multi-Linear Regression
Linear Regression Model – Ridge Regression
Linear Regression Model – Lasso Regression
Maximum Likelihood Estimation
Logistic Regression
Random Forest
Variable Importance Plot
Test your knowledge

Advanced Statistics for Machine Learning

The Course Overview
Artificial Neural Networks
Forward Propagation and Back Propagation
Optimization of Neural Networks
ANN Classifier Applied on Handwritten Digits
Introduction to Deep Learning
K-means Clustering
Principal Component Analysis
Singular Value Decomposition
Deep Autoencoders
Deep Autoencoders Applied on Handwritten Digits
Introduction to Reinforcement Learning
Reinforcement Learning Basics
Markov Decision Process and Bellman Equations
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning
Test your knowledge

Screenshots

LEARNING PATH: Statistics for Machine Learning - Screenshot_01LEARNING PATH: Statistics for Machine Learning - Screenshot_02LEARNING PATH: Statistics for Machine Learning - Screenshot_03LEARNING PATH: Statistics for Machine Learning - Screenshot_04

Reviews

Jim
May 7, 2018
Get somebody who knows the subject to talk about it. Not some hired guy who mispronounces the terms and doesn't know what he is talking about.

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1595828
udemy ID
3/14/2018
course created date
11/7/2020
course indexed date
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