Last updated 3/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 413.77 MB | Duration: 4h 11m

Harness the statistical fundamentals and teology for model building and validation

What you'll learn

Introduces statistical teology 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

Requirements

Prior knowledge of Python and R programming is expected.

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 teology for model building and validation

Explore and execute unsupervised and reinforcement learning models

You will start off with the basics of statistical teology 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 eeering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies

Overview

Section 1: Fundamentals of Statistical Modeling and Machine Learning Techniques

Lecture 1 The Course Overview

Lecture 2 Machine Learning

Lecture 3 Statistical Teology 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

Section 2: Advanced Statistics for Machine Learning

Lecture 20 The Course Overview

Lecture 21 Artificial Neural Networks

Lecture 22 Forward Propagation and Back Propagation

Lecture 23 Optimization of Neural Networks

Lecture 24 ANN Classifier Applied on Handwritten Digits

Lecture 25 Introduction to Deep Learning

Lecture 26 K-means Clustering

Lecture 27 Principal Component Analysis

Lecture 28 Singular Value Decomposition

Lecture 29 Deep Autoencoders

Lecture 30 Deep Autoencoders Applied on Handwritten Digits

Lecture 31 Introduction to Reinforcement Learning

Lecture 32 Reinforcement Learning Basics

Lecture 33 Markov Decision Process and Bellman Equations

Lecture 34 Dynamic Programming

Lecture 35 Monte Carlo Methods

Lecture 36 Temporal Difference Learning

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

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