English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2 Hours | 386 MB
Learn the Basic Building Blocks of Statistics and Machine Learning
Understand various concepts related to Statistics and Machine Learning
Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This video will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, NumPy, random Forest and so on. By the end of the course, 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.
What You Will Learn
- Introduces statistical terminology and machine learning
- Provides an overview of machine learning terminology for model building and validation
- Offers practical solutions for simple linear regression and multi-linear regression
- Compares logistic regression and random forest using examples
Table of Contents
01 The Course Overview
02 Machine Learning
03 Statistical Terminology for Model Building and Validation
04 Bias Versus Variance Trade-Off
05 Linear Regression Versus Gradient Descent
06 Machine Learning Losses
07 Train, Validation, and Test Data
08 Cross-Validation and Grid Search
09 Machine Learning Model Overview
10 Compensating Factors in Machine Learning Models
11 Simple Linear Regression from First Principles
12 Simple Linear Regression Using Wine Quality Data
13 Multi-Linear Regression
14 Linear Regression Model – Ridge Regression
15 Linear Regression Model – Lasso Regression
16 Maximum Likelihood Estimation
17 Logistic Regression
18 Random Forest
19 Variable Importance Plot
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