Efficient Machine Learning
3.9 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,041 students enrolled

Efficient Machine Learning

Become an Advanced Machine Learning Specialist, Learn Preprocessing, Feature Engineering, Model Evaluation and Selection
3.9 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,041 students enrolled
Created by Usama Albaghdady
Last updated 10/2019
English
English
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 2.5 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Master Machine Learning
  • Performing Ideal Preprocessing
  • Understand Feature Engineering
  • Understand Feature Selection
  • Know the Best Way to Evaluate Models
  • Analyse Models and Overcome its Challenges
  • Hyperparameters Tuning
  • Make Accurate Predictions
  • Work with Real-World Data
Course content
Expand all 31 lectures 02:17:16
+ Preprocessing and Feature Scaling
7 lectures 25:31
The Dataset
03:43
Missing Values
02:43
Label Encoder
03:11
One-Hot Encoder
01:36
Normalization
03:00
Standardization
02:12
+ Feature Engineering
11 lectures 55:46
Feature Engineering Intro
08:45
Binarization
02:08
Principal Component Analysis (PCA)
07:23
Installing Featuretools
01:21
Manual Feature Engineering
05:57
Automatic Feature Engineering
08:26
Feature Selection 1 (Intro)
03:05
Feature Selection 3 (Feature Importance)
02:32
Feature Selection 4 (Model-Based Feature Selection)
04:57
Feature Selection 5 (Recursive Feature Elimination)
04:37
+ Model Evaluation and Selection
11 lectures 53:56
Model Evaluation and Selection Intro
02:35
Regression Evaluation
05:30
Classification Accuracy
03:47
F1-score and Fbeta-score
05:56
Area Under Curve (AUC)
03:39
Evaluation Measures for Multi-Class Classification
03:18
Overfitting vs Underfitting
04:43
Cross-Validation
06:13
Analyzing Learning Curves
06:22
Grid Search vs Random Search
05:59
Requirements
  • Basic Understanding of Machine Learning
  • Some Programming Experience
Description

If you’re a machine learning specialist looking to make the transaction into the real-world AI applications.

This comprehensive course will be your guide to learning how to scale-up your machine learning model to the optimal state possible, you 'll be learning everything you need to move you machine learning model to the next stage.

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

You'll learn the machine learning, AI, and data mining techniques real employers are looking for, including:


Handling Missing Values

Label Encoder

One-Hot Encoder

Normalization

Standardization

Binarization

Principal Analysis Component (PCA)

Manual Feature Engineering

Automatic Feature Engineering

Feature Selection

Model Evaluation

Confusion Matrix

Precision and Recall

F1-score and Fbeta-score

Area Under Curve (AUC)

Overfitting vs Underfitting

Cross-Validation

Analyzing Learning Curves

Hyperparameters Tuning


Who this course is for:
  • Anyone interested in Machine Learning
  • Any data analysts who want to level up in Machine Learning
  • Anyone who want to master Machine Learning