Home Courses Applied Machine Learning Online Course ML models - using byte files only :Random Model

ML models - using byte files only :Random Model

Instructor: Applied AI Course Duration: 11 mins

This content is restricted. Please Login

Prev
Next
Exploratory Data Analysis :Train-Test class distribution
k-NN
1.1
How to optimally learn from case-studies in the course?
1 min
1.2
Business/Real world problem : Problem definition

6 min
1.3
Business objectives and constraints.
5 min
1.4
Mapping to an ML problem : Data overview
5 min
1.5
Mapping to an ML problem : ML problem and performance metric.
4 min
1.6
Mapping to an ML problem : Train-test split
5 min
1.7
EDA: Basic Statistics.
7 min
1.8
EDA: Basic Feature Extraction
10 min
1.9
EDA: Text Preprocessing
6 min
1.10
EDA: Advanced Feature Extraction
31 min
1.11
EDA: Feature analysis.
9 min
1.12
EDA: Data Visualization: T-SNE.
3 min
1.13
EDA: TF-IDF weighted Word2Vec featurization.
6 min
1.14
ML Models :Loading Data
6 min
1.15
ML Models: Random Model
7 min
1.16
ML Models : Logistic Regression and Linear SVM
11 min
1.17
ML Models : XGBoost
6 min
2.1
Business/Real world problem : Overview

13 min
2.2
Business objectives and constraints.
11 min
2.3
ML problem formulation :Data
5 min
2.4
ML problem formulation: Mapping real world to ML problem.
19 min
2.5
ML problem formulation :Train, CV and Test data construction
4 min
2.6
Exploratory Data Analysis:Reading data & preprocessing
7 min
2.7
Exploratory Data Analysis:Distribution of Class-labels
7 min
2.8
Exploratory Data Analysis: “Random” Model
20 min
2.9
Univariate Analysis:Gene feature
34 min
2.10
Univariate Analysis:Variation Feature
19 min
2.11
Univariate Analysis:Text feature
15 min
2.12
Machine Learning Models:Data preparation
8 min
2.13
Baseline Model: Naive Bayes
23 min
2.14
K-Nearest Neighbors Classification
9 min
2.15
Logistic Regression with class balancing
10 min
2.16
Logistic Regression without class balancing
4 min
2.17
Linear-SVM.
6 min
2.18
Random-Forest with one-hot encoded features
7 min
2.19
Random-Forest with response-coded features
6 min
2.20
Stacking Classifier
8 min
2.21
Majority Voting classifier
5 min
3.1
Problem definition.
6 min
3.2
Overview of Graphs: node/vertex, edge/link, directed-edge, path.
11 min
3.3
Data format & Limitations.
9 min
3.4
Mapping to a supervised classification problem.
9 min
3.5
Business constraints & Metrics.
7 min
3.6
EDA:Basic Stats
12 min
3.7
EDA:Follower and following stats.
12 min
3.8
EDA:Binary Classification Task
16 min
3.9
EDA:Train and test split.
11 min
3.10
Feature engineering on Graphs:Jaccard & Cosine Similarities
15 min
3.11
PageRank
14 min
3.12
Shortest Path
4 min
3.13
Connected-components
12 min
3.14
Adar Index
12 min
3.15
Kartz Centrality
6 min
3.16
HITS Score
10 min
3.17
SVD
11 min
3.18
Weight features
6 min
3.19
Modeling
11 min
4.1
Business/Real world problem Overview

9 min
4.2
Objectives and Constraints
11 min
4.3
Mapping to ML problem :Data
8 min
4.4
Mapping to ML problem :dask dataframes
12 min
4.5
Mapping to ML problem :Fields/Features.

6 min
4.6
Mapping to ML problem :Time series forecasting/Regression
8 min
4.7
Mapping to ML problem :Performance metrics
6 min
4.8
Data Cleaning :Latitude and Longitude data
4 min
4.9
Data Cleaning :Trip Duration.
7 min
4.10
Data Cleaning :Speed.
5 min
4.11
Data Cleaning :Distance.
2 min
4.12
Data Cleaning :Fare
6 min
4.13
Data Cleaning :Remove all outliers/erroneous points
3 min
4.14
Data Preparation:Clustering/Segmentation
19 min
4.15
Data Preparation:Time binning
5 min
4.16
Data Preparation:Smoothing time-series data.
5 min
4.17
Data Preparation:Smoothing time-series data cont..
2 min
4.18
Data Preparation: Time series and Fourier transforms.
13 min
4.19
Ratios and previous-time-bin values
9 min
4.20
Simple moving average
8 min
4.21
Weighted Moving average.
5 min
4.22
Exponential weighted moving average
6 min
4.23
Results.
4 min
4.24
Regression models :Train-Test split & Features
8 min
4.25
Linear regression.
3 min
4.26
Random Forest regression
4 min
4.27
Xgboost Regression
2 min
4.28
Model comparison
6 min
5.1
Business/Real world problem

10 min
5.2
Business objectives and constraints
5 min
5.3
Mapping to an ML problem: Data overview
4 min
5.4
Mapping to an ML problem:ML problem formulation.
5 min
5.5
Mapping to an ML problem:Performance metrics.
21 min
5.6
Hamming loss
7 min
5.7
EDA:Data Loading
13 min
5.8
EDA:Analysis of tags
11 min
5.9
EDA:Data Preprocessing
11 min
5.10
Data Modeling : Multi label Classification
18 min
5.11
Data preparation.
8 min
5.12
Train-Test Split
2 min
5.13
Featurization
6 min
5.14
Logistic regression: One VS Rest
7 min
5.15
Sampling data and tags+Weighted models.
4 min
5.16
Logistic regression revisited
4 min
5.17
Why not use advanced techniques
3 min
7.1
Case Study 7: LIVE session on Ad Click Prediction
7.2
Case Study 7: Live Session: Ad-Click Prediction (contd.) and Performance metrics
7.3
Productionization of real-world ML systems
7.4
Scenario based Interview Questions for ML engineer roles
7.5
ML System Design For a Product Search Engine