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Numpy for Data Science
ML with Python path12 lessons, 2h 06m 18s
[Preview Concept] How ARIMA works
Introduction to Numpy (02:19)
Python Setup - Google Colab (05:47)
Python Setup - Local Installation (10:37)
Rotation (11:01)
Creating Arrays (10:48)
Inspecting Arrays (05:24)
Cropping (10:16)
Why Data Types Matter (08:43)
Xyz lesson
Import and Export Data (07:33)
Missing Data (03:47)
Extracting Specific Items (12:50)
Random Numbers (11:22)
Set Operations (06:17)
Statistical Summaries (06:28)
Reshaping and New Axis (08:15)
Sequences and Repetitions (5:30)
Mesh Grid (04:06)
Concatenate and Split (06:11)
At and iat (04:12)
Mini Challenge (01:32)
Filtering Data Sorting Methods (08:30)
Searchsorted and Bisect (06:21)
Handling Dates (07:04)
Sorting (02:42)
Vectorization (05:24)
Apply Along (06:13)
Useful Functions (07:19)
Broadcasting (06:47)
Universal Functions (UFuncs) (03:37)
Interpolation (06:48)
Fitting Polynomials (09:24)
Matrix Operations (10:43)
Solving Linear Equations (02:33)
Closing Remarks (01:47)
Summary (02:04)
Assignment/ Challenges
Certificate of completion
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Pandas for Data Science
ML with Python path10 lessons, 2h 32m 07s
Data Code download Pre-configured Notebook Lesson assessment
Overview (02:13)
Introduction to Pandas (03:19)
Need For DataFrame (03:47)
Creating Dataframe (08:37)
Mini Challenge (00:48)
Series (07:24)
Mini Challenge (02:43)
Python Setup Google Colab (04:33)
Python Setup Local Installation (09:47)
Inspecting Dataframes (04:50)
Renaming Columns (04:22)
Pandas Summary (03:17)
Essential Operations (04:28)
Display Options (02:15)
Extracting Specific Part of Data - Part 1 (5:30)
Extracting Specific Part of Data - Part 2 (02:06)
Mini Challenge (01:11)
At and iat (04:12)
Mini Challenge (01:32)
Filtering Data That Satisfy Conditions (05:30)
Membership Filtering (04:21)
Query and Eval (04:04)
Sorting (02:42)
Map and applymap (07:24)
Apply a function rowwise or columnwise (7:02)
Scaling and Standardization (05:05)
Make Index as a Dataframe Column (02:09)
Discretization and Binning (05:26)
Random Sampling (05:13)
Dummy Variables (02:03)
Categorical Data Part-1 (3:32)
Categorical Data Part-2 (03:07)
Efficiently Read Data From Multiple Files (05:30)
Group by Mechanism (3:10)
Mini Challenge (02:04)
Iterating Between groups (3:02)
Transform (04:05)
Reshaping and Pivoting Data (02:09)
Cross Tabulation (02:26)
Pivoting (04:13)
Wide to Long and Back (07:03)
Joining Dataframes (04:32)
Types of Joins (06:07)
Concatenating Dataframes (2:08)
Representing Missing Values (05:07)
Threshold Based Dropping (02:30)
Approaches To Filling Missing Data (04:18)
Interpolation (06:18)
Compressed File Formats (4:02)
Sparse Datatype (03:05)
Combining Categories (03:09)
Split Contents of a Column (01:26)
Insert Column at Specific Location (02:13)
Select using both Position and Lab (04:03)
Styling Dataframes (02:32)
Comprehensive Profile Report (04:07)
Interactive Plots (2:08)
Third Party Data (04:07)
Interactive Data Analysis (05:30)
Optimizing Dataframes (05:18)
Sampling On Load (04:18)
Efficient File Formats (2:02)
HDF5 (06:05)
Chunking (04:09)
Load to Database (06:26)
Faster Pandas (01:13)
Numba (07:05)
Dask Part-1 (07:09)
Dask Part-2 (05:26)
Modin (06:13)
Swifter (05:03)
Vaex (13:32)
Cython (07:07)
Cythonize Pandas Code (6:08)
Cythonize Apply (06:07)
Matplotlib Part 1 - Getting Started (07:30)
Matplotlib Part 2 - Plot Components (07:18)
Matplotlib Part 3 - Subplots (07:18)
Matplotlib Part 4 - Annotations (05:02)
Dual Axis Line Plots (07:05)
Bar Charts (06:09)
Histogram and Density Plots (02:26)
Regression Plots (06:13)
Pair Plots (07:13)
Summary (02:45)
Assignment/ Challenges
Certificate of completion
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Fundamentals of Machine Learning
ML with Python path12 lessons, 2h 06m 18s
Why Machine Learning
What is Machine Learning?
Rule Bases Systems vs ML Based Approach
Garbage in - Garbage out
Broad Types of ML Problems
Real World Applications of ML
What Machine Learning Can and Cannot Do
Data Science vs AI vs ML
Introduction to ML Project Workflow
ML Project - 'Discover' phase
ML Project - 'Design' phase
ML Project - 'Develop' phase
ML Project - 'Validate' phase
ML Project - 'Test' phase
ML Project - 'Deploy' phase
What is an ML Model?
Interpreting ML Model
How to validate ML Models?
Need for Validation Sample
ML Terminology you need to know
Overview of ML Algorithms
What is Ensemble Learning
Reinforcement Learning
Recommendation Systems
Basic Statistical Terms
Role of Statistical Significance Tests
Commercially Viable Projects by Industry
Summary
Assignment/ Challenges
Certificate of completion
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Restaurant Visitor Forecasting Project
ML with Python path12 lessons, 2h 06m 18s
Course Introduction and What You Will Learn (05:03)
Download Resources (01:02)
Data Overview (04:46)
Basic Data Stats (08:33)
Exploratory Data Analysis (08:34)
Feature Engineering - Domain Specific (04:43)
Feature Engineering - Interaction Features (03:54)
Exploratory Data Analysis Part 2 (04:34)
ANOVA Concept - Intuition (10:07)
ANOVA Concept - Maths (13:22)
Significance Tests (08:23)
Label Encoding (02:04)
Data Preprocessing for Model Building (04:08)
Feature Encoding Approaches (08:43)
Evaluation Metrics (05:43)
Introduction to Time Series Modeling (01:09)
Time Series and Stationarity Concepts (11:09)
ACF and PACF (08:02)
Introduction to ARIMA Modeling (05:26)
AR and MA Models - Part 1 (06:55)
AR and MA Models - Part 2 (06:23)
Auto Arima Concept (03:45)
Strategy 1 - ARIMA Forecasting Demo (07:34)
Strategy 1 - ARIMA Forecasting Demo - Part 2 (08:55)
Durbin Watson Statistic (03:08)
Strategy 2 - Auto ARIMA (08:12)
Strategy 3 - AutoARIMA for Genre (09:34)
SARIMA and SARIMAX (06:33)
Strategy 4 SARIMA Demo (06:34)
Strategy 5 SARIMAX Demo (10:18)
Exogenous Variables Deepdive Part 1 - Time Based and Demographics (05:03)
Exogenous Variables Deepdive Part 2 - Qualitative and Promotions (05:04)
Exogenous Variables Deepdive Part 3 - Series decomposition and LifeCycle (12:22)
Exogenous Variables Deepdive Part 4 - Macro Economic Features (03:34)
Forecast for Unknown Future (02:43)
Prophet by Facebook (04:09)
Prophet Forecasting Demo (08:49)
New Plan of Attack (03:56)
XGBoost Demo (08:34)
CatBoost (05:34)
CatBoost Demo (07:23)
Hyper Parameters and Tuning (07:12)
Cohorted Ensembles (07:45)
Cohorted Ensembles Demo (04:34)
Final Words (2:45)
Assignment/ Challenges
Certificate of completion
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Microsoft Malware Detection Project
ML with Python path12 lessons, 2h 06m 18s
Course Overview (4 mins)
Problem Description (5:00)
Download Resources (1:22)
Python Setup - Google Colab (05:06)
Python Setup - Local Installation(10:02)
Data Overview (12:21)
Optimize Memory Usage (11:19)
Understand The Data (8:00)
Data Preprocessing for EDA (7:00)
Need for EDA (5:39)
Exploratory Data Analysis Part 1 (14:00)
Exploratory Data Analysis Part 2 (13:23)
Chi Squared Test Theory and Maths (18:45)
Chi Square Test and Odds Ratio Demo (09:34)
ANOVA Concept - Intuition (10:23)
ANOVA maths (06:05)
ANOVA Demo (08:00)
Feature Engineering - Domain Specific (08:00)
Feature Encoding Approaches (08:00)
Feature Encoding Demo (08:34)
Data Processing for Model Building (05:23)
Confusion Matrix and Evaluation Metrics (10:19)
Concordance and Discordance (09:34)
Precision Recall Curve (03:34)
Evaluation Metrics Demo (05:09)
Decision Trees and Improvements (17:09)
Random Forests (09:00)
XGBoost (07:09)
LightGBM (08:55)
Tuning Hyperparameters (07:06)
Feature Importance (11:34)
Feature Importance Demo (02:09)
Final Words (01:08)
Assignment/ Challenges
Certificate of completion
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Credit Card Fraud Detection Project
ML with Python path12 lessons, 2h 06m 18s
Course Overview (03:09)
Data Overview (05:56)
Optimize Memory Usage (05:23)
Basic Stats (06:09)
Data Preprocessing for EDA (04:19)
Exploratory Data Analysis (12:00)
Chi Squared Test Theory and Maths (18:09)
Chi Square Test and Odds Ratio Demo (09:00)
ANOVA Concept - Intuition (10:04)
ANOVA Cancept - Maths (13:03)
ANOVA Demo (07:04)
Feature Engineering (05:09)
Feature Engineering (05:09)
Feature Encoding Approaches (8:05)
Feature Encoding Demo (04:09)
Data Preprocessing for Model Building (03:34)
Why XGBoost (04:03)
XGBoost Demo (02:00)
Confusion Matrix and Evaluation Metrics (09:04)
Concordance and Discordance (09:56)
ROC Curve (09:18)
Precision Recall Curve (03:56)
Evaluation Metrics Demo (03:19)
Capture Rates and Calibration Curve (11:45)
Light GBM (08:45)
Light GBM Demo (04:45)
Random Forest Introduction (01:02)
Decision Trees Algorithm (13:49)
Random Forest Classifier Algorithm (06:00)
Extra Trees Algorithm (06:03)
Random Forests Demo (09:09)
Random Oversampler (10:03)
Cost Sensitive Learning with Class Weights (07:34)
Probability Calibration (08:55)
Model Calibration Demo (02:34)
Model Tuning (07:37)
Feature Engineering (05:30)
Feature Importance (11:20)
Feature Importance Demo (03:10)
Partial Dependence and ICE Plots (06:40)
SHAP Interpretations (07:40)
SHAP Demo (05:00)
XGBoost Demo (02:00)
Final Words (02:45)
Final Words (02:45)
Assignment/ Challenges
Certificate of completion
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Base R-Programming
ML with Python path12 lessons, 2h 06m 18s
Overview (2 Lessons, 49:02)
Course Overview (1:20)
Installing R Studio (6:30)
R Studio walkthrough (11:10)
Datatypes (6:45)
Data Structures (3:06)
Vectorization (3:47)
Online Pizza Advertisement (2:20)
Create vector with a single element (10:56)
Create group of elements in a vector (12:10)
Use repetitions and sequence to create a vector fast (6:16)
Random numbers, rounding and sampling (9:00)
Formatting numbers (3:36)
Create subsets (8:04)
Handling Missing Values (10:00)
Binning (9:09)
Operations within a vector (9:12)
Operations between same size vectors (3:44)
Operations between different sized vectors (4:20)
Revenue impact of Ad-campaign (4:55)
Module Overview (1:30)
Set Operations (5:19)
If and ifelse (4:19)
Making assignments within ifelse (2:48)
Checking existence (1:16)
Nested if-else (3:00)
For loops (4:00)
Writing smarter For loops (2:00)
Break while repeat (5:08)
Memory pre-allocation tactics (5:05)
Why Dates cant just be strings (5:09)
Date operations (1:00
Working with lubridate and anytime (5:00)
Introduction to lists (6:03)
Named list, unlist and more (7:08)
Introduction to Dataframe (3:48)
Creating Dataframe (3:34)
Visual editing (2:25)
Various dataframe operations (11:06)
Inspecting and Rownames (10:23)
Select, delete, subset (7:09)
Attributes and comments (4:04)
Saving dataframe to disk (7 :20)
Native RDS files (2:34)
Handling CSV files (2:09)
Xlsx files (2:37)
SAS and Stata files (4 :07)
R datasets, packages and public datasets (11:04)
Useful data summarization function (8:17)
Conditional filtering and missing values (10:45)
Matrix vs dataframe (6:39)
Joining operations for dataframes (9:28)
Pivot and frequency table (11:09)
Grouping and case problem solution (9:29)
Module Overview (2:35)
Base Graphics basics (2 :24)
Scatterplot (1:34)
Adding plot components (8:45)
Legend (1:34)
Saving plot components and challenge (3:34)
Line plot with secondary Y axis (7:39)
Change Par settings (4:24)
Histogram and bar charts (6:34)
Box plot (3:20)
Dot plot and density plot (5:34)
Multiple plots and custom layouts (5:00)
Intro to stringr (5:25)
Sentences, punctuations, strings manipulations (6:45)
Writing effective functions (7:20)
Local and global namespace (12:45)
Debugging R code (9:56)
Error handling (7:40)
Apply Function (3:00)
Apply, sapply, vapply (4:10)
Apply, sapply, vapply (4:10)
Mapply (3:20)
Summary (2:20)
Assignment/ Challenges
Certificate of completion
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Dplyr for Data Wrangling
ML with Python path12 lessons, 2h 06m 18s
Course Overview (02:00)
Getting started with Dplyr pipes (06:20)
T-pipe (01:20)
Compound assignment and exposition pipe (02:10)
Tibble (04:30)
Types of joins in dplyr (03:10)
Joins demo (04:40)
Joins challenges (03:30)
Assignment/ Challenges
Certificate of completion
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Wrangling Data with Data.Table
ML with Python path12 lessons, 2h 06m 18s
Course Overview (01:09)
Introduction to datatable (03:04)
Creating datatable and importing (07:03)
Datatable syntax (01:04)
Filtering and subsetting (04:05)
Creating new columns (07:06)
Running multiple statements in one (02:07)
Groupby operations (04:40)
Special symbols (04:30)
Applying functions (03:40)
Go faster with keys (10:20)
Fast loops with set function (07:30)
Assignment/ Challenges
Certificate of completion
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GGPlot2 Visualization for Data Analysis
ML with Python path12 lessons, 2h 06m 18s
Course Overview (04:08)
Introduction to GGPlot2 (05:04)
Scatterplot with geom point() (02:07)
Line of Best Fit and Smoothing Lines (01:09)
Adjust X and Y Limits (02:35)
Confidence Interval Shading (02:23)
Changing Color of Points (03:45)
Changing Size (01:56)
Removing Legend (01:45)
Color Palette (01:45)
Customize Axis Text (02:45)
Look and Feel (04:10)
Labels and Text (05:56)
Custom Annotations (02:30)
Legend (05:30)
Better Represent Overlapping Points with Jitter and Counts Plot (03:40)
Multiplot with Facets (06:40)
Custom Layout (01:30)
Histograms (08:50)
Bar Charts (02:40)
Box Plot and Violin Plots (04:33)
Time Series Plots (06:45)
Multiple Time Series in Same Plot (03:00)
Assignment/ Challenges
Certificate of completion
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Regression Modeling in R
ML with Python path12 lessons, 2h 06m 18s
Course Overview (01:45)
What is Linear Regression (05:45)
Understanding with Practical Examples (02:45)
Examples of Industrial Applications (02:33)
Statistical Modeling vs Machine Learning (02:23)
Graphical Understanding (03:34)
Formulate Line of Best Fit (04:34)
Linear Regression from Scratch using Formula (07:24)
R-Squared Explained (07:45)
Pre-Model Build Analysis (05:45)
Building and Interpreting Linear Regression Models (05:45)
Problem with R-Squared (02:34)
Adjusted R-Squared (01:34)
F-Statistic, AIC, BIC (03:34)
R-Demo (04:34)
Assumptions (18:34)
Problem Statement (03:20)
Handling Missing Values (09:20)
Outlier Analysis (04:27)
Graphical and Statistical Analysis (10:20)
Building Linear Regression (07:00)
Good Model (03:05)
Evaluation Measures (05:00)
Need for Cross Validation (12:03)
Cross Validation Approaches (05:30)
Variable Transformations and Interactions (05:20)
Variance Inflation Factor (04:30)
Cooks Distance for Influential Points (06:10)
Cooks Distance Demo (05:04)
BoxCox and YeoJohnson Transformations (06:20)
Residual Analysis (06:20)
Overcoming Heteroscedasticity (08:20)
Stepwise Regression for Model Search (05:05)
Best Subsets Model Search (06:05)
What Exactly is Gradient Descent (08:30)
How Gradient Descent Learns (06:20)
Comparing Types of Gradient Descent (03:10)
Stopping Criteria and Scaling (02:20)
Types of Gradient Descent (04:17)
Introduction to Logistic Regression (03:40)
One vs Rest Strategy (02:40)
Use Case Examples (04:20)
Math behind Logistic Regression Part 1 (04:20)
Math behind Logistic Regression Part 2 (04:40)
Why Negative Logloss (03:20)
Problem Statement Marketing (01:20)
Demo - EDA for Logit (09:11)
Building Logistic Regression Model (05:33)
McFadden's R-Squared (03:30)
Confusion Matrix and Evaluation Metrics (09:30)
Precision Recall Curve (03:20)
Optimal Cutoff Score (05 :10)
The ROC Curve (02:03)
KS Statistic and Gain Curve (11:10)
Concordant and Discordant pairs (06:20)
Approaches to Handle Class Imbalance (06:33)
Cost Sensitive Learning (06:28)
Oversampling (03:17)
Hybrid Sampling (05:40)
Assignment/ Challenges
Certificate of completion
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Caret Package for ML in R
ML with Python path12 lessons, 2h 06m 18s
What is Caret (04:00)
Problem Statement (03:08)
Sampling Approaches (08:04)
Missing Value Treatment (07:05)
One Hot Encoding (04:05)
Box Cox Transformation Concept (02:05)
Applying Box Cox and PCA (06:06)
Feature Plots (05:06)
Statistical Analysis (05:06)
Recursive Feature Elimination (RFE) Algorithm (03:05)
Recursive Feature Elimination (RFE Demo) (03:22)
Genetic Algorithms (03:43)
Genetic Algorithms part 2 - Feature Selection (09:33)
Genetic Algorithms Demo (06:34)
Simulated Annealing Concept (06:45)
Simulated Annealing Demo (05:25)
Simulated Annealing Demo (05:25)
Building ML with train function (08:23)
Automatic Hyperparameter Tuning (04:34)
Customize Hyperparameter Search (02:24)
Comparing Adaboost xgboost random forest and svm (02:34)
Tuning Non Default Parameters with Caret (03:45)
Caret Ensembles (04:00)
Assignment/ Challenges
Certificate of completion
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Spacy for NLP
Specialized Courses12 lessons, 2h 06m 18s
Overview of Spacy (05:07)
Common Use Cases (04:19)
Introduction to Spacy Models (08:11)
What Happens Inside Spacy Models (03:02)
Install Spacy and Models (04:05)
Data Structures in Spacy (04:15)
Data structures in Spacy - Make a doc from scratch (03:05)
Data structures in Spacy - nlp.make.doc() (03:09)
String Store (05:06)
How Tokenization Works (04:18)
Create Custom Tokenization Rules (06:19)
Build Custom Tokenizer (04:09)
Named Entity Recognition (07:06)
POS Tagging (05:18)
Dependency Parsing (01:05)
Need for Dependency Parsing (02:13)
Navigating the Parse Tree (02:20)
Noun Chunks (04:13)
Contextual Token Matching (08:15)
Phrase Matching (07:10)
Handling Large Number of Patterns (04:11)
Course Review (01:01)
Understanding Spacy Pipelines (04:00)
How to Apply Particular Component (04:03)
Manipulating the Pipeline (04:14)
Entity Ruler (02:12)
How to Create Custom Pipeline Components (03:12)
Custom Sentence Segmenter (06:07)
Efficient Text Processing Approaches (07:16)
User Defined Attributes (04:07)
Property Extensions (02:21)
Method Extensions (3:08)
Need for Word Vectors (11:03)
Advantage of Vector Representation (05:00)
How Word Vectors are Computed (09:08)
Understanding Cosine Similarity (09:22)
The Training Loop (04:11)
Train a New NER Model from Scratch (07:10)
Training Custom NER Models (05:09)
Training Custom NER Models - Part 2 (03:04)
Training Custom NER Models - Part 3 (03:00)
Training Custom NER Models - Part 4 (3:10)
Text Classification with TextCat - Part 1 (4:08)
Text Classification with TextCat - Part 2 (2:02)
Text Classification with TextCat - Part 3 (3:10)
Text Classification with TextCat - Part 4 (2:00)
Text Classification with TextCat - Part 5 (3:05)
Text Classification with TextCat - Part 6 (5:15)
Training Customer POS Tagger Model - Part 1 (4:09)
Training Customer POS Tagger Model - Part 2 (5:07)
Training Custom Intent Parser - Part 1 (5:06)
Training Custom Intent Parser - Part 2 (3:07)
Training Custom Intent Parser - Part 3 (3:02)
Training Custom Intent Parser - Part 4 (5:20)
Building Chatbot with Spacy and Rasa (6:20)
Intent Classification (4:07)
NLU Pipeline (2:09)
Training NLU Model (3:00)
RASA Core (2:11)
Writing Stories (8:18)
Assignment/ Challenges
Certificate of completion
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Data Processing and EDA
ML with Python Launching Oct2112 lessons, 2h 06m 18s
Measures of Central Tendency
Measures of Dispersion
Correlation and Correlation Test
Chi-Squared Test
ANOVA
Types of missing values
Identify missing values
Visualise missing values using 'missingo'
Dropping rows or columns with missing values (how and when)
Replace with 0, mean, median, mode, most frequent, string
Causes of missing values and when simple methods fail?
Predict the missing value
How to Validate the Accuracy of Your Missing Value Treatment Approach
How to spot Outliers- Box plot interpretation - IQR
How to spot Outliers-Z score
Multivariate outliers- Multivariate
Multivariate outliers- Series
Local Outlier factor
Isolation forest
Feature Scaling
Feature Encoding
Standardization vs Normalization
Plotting Correlation: Relationship between Two Numerical Variables- Scatterplot and Lines of Best Fit
Plotting Correlation: Relationship between two numerical variables- Correlation matrix with Heatmap
Histogram and Density plot
Bar Plot, Stacked
Box Plot and Violin Plot
Multiple Box Plots
Bubble Plots
Pair Plots
Trellis Chart or Facets
Mosaic Plots
Line Plots
Line Plots with Multiple Lines
Line Plots with Secondary Axis
Autocorrelation Plot
Time Series Seasonal Plots- Multi-Line chart
Time Series Seasonal Plots- The monthly and yearly box plot
Waterfall Chart (How to Create and Where is it Used?)
3D Plots
Problem Statement
Data Overview
Univariate Analysis
Bivariate Analysis - Statistical Significance Tests
EDA - Get Business Insight
Problem Statement
Data Overview
Univariate Analysis
Bivariate Analysis - Statistical Significance Tests
EDA - Get Business Insight
Summary
Assignment/ Challenges
Certificate of completion
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Regression and Regularization
ML with Python Launching Oct2112 lessons, 2h 06m 18s
Introduction to Linear Regression
Industrial Applications of Regression
Simple Linear Regression - Ordinary Least Square (OLS)
Multiple Linear Regression - Intuition
Cost Function
Gradient Descent
Model Diagnostics - R square and Adj R square
Model Diagnostics - p-value
Model Diagnostics - VIF
Model Diagnostics - AIC/BIC
Assumptions of Linear Regression
Mean Absolute Error
Mean Absolute Percentage Error
Mean Squared Error
Room Mean Squared Error
Problem Statement
Data Overview
EDA
Data Preprocessing
Various Approaches to implementing Linear Regression
Forward Stepwise Regression
Backward Stepwise Regression
Stepwise Regression
Sklearn Implementation
Model Evaluation
Check Assumptions of Linear regression
Intuition and Motivation
Polynomial Regression Implementation
How to Choose the Degree of the Polynomial
Underfitting and Overfitting
Cross-Validation
L1 Regularization Intuition
L2 Regularization Intuition
Elastic net Regularization Intuition
L1 Regularization Implementation
L2 Regularization Implementation
Elastic-net Regularization Implementation
Summary
Assignment/ Challenges
Certificate of completion
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Foundations of Classification Algorithms
ML with Python Launching Oct2112 lessons, 2h 06m 18s
Introduction to Logistic Regression
Industrial Applications of Logistic Regression/Classification
Solve Multiclass Classification using Logistic Regression
Graphical and Geometric Representation
The Logistic Function
Why use Logistic Regression over Linear Regression
Linear Regression to Logistic Regression
Maths behind Logistic Regression
Accuracy
Confusion Matrix - TP, TN, FP, FN
Classification Report - Precision, Recall, F1-score
ROC Curve
AUC
KS Statistic and Gain curve
Concordant and Discordant Pair
Problem Statement
Data Overview
EDA
Data Preprocessing
Forward Stepwise Regression
Model Implementation
Model Evaluation
L1 Regularization Implementation
L2 Regularization Implementation
Elastic-net Regularization Implementation
Summary
Assignment/ Challenges
Certificate of completion
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Handling Imbalanced Data for Classification
ML with Python Launching Oct2112 lessons, 2h 06m 18s
What is imbalanced data
Why is an imbalanced data problem for classification
How to fix the problem of class imbalance
Upsampling minority class using resample
Undersampling majority class using resample
Random undersampling using imbalanced-learn
Random oversampling using imbalanced-learn
Oversampling using SMOTE
Oversampling followed by under-sampling
Using inbuilt class_wight parameter of algorithms
ADASYN
One-Class SVM
Mahalanobis Distance
LightGBM with the focal loss
Summary
Assignment/ Challenges
Certificate of completion
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Supervised Learning
ML with Python Launching Oct2112 lessons, 2h 06m 18s
Introduction to KNN algorithm
Intuition behind KNN
Maths behind KNN
How to choose the optimal value for k
Problem Statement
Data Overview and Preprocessing
Model Building
Model Processing
Model Refinement Using Different Values for k
Hyperparameter tuning to find the best value of k
Hyperparameter tuning to find the best value of k
The Intuition behind Naive Bayes
Various Types of Naive Bayes
Problem Statement
Data Overview and Preprocessing
Model Building - Gaussian Naive Bayes
Model Building - Bernoulli Naive Bayes
Model Evaluation
Introduction to SVM Algorithm
The Intuition Behind SVM Classification
Maths Behind SVM
Various Kernels of SVM
Kernel Tricks
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement Using Different Kernels
Model Improvement Using Hyperparameter Tuning
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using Different Kernels
Introduction to Decision Tree Algorithm
The Intuition Behind Decision Tree Classification
The Intuition Behind Decision Tree Regression
The Measure of Impurity Classification
The Measure of Impurity Regression
How to Visualize and Interpret Decision Tree
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using Different Kernels
Summary
Assignment/ Challenges
Certificate of completion
-
Ensemble Learning
ML with Python Launching Oct2112 lessons, 2h 06m 18s
What is Ensemble Learning
Bagging vs Boosting
Evolution of Boosting Algorithms
Introduction to Random Forest
The Intuition Behind Random Forest
Maths Behind Random Forest
Bootstrap Aggregation
Various parameters of random forest
Bias variance trade-off
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using various parameters
Hyperparameter tuning
Introduction to AdaBoost
The Intuition Behind AdaBoost
Maths behind AdaBoost
Various parameters of AdaBoost
Bias variance trade-off
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using various parameters
Hyperparameter tuning
Introduction to CatBoost
The intuition behind CatBoost
Maths behind CatBoost
Various parameters of CatBoost
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using various parameters
Hyperparameter tuning
Introduction to Gradient Boosting
The Intuition behind Gradient Boosting
Maths behind Gradient Boosting
Various parameters of Gradient Boosting
Bias Variance trade-off
The Intuition Behind XGBoost
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using various parameters
Hyperparameter tuning
Introduction to LightGBM
The intuition behind LightGBM
Maths behind LightGBM
Various parameters of LightGBM
Problem Statement
Data Overview and Preprocessing
Model Building
Model Evaluation
Model Refinement using various parameters
Hyperparameter tuning
Final Words (2:45)
Assignments/challenges
Certificate of completion
-
Feature Engineering
ML with Python Launching Oct2112 lessons, 2h 06m 18s
Overview of Numerical data
Feature Standardization
Feature Normalization
Feature Binning
Feature Transformation
Feature Interactions
Ranks
Splines
Overview of Categorical data
Feature Encoding
Dummy Coding
Contrast Coding
Bin Counting
Feature Hashing
Feature Interactions
Overview of Text data
Count Vectors
Tf-idf Vectors
Work Tokenization
Bag of Words
Stemming
Lemmatization
POS Tagging
Reduce Dimensions using Feature Hashing
Overview of Time Series data
Date related feature engineering
Time-related feature engineering
Lag features
Sliding window features
Rolling window features
Expanding window features
Feature Interactions
Summary
Assignment/ Challenges
Certificate of completion
-
Feature Selection
ML with Python Launching Oct2112 lessons, 2h 06m 18s
What is Feature Selection
Why Feature Selection
Types of Feature Selection Algorithm
Pearson Correlation Intuitions
Pearson Correlation Implementation
LDA Intuitions
LDA Implementation
ANOVA Intuitions
ANOVA Implementation
Chi-square Intuitions
Chi-square Implementation
Forward Selection Intuitions
Forward Selection Implementation
Backward Selection Intuitions
Backward Selection Implementation
Stepwise Selection Intuitions
Stepwise Selection Implementation
Recursive Features Selection Intuitions
Recursive Feature Selection Implementation
Lasso Regression Intuitions
Lasso Regression Implementation
Random Forest Intuitions
Randorm Forest Implementation
Summary
Assignment/ Challenges
Certificate of completion
-
Dimesionality Reduction
ML with Python Launching Oct2112 lessons, 2h 06m 18s
Curse of Dimensionality
How Dimensionality Reduction Solves the Problem
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Try for free: 30 days unlimited access to Machine Learning Plus University! Click here to join.
Machine Learning Plus University: You can master Machine Learning
I strongly believe that if you had the right teacher you could completely master machine learning
Do you think mastering Machine Learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science?
Well, that’s exactly not the case.
All you need to master machine learning is for someone to explain things to you in simple, intuitive terms. And that’s exactly what I do. My mission is to change machine learning education and how complex Artificial Intelligence topics are taught.
Welcome to Machine Learning Plus University, the most comprehensive machine learning course online today. Here you’ll learn how to successfully and confidently apply machine learning to your work, research, and projects. Join me in complete machine learning mastery.
Created by: Selva Prabhakaran, Principal Data Scientist •Last updated: 9/2021 •Languages: English
What you'll be able to do...
- Successfully complete your machine learning projects
- Land a job in the Artificial Intelligence field
- Apply machine learning to your job and workplace
- Complete your final graduation project and obtain your undergraduate degree
- Finish your MSc or PhD thesis
- Perform novel research and publish paper in a reputable AI journal
- Learn machine learning, and then teach your high school or college students
- Understand machine learning, and launch a business in the AI space
- Finish that AI project you are hacking on over nights and weekends
Requirements
In order to be successful in PyImageSearch University, you need the following:
- Understanding of Python basics
- Windows, macOS, Linux, or Raspbian (all major operating systems supported)
- Free Gmail/Google account to run pre-configured Jupyter Notebooks in Colab (optional)
- A desire to learn
Course description
Machine Learning Plus University is a comprehensive set of self-paced courses for developers, students, and researchers who are ready to master machine learning. Inside this University, you’ll learn how to successfully and confidently apply machine learning to your work, research, and projects.
Unlike other online courses, which are created once and never updated, leaving you with stale, out-of-date information, I keep Machine Learning Plus University up-to-date by releasing a brand new course every month!
Releasing a new course every month ensures you can keep up with the state-of-the-art in machine learning, learn new algorithms and techniques, and:
- Successfully complete your projects at work
- Perform novel research (and publish papers)
- Finish your final graduation project for school
- Launch your next company in the Artificial Intelligence space
To help you accomplish these goals, in each lesson I provide:
- Detailed video tutorials for every lesson
- High-quality, well documented source code with line-by-line explanations (ensuring you know exactly what the code is doing)
- Jupyter Notebooks that are pre-configured to run in Google Colab
- Support for all major operating systems (Windows, macOS, Linux, and Raspbian)
Machine Learning Plus University is without a doubt the most complete, comprehensive computer vision education online inside. I’ll see you inside.
Selva Prabhakaran
Creator, machinelearningplus.com
Trusted by members of top artificial intelligence companies, schools, and organizations
Who this course is for:
If any of these descriptions fit you, rest assured, Machine Learning Plus University is designed for you.
- You are a machine learning practitioner that utilizes machine learning at your day job, and you’re eager to level-up your skills.
- You’re a developer who wants to learn machine learning, complete your challenging project at work, and stand out from your coworkers (and land that big promotion).
- You are a college student who needs help with your homework, completing your final graduation project, or you simply want more than what your university offers.
- You are a researcher or scientist looking to apply machine learning techniques to your research (and publish a paper).
- You have experience with machine learning and want to learn more about niche topics and applications
- You are an entrepreneur studying machine learning so you can launch your next business in the Artificial Intelligence space.
- You are a “machine learning hobbyist” who wants to successfully complete that project you are hacking on over nights and weekends.
- You’re a Machine Learning Plus reader that wants access to centralized repos containing high-quality, well documented source code, pre-trained models, image datasets, etc. for all the tutorials on machinelearningplus.com.
- You prefer running code examples with Jupyter Notebooks in Google Colab — my notebooks are pre-configured and ready to run in Google Colab with only a single click.
- You learn best through video tutorials — Machine Learning Plus University includes video guides for every single lesson.
14 Certificates of Completion
We don’t offer just one Certificate of Completion like most online courses. Instead, we offer a certificate for each of the 14 courses inside Machine Learning Plus University.
And since a brand new course is released every month, that means each month you receive…
- A brand new course
- A new set of lessons
- A new set of quizzes
- A new final exam
- And another opportunity to demonstrate your machine learning knowledge to the world
Machine Learning graduates have gone on to:
- Perform novel research and publish papers in prestigious journals
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- Switch from part-time AI projects in companies to full-time data science role
Machine Learning Plus University is your chance to join them in machine learning mastery.
Machine Learning Plus University syllabus
25 Courses •128 Classes •37h 19m 02s Lectures
Excellent choice for those who want to learn about Data Science and Machine Learning techniques. It covers both theoretical and practical training of Exploratory Data Analysis, Linear and Logistic Regression, Random Forest, and others.
The instructor has a wide knowledge of each topic he explains. What I liked most about the classes was how he explains the mathematics behind the algorithms easily, which in turn makes the course more interesting. I definitely recommend this!
Full Access Plan
FREE for 7 days, then...
No commitments, cancel anytime.
This course includes:
Full access to PyImageSearch University
Brand new courses released every month, ensuring you can keep up with state-of-the-art techniques
37h 19m on-demand video
25 courses on essential computer vision, deep learning, and OpenCV topics
25 Certificates of Completion
304 tutorials and downloadable resources
Pre-configured Jupyter Notebooks in Google Colab for 200+ PyImageSearch tutorials
Run all code examples in your web browser — works on Windows, macOS, and Linux (no dev environment configuration required!)
Access to centralized code repos for all 400+ tutorials on PyImageSearch
Easy one-click downloads for code, datasets, pre-trained models, etc.
Access on mobile, laptop, desktop, etc.
Frequently Asked Questions
Do I need programming experience before joining the University?
No. You are good to start with zero coding experience or background. We teach Python programming needed for data science, from scratch.
Do I need to know anything about machine learning to get started in Machine Learning Plus University?
No. The courses inside Machine Learning Plus University will teach you all concepts and implementation of Machine Learning. As long as you complete the courses and assignments diligently, you will be successful inside Machine Learning Plus University.
What happens affter I purchase?
After you purchase, you will be able to login and immediately access any code downloads, Jupyter Notebooks, video tutorials, courses, certificates of completion, etc.
Do I need any special software or hardware?
No. All of our courses, coding exercises, etc. can be completed inside your browser using our pre-configured Jupyter Notebooks running in Google Colab. If you prefer to instead configure your local development environment, we provide install instructions as well.
How will I be charged?
You will not be charged for the first 30 days. And you can cancel the trial anytime during the period, without any charges. If you continue the membership, you will be charged on a yearly or 6 monthly basis depending on the plan that you subscribed to.
Can I pause/cancel my subscription?
Yes. Once you login, click your ‘My profile’ icon on top right, followed by “Subscriptions”. From there you can edit your payment method or cancel/pause your membership.
Do you provide bulk Machine Learning Plus University memberships to business, colleges etc?
Yes. Just write an email to [email protected], and we can schedule a call to discuss getting your organization access to Machine Learning Plus University
Hi there — I’m Selva
About your teacher
Hey, I’m Selva Prabhakaran, a Principal Data Scientist of a global corporate and creator of machinelearningplus.com, who has spent his entire adult life studying machine learning and deploying multiple global products. Over the past 10 years I have:
- Started the MachineLearningPlus.com blog and published over 300+ tutorials and articles aimed at teaching machine learning and deep learning – and impacting over 4 million readers annually
- Authored multiple books and courses on Data Science that have collectively been taken up by over 10,000 students
- Built competent data science teams for multi-national companies, and mentored over 50 data scientists, many of whom these data scientists have gone on to become data science leads in different firms
- Answered over 20,000+ emails and helped 10,000s of developers, researchers, and students just like yourself learn the ropes of machine learning.
If having an actual experience of building data science models and products sounds interesting to you, join me inside Machine Learning Plus University. You’ll learn a ton about machine learning in a practical, hands-on way. And you’ll have fun doing it. See you on the other side!
![[Preview Concept] How ARIMA works](https://res.cloudinary.com/ddlnkjeqd/image/upload/v1587985600/sample.jpg){kind=link}