
Foundations of Machine Learning
ML with Python path4h 12m 19s
[PREVIEW] What is Machine Learning (02:19)
[PREVIEW] GarbageIn GarbageOut (05:37)
Broad Types of ML Problems Part1 (02:23)
Broad Types of ML Problems Part2 (04:12)
Broad Types of ML Problems Part3 (04:13)
[PREVIEW] Industrial Applications of ML Part1 (11:57)
Industrial Applications of ML Part2 (08:43)
What ML Can and Cannot Do (08:23)
Data Science vs ML vs AI vs Deep Learning vs Statistical Modeling (08:28)
Introduction to ML Project Workflow (06:10)
Discover (05:19)
Design (04:16)
Develop (02:37)
Testing (04:15)
Deploy (04:16)
Interpreting ML Models (03:45)
Interpreting ML Models Part1 (06:02)
Interpreting ML Models Part2 (03:56)
How to Validate ML Models (06:16)
Need for Validation Sample (04:19)
ML Terminology You Need to Know  Part 1  Supervised vs Unsupervised Learning (06:34)
ML Terminology You Need to Know  Part 2  Independent vs Dependent Variables (04:49)
ML Terminology You Need to Know  Part 3  More Terms (04:27)
What is Ensemble Learning (05:12)
Reinforcement Learning Intuition (05:15)
Basic Statistical Concepts Part1 (07:33)
Basic Statistical Concepts Part2 (04:47)
Role of Significance Tests (04:17)
Summary
Assignments/challenges
Certificate of Completion

Numpy for Data Science
ML with Python path3h 07m 38s
[PREVIEW] Introduction to Numpy (02:19)
[PREVIEW PIC] Solved Notebooks
Python Setup Google Colab (05:47)
Python Setup Local Installation (10:37)
[PREVIEW] Creating Arrays (10:48)
Inspecting Arrays (05:24)
Why DataTypes Matter (08:43)
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)
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

Pandas for Data Science
ML with Python path6h 34m 17s
[PREVIEW] Introduction to Pandas (03:19)
[PREVIEW PIC] Notebooks with stepwise explanations
[PREVIEW PIC] Complete Dataset
[PREVIEW] 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 Part1 (3:32)
Categorical Data Part2 (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 Part1 (07:09)
Dask Part2 (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

Restaurant Visitor Forecasting Project
ML with Python path4h 58m 07s
[PREVIEW] Course Introduction and What You Will Learn (04:52)
[PREVIEW PIC] Solved Project Notebook (with stepwise explanations)
[PREVIEW PIC] Industry Data Set
Data Overview (04:46)
Basic Data Stats (08:33)
[PREVIEW] 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

Microsoft Malware Detection Project
ML with Python path4h 52m 21s
[PREVIEW] Project Overview (03:35)
Problem Description (5:00)
[PREVIEW PIC] Solved Project Notebook with stepwise explanations
Complete Industry Dataset
Python Setup  Google Colab (05:06)
Python Setup  Local Installation (10:02)
[PREVIEW] Data Overview (11:52)
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

Credit Card Fraud Detection Project
ML with Python path5h 11m 17s
[PREVIEW]Course Overview (03:09)
Solved notebooks with stepwise explanations
[PREVIEW PIC] Complete Industry Data
[PREVIEW] Data Overview (05:56)
Optimize Memory Usage (05:23)
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)
Principal Components Analysis (08:02)
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)
Assignment/ Challenges
Certificate of completion

BaseR Programming
ML with R path6h 48m 18s
Installing R Studio (6:30)
R Studio walkthrough (11:10)
[PREVIEW] 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 Adcampaign (4:55)
Module Overview (1:30)
Set Operations (5:19)
[PREVIEW] If and ifelse (4:19)
Making assignments within ifelse (2:48)
Checking existence (1:16)
Nested ifelse (3:00)
For loops (4:00)
Writing smarter For loops (2:00)
Break while repeat (5:08)
Memory preallocation 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)
Mapply (3:20)
Summary (2:20)
Assignment/ Challenges
Certificate of completion

Dplyr for Data Wrangling
ML with R path36m 47s
[PREVIEW] Course Overview (02:00)
[PREVIEW] Getting started with Dplyr pipes (06:20)
Tpipe (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
Join Machine Learning Plus Univerity"> Certificate of completion

Wrangling Data with Data.Table
ML with R path50m 41s
[PREVIEW] 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

GGPlot2 Visualization for Data Analysis
ML with R path1h 15m 13s
[PREVIEW] Course Overview (04:08)
Introduction to GGPlot2 (05:04)
[PREVIEW] 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)
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

Statistical Foundations for ML in R
ML with R path3h 20m 27s
Course Overview (01:00)
[PREVIEW] Introduction to Statistical Analysis (02:09)
[PREVIEW] Descriptive vs Prescriptive Analysis (01:09)
[PREVIEW] Types of Statistical Analyses (02:06)
Statistical Testing (2:09)
Panel Data and Types (3 :34)
Cross Sectional and Pooled Cross Sectional (1:05)
Types of Variables (2:45)
Measures of Central Tendency and Dispersion (11:35)
Demo  Measures of Central Tendency Dispersion (02:46)
Law of Large Numbers (02:33)
Gamblers Fallacy (03 :24)
Normal Distribution (04:45)
Standard Normal Distribution (04:40)
Central Limit Theorem (04:34)
Demo  Central Limit Theorem (04:12)
Standard Error (06:34)
Confidence Intervals Formula (02:34)
Confidence Intervals with Bootstrapping (06:45)
Correlation (13:00)
Demo  Correlation Test (08:45)
Introduction to TTests (08:34)
One Sample TTest (08:44)
One Sample TTest  R Demo (06:33)
One Sample TTest  Hand Computation (08:23)
What is Two Sample Paired TTest (02:44)
Two Sample Dependent T test  Examples of when to use (05:45)
Two Sample Dependent T test  Hand Computation (10:45)
Two Sample Dependent T test  R Demo (08:34)
What is Independent Two Sample Ttest (02:55)
Independent Two Sample T test  Examples of when to use (02:23)
Independent Two Sample T test  Hand Computation (08:34)
Independent Two Sample T test  R Demo (03:23)
What is ChiSquared Test (03:09)
ChiSquared Test  Examples of When to Use (02:04)
Chi Square Test  Hand Computation (10:22)
ChiSquared Test  R Demo (03 :44)
What is ANOVA (04:34)
When to Use ANOVA (04:56)
ANOVA Terminologies (04:37)
ANOVA  Hand Computation (05:45)
ANOVA  R Demo (03:27)
Assignment/ Challenges
Certificate of completion

Regression Modeling in R
ML with R path5h 07m 13s
[PREVIEW] Course Overview (01:45)
[PREVIEW] 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)
[PREVIEW] RSquared Explained (07:45)
PreModel Build Analysis (05:45)
Building and Interpreting Linear Regression Models (05:45)
Problem with RSquared (02:34)
Adjusted RSquared (01:34)
FStatistic, AIC, BIC (03:34)
RDemo (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 RSquared (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

Caret Package for ML in R
ML with R path2h 01m 05s
[PREVIEW] 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)
Exploring the ML Models (BIG LIST) (02:14)
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

Spacy for NLP
Specialized Courses4h 27m 25s
[PREVIEW] Overview of Spacy (05:07)
[PREVIEW] 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)
Course Review (01:00)
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)
Course Review (1:00)
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)
Course Review (1:00)
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)
Course Review (1:00)
Assignment/ Challenges
Certificate of completion

Data Processing and EDA
ML with Python Launching Oct215h 31m 23s
Measures of Central Tendency
Measures of Dispersion
Correlation and Correlation Test
ChiSquared 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 OutliersZ 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 MultiLine 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

Regression and Regularization
ML with Python Launching Oct215h 34m 23s
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  pvalue
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
CrossValidation
L1 Regularization Intuition
L2 Regularization Intuition
Elastic net Regularization Intuition
L1 Regularization Implementation
L2 Regularization Implementation
Elasticnet Regularization Implementation
Summary
Assignment/ Challenges
Certificate of completion

Getting Started with Classification Algorithms
ML with Python Launching Oct213h 10m 31s
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, F1score
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
Elasticnet Regularization Implementation
Summary
Assignment/ Challenges
Certificate of completion

Handling Imbalanced Data for Classification
ML with Python Launching Nov212h 25m 41s
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 imbalancedlearn
Random oversampling using imbalancedlearn
Oversampling using SMOTE
Oversampling followed by undersampling
Using inbuilt class_wight parameter of algorithms
ADASYN
OneClass SVM
Mahalanobis Distance
LightGBM with the focal loss
Summary
Assignment/ Challenges
Certificate of completion

Supervised Learning
ML with Python Launching Nov215h 49m 23s
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
Introduction to Naive Bayes algorithm
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 Dec215h 39m 07s
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 tradeoff
Problem Statement
Data Overview and Preprocessing
Model Building
Model Refinement using various parameters
Hyperparameter tuning
Introduction to AdaBoost
The Intuition Behind AdaBoost
Maths behind AdaBoost
Various parameters of AdaBoost
Bias variance tradeoff
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 tradeoff
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)
Assignment/ Challenges
Certificate of completion

Feature Engineering
ML with Python Launching Dec215h 02m 31s
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
Tfidf Vectors
Work Tokenization
Bag of Words
Stemming
Lemmatization
POS Tagging
Reduce Dimensions using Feature Hashing
Overview of Time Series data
Date related feature engineering
Timerelated 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 Jan223h 13m 34s
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
Chisquare Intuitions
Chisquare 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

Dimensionality Reduction
ML with Python Launching Jan224h 55m 23s
Curse of Dimensionality
How Dimensionality Reduction Solves the Problem
Intuitions Behind PCA
Maths Behind PCA
PCA Implementation
Intuitions Behind tSNE
Maths Behind tSNE
TSNE Implementation
Intuitions Behind Sammon mapping
Maths behind Sammon mapping
Sammon mapping implementation
Intuitions behind Self Organizing Maps
Maths behind Self Organizing Maps
Maths behind Self Organizing Maps
Intuitions behind LDA
Maths behind LDA
LDA Implementation
Intuitions Behind MDS
Maths Behind MDS
MDS Implementation
Intuitions Behind ICA
Maths Behind ICA
ICA Implementation
Intuitions Behind NonNegative Matrix Factorization
Maths Behind NonNegative Matrix Factorization
NonNegative Matrix Factorization implementation
Summary
Assignment/ Challenges
Certificate of completion

Deployment of ML Models
ML with Python Launching Jan229h 48m 23s
Deployments of Machine Learning Model
Deployment of Machine Learning Pipelines
Research and Production Environment
Building Reproducible Machine Learning Pipelines
Challenges to Reproducibility
Streamlining Model Deployment with OpenSource
Machine Learning System Architecture and Why it Matters
Specific Challenges of Machine Learning Systems
Principles for Machine Learning Systems
Machine Learning System Architecture Approaches
Machine Learning System Component Breakdown
Research Environment  Process Overview
Machine Learning Pipeline Overview
Feature Engineering  Variable Characteristics
Feature Engineering Techniques
Feature Selection
Training a Machine Learning Model
Data Analysis Demo  Missing Data
Data Analysis Demo  Temporal Variables
Data Analysis Demo  Numerical Variables
Data Analysis Demo  Categorical Variables
Feature Engineering Demo 1
Feature Engineering Demo 2
Feature Selection Demo
Model Training Demo
Scoring New Data With Our Model
Python Open Source for Machine Learning
Open Source Libraries for Feature Engineering
Feature Engineering with Open Source Demo
Intro to Object Oriented Programing
Inheritance and the Scikitlearn API
Create ScikitLearn Compatible Transformers
Create Transformers that Learn Parameters
Feature Engineering Pipeline Demo
Should Feature Selection be Part of the Pipeline?
Getting Ready for Deployment  Final Pipeline
Introduction to Production Code
Code Overview
Package Requirements Files
Working with tox
Package Config
The Model Training Script & Pipeline
Introduction to Pytest [Optional]
Feature Engineering Code in the Package
Making Predictions with the Package
Building the Package
Tooling
Running the API Locally
Understanding the Architecture of the API
Introduction to FastAPI
The API Endpoints
Using Schemas in our API
Logging in our Application
The Uvicorn Web Server
Introducing Heroku and Platform as a Service (PaaS)
Deploying our Application to Heroku
Understanding the HerokuSpecific Project Files
Introduction to CI/CD
Setting up CircleCI
CI/CD Automation Overview Part 1
CI/CD Config
CI/CD Automation Overview Part 2
Using a Private Index Server (Gemfury)
Hands on: Run the CI Tests in your own Github Fork
Hands on: Run the CI Deploy on Your Own Github Fork
Hands on: Run the CI Publish on Your Own Github Fork
Docker Refresher [Optional  For those unfamiliar/rusty with Docker]
The Value of Docker and Containers
Understanding The Container Deployment Process
Hands On: Containerising the App Locally
Updating the CI Pipeline for a Container Deployment
How to Use the Course Resources
Introduction
Setting up Differential Tests
Differential Tests in CI (Part 1 of 2)
Differential Tests in CI (Part 2 of 2)
Wrap up
Introduction to AWS
AWS Costs and Caution
Ntro to AWS ECS
Container Orchestration Options: Kubernetes, ECS, Docker Swarm
Create an AWS Account
Setting Permissions with IAM
Installing the AWS CLI
Configuring the AWS CLI
Intro the Elastic Container Registry (ECR)
Uploading Images to the Elastic Container Registry (ECR)
Creating the ECS Cluster with Fargate Launch Method
Creating the ECS Cluster with the EC2 Launch Method
Updating the Cluster Containers
Tearing down the ECS Cluster
Deploying to ECS via the CI pipeline
Wrap up
Challenges of using Big Data in Machine Learning
Introduction to a Large Dataset  Plant Seedlings Images
Building a CNN in the Research Environment
Production Code for a CNN Learning Pipeline
Reproducibility in Neural Networks
Packaging the CNN
Adding the CNN to the API
Additional Considerations and Wrap Up
Introduction
Primer on Monorepos
Creating the API Skeleton
Adding Config and Logging
Adding the Prediction Endpoint
Adding a Version Endpoint
API Schema Validation
Wrap Up
Summary
Assignment/ Challenges
Certificate of completion

Unsupervised Learning
ML with Python Launching Feb226h 09m 23s
What is unsupervised learning
Industrial Applications of unsupervised learning
Intuitions behind kmeans
Maths behind kmeans
How to select the optimal number of clusters
Kmeans ++
Problem Statement
EDA and data preprocessing
Model Building
Find the optimal number of clusters
Visualize the clusters
Intuitions behind kmedoids
Maths behind kmedoids
Problem Statement
EDA and data preprocessing
Model Building
Visualize the clusters
Intuitions behind hierarchical clustering
Maths behind hierarchical clustering
Visualize the hierarchical clustering model
Problem Statement
EDA and data preprocessing
Model Building
Visualize the clusters
Intuitions behind DBSCAN clustering
Maths behind DBSCAN clustering
Problem Statement
EDA and data preprocessing
Model Building
Visualize the clusters
Intuitions behind fuzzyc clustering
Maths behind fuzzyc clustering
Problem Statement
EDA and data preprocessing
Model Building
Visualize the clusters
Intuitions behind GMM
Maths behind GMM
Problem Statement
EDA and data preprocessing
Model Building
Visualize the clusters
Summary
Assignment/ Challenges
Certificate of completion

Recommendation System
ML with Python Launching Feb225h 18m 29s
Getting the most from this course
Course Roadmap
What Is a Recommender System?
Types of recommenders
Understanding You through Implicit and Explicit Ratings
TopN Recommender Architecture
Ranks
[Activity] The Basics of Python
Data Structures in Python
Functions in Python
[Exercise] Booleans, loops, and a handson challenge
Train/test and cross validation
Accuracy Metrics (RMSE, MAE)
TopN Hit Rate  Many Ways
Coverage, Diversity, and Novelty
Churn, Responsiveness, and A/B Tests
[Quiz] Review ways to measure your recommender.
[Activity] Walkthrough of RecommenderMetrics.py
Walkthrough of test metrices
Measure the Performance of SVD Recommendations
Our Recommender Engine Architecture
[Activity] Recommender Engine Walkthrough, Part 1
[Activity] Recommender Engine Walkthrough, Part 2
[Activity] Review the Results of our Algorithm Evaluation.
Content based recommendations and the cosin similarity metrics
KNearestNeighbors and Content Recs
[Activity] Producing and Evaluating ContentBased Movie Recommendations
A Note on Using Implicit Ratings.
[Activity] Bleeding Edge Alert! Mise en Scene Recommendations
[Exercise] Dive Deeper into ContentBased Recommendations
Measuring Similarity and Sparsity
Similarity Metrics
User based collaborative Filtering
Preview
[Activity] Userbased Collaborative Filtering, HandsOn
Itembased Collaborative Filtering
[Activity] Itembased Collaborative Filtering, HandsOn
[Exercise] Tuning Collaborative Filtering Algorithms
[Activity] Evaluating Collaborative Filtering Systems Offline
[Exercise] Measure the Hit Rate of ItemBased Collaborative Filtering
KNN Recommenders
[Activity] Running User and ItemBased KNN on MovieLens
[Exercise] Experiment with different KNN parameters.
Bleeding Edge Alert! TranslationBased Recommendations
Principal component analysis
Singular Value Decomposition
Preview
Improving on SVD
[Exercise] Tune the Hyperparameters on SVD
Bleeding Edge Alert! Sparse Linear Methods (SLIM)
Deep Learning Introduction
Deep Learning PreRequisites
History of Artificial Neural Networks
[Activity] Playing with Tensorflow
Training Neural Networks
Tuning Neural Networks
Activation Functions: More Depth
Introduction to Tensorflow
[Activity] Handwriting Recognition with Tensorflow, part 1
[Activity] Handwriting Recognition with Tensorflow, part 2
Introduction to Keras
[Activity] Handwriting Recognition with Keras
Classifier Patterns with Keras
[Exercise] Predict Political Parties of Politicians with Keras
Intro to Convolutional Neural Networks (CNN's)
CNN Architectures
[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
Intro to Recurrent Neural Networks (RNN's)
Training Recurrent Neural Networks
[Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras
Tuning Neural Networks
Generative Adversarial Networks (GAN's)
GAN's in Action
Generating Images of Clothing with Generative Adversarial Networks
Intro to Deep Learning for Recommenders
[Activity] Recommendations with RBM's, part 1
[Activity] Recommendations with RBM's, part 2
[Activity] Evaluating the RBM Recommender
[Exercise] Tuning Restricted Boltzmann Machines
Exercise Results: Tuning a RBM Recommender
AutoEncoders for Recommendations: Deep Learning for Recs
[Activity] Recommendations with Deep Neural Networks
Clickstream Recommendations with RNN's
[Exercise] Get GRU4Rec Working on your Desktop
Exercise Results: GRU4Rec in Action
Tensorflow Recommenders (TFRS): Building a Ranking Stage
TFRS: Incorporating Side Features and Deep Retrieval
TFRS: MultiTask Recommenders, Deep & Cross Networks, ScaNN, and Serving
Bleeding Edge Alert! Deep Factorization Machines
More Emerging Tech to Watch
Introduction and Installation of Apache Spark
Apache Spark Architecture
[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
[Activity] Recommendations from 20 million ratings with Spark
DSSTNE in Action
Scaling Up DSSTNE
AWS SageMaker and Factorization Machines
SageMaker in Action: Factorization Machines on one million ratings, in the cloud
Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and more)
Recommender System Architecture The Cold Start Problem (and solutions)
[Exercise] Implement Random Exploration
Exercise Solution: Random Exploration
[Exercise] Implement a Stoplist
Exercise Solution: Implement a Stoplist
[Exercise] Identify and Eliminate Outlier Users
Exercise Solution: Outlier Removal
Fraud, The Perils of Clickstream, and International Concerns
Temporal Effects, and ValueAware Recommendations
Case Study: YouTube, Part 1
Case Study: YouTube, Part 2
Case Study: Netflix, Part 1
Case Study: Netflix, Part 2
Hybrid Recommenders and Exercise
Exercise Solution: Hybrid Recommenders
More to Explore
Summary
Assignment/ Challenges
Certificate of completion
Try for free: 30 days unlimited access to Machine Learning Plus University! Click here to join.
Build Skills to Solve Real World Data Science Projects
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 timeconsuming, 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 Machine Learning Plus University, you need the following:
 Logical aptitude and high school maths
 Windows, macOS, Linux, or Raspbian (all major operating systems supported)
 Free Gmail/Google account to run preconfigured Jupyter Notebooks in Colab (optional)
 A desire to learn
Course description
Machine Learning Plus University is a comprehensive set of selfpaced 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, outofdate information, I keep Machine Learning Plus University uptodate by releasing a brand new course every month!
Releasing a new course every month ensures you can keep up with the stateoftheart 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
 Highquality, well documented source code with linebyline explanations (ensuring you know exactly what the code is doing)
 Jupyter Notebooks that are preconfigured 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 machine learning 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 levelup 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 highquality, well documented source code, pretrained 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 preconfigured 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.
26 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 26 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
 Crack top ranks in highly competitive Kaggle competitions
 Land coveted Data Scientist jobs in industry
 Switch from parttime AI projects in companies to fulltime data science role
Machine Learning Plus University is your chance to join them in machine learning mastery.
Machine Learning Plus University syllabus
26 Courses • 1141 Classes • 104h 21m 11s
In my early days of career , I used ML+ content to gain experience on various sorts of projects ranging from Linear regression to Random forest. The content over there isn’t just easy to understand but at the same time engaging too. Many thanks to Machine Learning Plus for providing me a platform to play around with data.
Loved the way they explained concepts in the learning path, and solved each and every query. Professional group with immense passion in this field..Thanks Machine Learning Plus. Proud to be certified in Data Science
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!
The ML path is an absolute nobrainer ..Even with zero Data Science experience I was able to grasp all the concepts. Helped me big time in my projects
MLPlus’s ML program has amazing content..such nice explanations. Would look out for more such courses.
Despite being in this field for some years, this adds a lot of value for easy understanding of concepts..must have for beginners.
Full Access Plan (Other Countries)
FREE for 18 days, then...
No commitments, cancel anytime.
This course includes:
Full access to Machine Learning Plus University
Brand new courses released every month, ensuring you can keep up with stateoftheart techniques
104h 21m ondemand video
26 courses on essential machine learning topics
26 Certificates of Completion
1141 tutorials and downloadable resources
Preconfigured Jupyter Notebooks in Google Colab for all courses
Run all code examples in your web browser — works on Windows, macOS, and Linux (no dev environment configuration required!)
Easy oneclick downloads for code, datasets, pretrained 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 after 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 preconfigured 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 multinational 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, handson way. And you’ll have fun doing it. See you on the other side!