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 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

4.84 (67 Ratings) • 851 Students Enrolled
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 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Unlike other online courses, which are created once and never updated, leaving you with stale, out-of-date information, I keep Machine Learning Plus University up-to-date by releasing a brand new course every month!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 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 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.
26 Certificates of Completion

Certificate 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 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

26 Courses • 1141 Classes • 104h 21m 11s

  • Foundations of Machine Learning
     ML with Python path 

    4h 12m 19s

    [PREVIEW] What is Machine Learning (02:19)

    [PREVIEW] Garbage-In Garbage-Out (05:37)

    Broad Types of ML Problems Part-1 (02:23)

    Broad Types of ML Problems Part-2 (04:12)

    Broad Types of ML Problems Part-3 (04:13)

    [PREVIEW] Industrial Applications of ML Part-1 (11:57)

    Industrial Applications of ML Part-2 (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 Part-1 (06:02)

    Interpreting ML Models Part-2 (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 Part-1 (07:33)

    Basic Statistical Concepts Part-2 (04:47)

    Role of Significance Tests (04:17)

    Summary

    Assignments/challenges

    Certificate of Completion

    Join Machine Learning Plus Univerity

  • Numpy for Data Science
     ML with Python path 

    3h 07m 38s

    [PREVIEW] Introduction to Numpy (02:19)

    [PREVIEW PIC] Solved Notebooks

    [PREVIEW PIC] Datasets

    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)

    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

    Join Machine Learning Plus Univerity

  • Pandas for Data Science
     ML with Python path 

    6h 34m 17s

    [PREVIEW] Introduction to Pandas (03:19)

    [PREVIEW PIC] Notebooks with step-wise 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 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

    Join Machine Learning Plus Univerity

  • Restaurant Visitor Forecasting Project
     ML with Python path 

    4h 58m 07s

    [PREVIEW] Course Introduction and What You Will Learn (04:52)

    [PREVIEW PIC] Solved Project Notebook (with step-wise 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

    Join Machine Learning Plus Univerity

  • Microsoft Malware Detection Project
     ML with Python path 

    4h 52m 21s

    [PREVIEW] Project Overview (03:35)

    Problem Description (5:00)

    [PREVIEW PIC] Solved Project Notebook with step-wise 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

    Join Machine Learning Plus Univerity

  • Credit Card Fraud Detection Project
     ML with Python path 

    5h 11m 17s

    [PREVIEW]Course Overview (03:09)

    Solved notebooks with step-wise explanations

    [PREVIEW PIC] Complete Industry Data

    [PREVIEW] Data Overview (05:56)

    Optimize Memory Usage (05:23)

    [PREVIEW] 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)

    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

    Join Machine Learning Plus Univerity

  • Base-R Programming
     ML with R path 

    6h 48m 18s

    [PREVIEW] Course Overview

    Installing R Studio (6:30)

    R Studio walkthrough (11:10)

    [PREVIEW] Datatypes (6:45)

    [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 Ad-campaign (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 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)

    Mapply (3:20)

    Summary (2:20)

    Assignment/ Challenges

    Certificate of completion

    Join Machine Learning Plus Univerity

  • Dplyr for Data Wrangling
     ML with R path 

    36m 47s

    [PREVIEW] Course Overview (02:00)

    [PREVIEW] 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

    Join Machine Learning Plus Univerity"> Certificate of completion

    Join Machine Learning Plus Univerity

  • Wrangling Data with Data.Table
     ML with R path 

    50m 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

    Join Machine Learning Plus Univerity

  • GGPlot2 Visualization for Data Analysis
     ML with R path 

    1h 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)

    [PREVIEW] 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

    Join Machine Learning Plus Univerity

  • Statistical Foundations for ML in R
     ML with R path 

    3h 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 T-Tests (08:34)

    One Sample T-Test (08:44)

    One Sample T-Test - R Demo (06:33)

    One Sample T-Test - Hand Computation (08:23)

    What is Two Sample Paired T-Test (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 T-test (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 Chi-Squared Test (03:09)

    Chi-Squared Test - Examples of When to Use (02:04)

    Chi Square Test - Hand Computation (10:22)

    Chi-Squared 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

    Join Machine Learning Plus Univerity

  • Regression Modeling in R
     ML with R path 

    5h 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] 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

    Join Machine Learning Plus Univerity

  • Caret Package for ML in R
     ML with R path 

    2h 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

    Join Machine Learning Plus Univerity

  • Spacy for NLP
     Specialized Courses 

    4h 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

    Join Machine Learning Plus Univerity

  • Data Processing and EDA
     ML with Python   Launching Oct21 

    5h 31m 23s

    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

    Join Machine Learning Plus Univerity

  • Regression and Regularization
     ML with Python   Launching Oct21 

    5h 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 - 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

    Join Machine Learning Plus Univerity

  • Getting Started with Classification Algorithms
     ML with Python   Launching Oct21 

    3h 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, 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

    Join Machine Learning Plus Univerity

  • Handling Imbalanced Data for Classification
     ML with Python   Launching Nov21 

    2h 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 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

    Join Machine Learning Plus Univerity

  • Supervised Learning
     ML with Python   Launching Nov21 

    5h 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

    Join Machine Learning Plus Univerity

  • Ensemble Learning
     ML with Python   Launching Dec21 

    5h 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 trade-off

    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 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)

    Assignment/ Challenges

    Certificate of completion

    Join Machine Learning Plus Univerity

  • Feature Engineering
     ML with Python   Launching Dec21 

    5h 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

    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

    Join Machine Learning Plus Univerity

  • Feature Selection
     ML with Python   Launching Jan22 

    3h 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

    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

    Join Machine Learning Plus Univerity

  • Dimensionality Reduction
     ML with Python   Launching Jan22 

    4h 55m 23s

    Curse of Dimensionality

    How Dimensionality Reduction Solves the Problem

    Intuitions Behind PCA

    Maths Behind PCA

    PCA Implementation

    Intuitions Behind t-SNE

    Maths Behind t-SNE

    T-SNE 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 Non-Negative Matrix Factorization

    Maths Behind Non-Negative Matrix Factorization

    Non-Negative Matrix Factorization implementation

    Summary

    Assignment/ Challenges

    Certificate of completion

    Join Machine Learning Plus Univerity

  • Deployment of ML Models
     ML with Python   Launching Jan22 

    9h 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 Open-Source

    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 Scikit-learn API

    Create Scikit-Learn 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 Heroku-Specific 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

    Join Machine Learning Plus Univerity

  • Unsupervised Learning
     ML with Python   Launching Feb22 

    6h 09m 23s

    What is unsupervised learning

    Industrial Applications of unsupervised learning

    Intuitions behind k-means

    Maths behind k-means

    How to select the optimal number of clusters

    K-means ++

    Problem Statement

    EDA and data preprocessing

    Model Building

    Find the optimal number of clusters

    Visualize the clusters

    Intuitions behind k-medoids

    Maths behind k-medoids

    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 fuzzy-c clustering

    Maths behind fuzzy-c 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

    Join Machine Learning Plus Univerity

  • Recommendation System
     ML with Python   Launching Feb22 

    5h 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

    Top-N Recommender Architecture

    Ranks

    [Activity] The Basics of Python

    Data Structures in Python

    Functions in Python

    [Exercise] Booleans, loops, and a hands-on challenge

    Train/test and cross validation

    Accuracy Metrics (RMSE, MAE)

    Top-N 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

    K-Nearest-Neighbors and Content Recs

    [Activity] Producing and Evaluating Content-Based Movie Recommendations

    A Note on Using Implicit Ratings.

    [Activity] Bleeding Edge Alert! Mise en Scene Recommendations

    [Exercise] Dive Deeper into Content-Based Recommendations

    Measuring Similarity and Sparsity

    Similarity Metrics

    User based collaborative Filtering

    Preview

    [Activity] User-based Collaborative Filtering, Hands-On

    Item-based Collaborative Filtering

    [Activity] Item-based Collaborative Filtering, Hands-On

    [Exercise] Tuning Collaborative Filtering Algorithms

    [Activity] Evaluating Collaborative Filtering Systems Offline

    [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering

    KNN Recommenders

    [Activity] Running User and Item-Based KNN on MovieLens

    [Exercise] Experiment with different KNN parameters.

    Bleeding Edge Alert! Translation-Based 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 Pre-Requisites

    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

    Auto-Encoders 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: Multi-Task 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 Value-Aware 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

    Join Machine Learning Plus Univerity

Course reviews

My students have published novel research papers, changed their careers from developers to machine learning practitioners, successfully applied machine learning to their work projects, landed positions at global companies, and successfully created new projects for their clients. Take a look and see for yourself how Machine Learning University can help you in your journey.

4.84 Based on 67 Reviews
  • 5 stars

    80%

  • 4 stars

    70%

  • 3 stars

    60%

  • 2 stars

    50%

  • 1 star

    40%

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.

Ayush K.
Data Scientist, Microsoft

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

Jyoti Goyal
Senior IT Consultant, Amazon

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!

Lorenna Christina
RPA Developer, Smarthis

The ML path is an absolute no-brainer ..Even with zero Data Science experience I was able to grasp all the concepts. Helped me big time in my projects

Pratik Sarangi
Management Consultant, Deloitte

MLPlus’s ML program has amazing content..such nice explanations. Would look out for more such courses.

Souptik Dhar
Data Analyst, Google

Despite being in this field for some years, this adds a lot of value for easy understanding of concepts..must have for beginners.

Surya Parmeswaran
Senior Data Scientist, Groupon
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 state-of-the-art techniques

104h 21m on-demand video

26 courses on essential machine learning topics

26 Certificates of Completion

1141 tutorials and downloadable resources

Pre-configured 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 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 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 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!

Course Preview

Machine Learning A-Z™: Hands-On Python & R In Data Science

Free Sample Videos:

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science