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Machine Learning Plus University: You can master Machine Learning

I strongly believe that if you had the right teacher you could completely master machine learning

Do you think mastering Machine Learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science?

Well, that’s exactly not the case.

All you need to master machine learning is for someone to explain things to you in simple, intuitive terms. And that’s exactly what I do. My mission is to change machine learning education and how complex Artificial Intelligence topics are taught.

Welcome to Machine Learning Plus University, the most comprehensive machine learning course online today. Here you’ll learn how to successfully and confidently apply machine learning to your work, research, and projects. Join me in complete machine learning mastery.

Created by: Selva Prabhakaran, Principal Data Scientist •Last updated: 9/2021 •Languages: English

4.56 (67Ratings) • 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 PyImageSearch University, you need the following:

  • Understanding of Python basics
  • Windows, macOS, Linux, or Raspbian (all major operating systems supported)
  • Free Gmail/Google account to run pre-configured Jupyter Notebooks in Colab (optional)
  • A desire to learn
Course description

Machine Learning Plus University is a comprehensive set of self-paced courses for developers, students, and researchers who are ready to master machine learning. Inside this University, you’ll learn how to successfully and confidently apply machine learning to your work, research, and projects.

Unlike other online courses, which are created once and never updated, leaving you with stale, out-of-date information, I keep Machine Learning Plus University up-to-date by releasing a brand new course every month!

Releasing a new course every month ensures you can keep up with the state-of-the-art in machine learning, learn new algorithms and techniques, and:

  • Successfully complete your projects at work
  • Perform novel research (and publish papers)
  • Finish your final graduation project for school
  • Launch your next company in the Artificial Intelligence space

To help you accomplish these goals, in each lesson I provide:

  • Detailed video tutorials for every lesson
  • High-quality, well documented source code with line-by-line explanations (ensuring you know exactly what the code is doing)
  • Jupyter Notebooks that are pre-configured to run in Google Colab
  • Support for all major operating systems (Windows, macOS, Linux, and Raspbian)

Machine Learning Plus University is without a doubt the most complete, comprehensive computer vision education online inside. I’ll see you inside.

Selva Prabhakaran
Creator, machinelearningplus.com

Trusted by members of top artificial intelligence companies, schools, and organizations
Who this course is for:

If any of these descriptions fit you, rest assured, Machine Learning Plus University is designed for you.

  • You are a machine learning practitioner that utilizes machine learning at your day job, and you’re eager to level-up your skills.
  • You’re a developer who wants to learn machine learning, complete your challenging project at work, and stand out from your coworkers (and land that big promotion).
  • You are a college student who needs help with your homework, completing your final graduation project, or you simply want more than what your university offers.
  • You are a researcher or scientist looking to apply machine learning techniques to your research (and publish a paper).
  • You have experience with machine learning and want to learn more about niche topics and applications
  • You are an entrepreneur studying machine learning so you can launch your next business in the Artificial Intelligence space.
  • You are a “machine learning hobbyist” who wants to successfully complete that project you are hacking on over nights and weekends.
  • You’re a Machine Learning Plus reader that wants access to centralized repos containing high-quality, well documented source code, pre-trained models, image datasets, etc. for all the tutorials on machinelearningplus.com.
  • You prefer running code examples with Jupyter Notebooks in Google Colab — my notebooks are pre-configured and ready to run in Google Colab with only a single click.
  • You learn best through video tutorials — Machine Learning Plus University includes video guides for every single lesson.
14 Certificates of Completion

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 14 courses inside Machine Learning Plus University.

And since a brand new course is released every month, that means each month you receive…

  • A brand new course
  • A new set of lessons
  • A new set of quizzes
  • A new final exam
  • And another opportunity to demonstrate your machine learning knowledge to the world

Machine Learning graduates have gone on to:

  • Perform novel research and publish papers in prestigious journals
  • 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

25 Courses •128 Classes •37h 19m 02s Lectures

  • Numpy for Data Science
      ML with Python path  

    12 lessons, 2h 06m 18s

    [Preview Concept] How ARIMA works

    Lesson Overview3 (02:13)

    Resources (02:13)

    Data Pre-configured Notebook

    Introduction to Numpy (02:19)

    Python Setup - Google Colab (05:47)

    Python Setup - Local Installation (10:37)

    Rotation (11:01)

    Creating Arrays (10:48)

    Inspecting Arrays (05:24)

    Cropping (10:16)

    Why Data Types Matter (08:43)

    Xyz lesson

    Import and Export Data (07:33)

    Missing Data (03:47)

    Extracting Specific Items (12:50)

    Random Numbers (11:22)

    Set Operations (06:17)

    Statistical Summaries (06:28)

    Reshaping and New Axis (08:15)

    Sequences and Repetitions (5:30)

    Mesh Grid (04:06)

    Concatenate and Split (06:11)

    At and iat (04:12)

    Mini Challenge (01:32)

    Filtering Data Sorting Methods (08:30)

    Searchsorted and Bisect (06:21)

    Handling Dates (07:04)

    Sorting (02:42)

    Vectorization (05:24)

    Apply Along (06:13)

    Useful Functions (07:19)

    Broadcasting (06:47)

    Universal Functions (UFuncs) (03:37)

    Interpolation (06:48)

    Fitting Polynomials (09:24)

    Matrix Operations (10:43)

    Solving Linear Equations (02:33)

    Closing Remarks (01:47)

    Summary (02:04)

    Assignment/ Challenges

    Certificate of completion

    Click here to join Machine Learning Plus University

  • Pandas for Data Science
      ML with Python path  

    10 lessons, 2h 32m 07s

    Data Code download Pre-configured Notebook Lesson assessment

    Overview (02:13)

    Introduction to Pandas (03:19)

    Need For DataFrame (03:47)

    Creating Dataframe (08:37)

    Mini Challenge (00:48)

    Series (07:24)

    Mini Challenge (02:43)

    Python Setup Google Colab (04:33)

    Python Setup Local Installation (09:47)

    Inspecting Dataframes (04:50)

    Renaming Columns (04:22)

    Pandas Summary (03:17)

    Essential Operations (04:28)

    Display Options (02:15)

    Extracting Specific Part of Data - Part 1 (5:30)

    Extracting Specific Part of Data - Part 2 (02:06)

    Mini Challenge (01:11)

    At and iat (04:12)

    Mini Challenge (01:32)

    Filtering Data That Satisfy Conditions (05:30)

    Membership Filtering (04:21)

    Query and Eval (04:04)

    Sorting (02:42)

    Map and applymap (07:24)

    Apply a function rowwise or columnwise (7:02)

    Scaling and Standardization (05:05)

    Make Index as a Dataframe Column (02:09)

    Discretization and Binning (05:26)

    Random Sampling (05:13)

    Dummy Variables (02:03)

    Categorical Data Part-1 (3:32)

    Categorical Data Part-2 (03:07)

    Efficiently Read Data From Multiple Files (05:30)

    Group by Mechanism (3:10)

    Mini Challenge (02:04)

    Iterating Between groups (3:02)

    Transform (04:05)

    Reshaping and Pivoting Data (02:09)

    Cross Tabulation (02:26)

    Pivoting (04:13)

    Wide to Long and Back (07:03)

    Joining Dataframes (04:32)

    Types of Joins (06:07)

    Concatenating Dataframes (2:08)

    Representing Missing Values (05:07)

    Threshold Based Dropping (02:30)

    Approaches To Filling Missing Data (04:18)

    Interpolation (06:18)

    Compressed File Formats (4:02)

    Sparse Datatype (03:05)

    Combining Categories (03:09)

    Split Contents of a Column (01:26)

    Insert Column at Specific Location (02:13)

    Select using both Position and Lab (04:03)

    Styling Dataframes (02:32)

    Comprehensive Profile Report (04:07)

    Interactive Plots (2:08)

    Third Party Data (04:07)

    Interactive Data Analysis (05:30)

    Optimizing Dataframes (05:18)

    Sampling On Load (04:18)

    Efficient File Formats (2:02)

    HDF5 (06:05)

    Chunking (04:09)

    Load to Database (06:26)

    Faster Pandas (01:13)

    Numba (07:05)

    Dask Part-1 (07:09)

    Dask Part-2 (05:26)

    Modin (06:13)

    Swifter (05:03)

    Vaex (13:32)

    Cython (07:07)

    Cythonize Pandas Code (6:08)

    Cythonize Apply (06:07)

    Matplotlib Part 1 - Getting Started (07:30)

    Matplotlib Part 2 - Plot Components (07:18)

    Matplotlib Part 3 - Subplots (07:18)

    Matplotlib Part 4 - Annotations (05:02)

    Dual Axis Line Plots (07:05)

    Bar Charts (06:09)

    Histogram and Density Plots (02:26)

    Regression Plots (06:13)

    Pair Plots (07:13)

    Summary (02:45)

    Assignment/ Challenges

    Certificate of completion

    Click here to join PyImageSearch University

  • Fundamentals of Machine Learning
      ML with Python path  

    12 lessons, 2h 06m 18s

    Why Machine Learning

    What is Machine Learning?

    Rule Bases Systems vs ML Based Approach

    Garbage in - Garbage out

    Broad Types of ML Problems

    Real World Applications of ML

    What Machine Learning Can and Cannot Do

    Data Science vs AI vs ML

    Introduction to ML Project Workflow

    ML Project - 'Discover' phase

    ML Project - 'Design' phase

    ML Project - 'Develop' phase

    ML Project - 'Validate' phase

    ML Project - 'Test' phase

    ML Project - 'Deploy' phase

    What is an ML Model?

    Interpreting ML Model

    How to validate ML Models?

    Need for Validation Sample

    ML Terminology you need to know

    Overview of ML Algorithms

    What is Ensemble Learning

    Reinforcement Learning

    Recommendation Systems

    Basic Statistical Terms

    Role of Statistical Significance Tests

    Commercially Viable Projects by Industry

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Restaurant Visitor Forecasting Project
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Introduction and What You Will Learn (05:03)

    Download Resources (01:02)

    Data Overview (04:46)

    Basic Data Stats (08:33)

    Exploratory Data Analysis (08:34)

    Feature Engineering - Domain Specific (04:43)

    Feature Engineering - Interaction Features (03:54)

    Exploratory Data Analysis Part 2 (04:34)

    ANOVA Concept - Intuition (10:07)

    ANOVA Concept - Maths (13:22)

    Significance Tests (08:23)

    Label Encoding (02:04)

    Data Preprocessing for Model Building (04:08)

    Feature Encoding Approaches (08:43)

    Evaluation Metrics (05:43)

    Introduction to Time Series Modeling (01:09)

    Time Series and Stationarity Concepts (11:09)

    ACF and PACF (08:02)

    Introduction to ARIMA Modeling (05:26)

    AR and MA Models - Part 1 (06:55)

    AR and MA Models - Part 2 (06:23)

    Auto Arima Concept (03:45)

    Strategy 1 - ARIMA Forecasting Demo (07:34)

    Strategy 1 - ARIMA Forecasting Demo - Part 2 (08:55)

    Durbin Watson Statistic (03:08)

    Strategy 2 - Auto ARIMA (08:12)

    Strategy 3 - AutoARIMA for Genre (09:34)

    SARIMA and SARIMAX (06:33)

    Strategy 4 SARIMA Demo (06:34)

    Strategy 5 SARIMAX Demo (10:18)

    Exogenous Variables Deepdive Part 1 - Time Based and Demographics (05:03)

    Exogenous Variables Deepdive Part 2 - Qualitative and Promotions (05:04)

    Exogenous Variables Deepdive Part 3 - Series decomposition and LifeCycle (12:22)

    Exogenous Variables Deepdive Part 4 - Macro Economic Features (03:34)

    Forecast for Unknown Future (02:43)

    Prophet by Facebook (04:09)

    Prophet Forecasting Demo (08:49)

    New Plan of Attack (03:56)

    XGBoost Demo (08:34)

    CatBoost (05:34)

    CatBoost Demo (07:23)

    Hyper Parameters and Tuning (07:12)

    Cohorted Ensembles (07:45)

    Cohorted Ensembles Demo (04:34)

    Final Words (2:45)

    Assignment/ Challenges

    Certificate of completion

  • Microsoft Malware Detection Project
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Overview (4 mins)

    Problem Description (5:00)

    Download Resources (1:22)

    Python Setup - Google Colab (05:06)

    Python Setup - Local Installation(10:02)

    Data Overview (12:21)

    Optimize Memory Usage (11:19)

    Understand The Data (8:00)

    Data Preprocessing for EDA (7:00)

    Need for EDA (5:39)

    Exploratory Data Analysis Part 1 (14:00)

    Exploratory Data Analysis Part 2 (13:23)

    Chi Squared Test Theory and Maths (18:45)

    Chi Square Test and Odds Ratio Demo (09:34)

    ANOVA Concept - Intuition (10:23)

    ANOVA maths (06:05)

    ANOVA Demo (08:00)

    Feature Engineering - Domain Specific (08:00)

    Feature Encoding Approaches (08:00)

    Feature Encoding Demo (08:34)

    Data Processing for Model Building (05:23)

    Confusion Matrix and Evaluation Metrics (10:19)

    Concordance and Discordance (09:34)

    Precision Recall Curve (03:34)

    Evaluation Metrics Demo (05:09)

    Decision Trees and Improvements (17:09)

    Random Forests (09:00)

    XGBoost (07:09)

    LightGBM (08:55)

    Tuning Hyperparameters (07:06)

    Feature Importance (11:34)

    Feature Importance Demo (02:09)

    Final Words (01:08)

    Assignment/ Challenges

    Certificate of completion

  • Credit Card Fraud Detection Project
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Overview (03:09)

    Data Overview (05:56)

    Optimize Memory Usage (05:23)

    Basic Stats (06:09)

    Data Preprocessing for EDA (04:19)

    Exploratory Data Analysis (12:00)

    Chi Squared Test Theory and Maths (18:09)

    Chi Square Test and Odds Ratio Demo (09:00)

    ANOVA Concept - Intuition (10:04)

    ANOVA Cancept - Maths (13:03)

    ANOVA Demo (07:04)

    Feature Engineering (05:09)

    Feature Engineering (05:09)

    Feature Encoding Approaches (8:05)

    Feature Encoding Demo (04:09)

    Data Preprocessing for Model Building (03:34)

    Why XGBoost (04:03)

    XGBoost Demo (02:00)

    Confusion Matrix and Evaluation Metrics (09:04)

    Concordance and Discordance (09:56)

    ROC Curve (09:18)

    Precision Recall Curve (03:56)

    Evaluation Metrics Demo (03:19)

    Capture Rates and Calibration Curve (11:45)

    Light GBM (08:45)

    Light GBM Demo (04:45)

    Random Forest Introduction (01:02)

    Decision Trees Algorithm (13:49)

    Random Forest Classifier Algorithm (06:00)

    Extra Trees Algorithm (06:03)

    Random Forests Demo (09:09)

    Random Oversampler (10:03)

    Cost Sensitive Learning with Class Weights (07:34)

    Probability Calibration (08:55)

    Model Calibration Demo (02:34)

    Model Tuning (07:37)

    Feature Engineering (05:30)

    Feature Importance (11:20)

    Feature Importance Demo (03:10)

    Partial Dependence and ICE Plots (06:40)

    SHAP Interpretations (07:40)

    SHAP Demo (05:00)

    XGBoost Demo (02:00)

    Final Words (02:45)

    Final Words (02:45)

    Assignment/ Challenges

    Certificate of completion

  • Base R-Programming
      ML with Python path  

    12 lessons, 2h 06m 18s

    Overview (2 Lessons, 49:02)

    Course Overview (1:20)

    Installing R Studio (6:30)

    R Studio walkthrough (11:10)

    Datatypes (6:45)

    Data Structures (3:06)

    Vectorization (3:47)

    Online Pizza Advertisement (2:20)

    Create vector with a single element (10:56)

    Create group of elements in a vector (12:10)

    Use repetitions and sequence to create a vector fast (6:16)

    Random numbers, rounding and sampling (9:00)

    Formatting numbers (3:36)

    Create subsets (8:04)

    Handling Missing Values (10:00)

    Binning (9:09)

    Operations within a vector (9:12)

    Operations between same size vectors (3:44)

    Operations between different sized vectors (4:20)

    Revenue impact of Ad-campaign (4:55)

    Module Overview (1:30)

    Set Operations (5:19)

    If and ifelse (4:19)

    Making assignments within ifelse (2:48)

    Checking existence (1:16)

    Nested if-else (3:00)

    For loops (4:00)

    Writing smarter For loops (2:00)

    Break while repeat (5:08)

    Memory pre-allocation tactics (5:05)

    Why Dates cant just be strings (5:09)

    Date operations (1:00

    Working with lubridate and anytime (5:00)

    Introduction to lists (6:03)

    Named list, unlist and more (7:08)

    Introduction to Dataframe (3:48)

    Creating Dataframe (3:34)

    Visual editing (2:25)

    Various dataframe operations (11:06)

    Inspecting and Rownames (10:23)

    Select, delete, subset (7:09)

    Attributes and comments (4:04)

    Saving dataframe to disk (7 :20)

    Native RDS files (2:34)

    Handling CSV files (2:09)

    Xlsx files (2:37)

    SAS and Stata files (4 :07)

    R datasets, packages and public datasets (11:04)

    Useful data summarization function (8:17)

    Conditional filtering and missing values (10:45)

    Matrix vs dataframe (6:39)

    Joining operations for dataframes (9:28)

    Pivot and frequency table (11:09)

    Grouping and case problem solution (9:29)

    Module Overview (2:35)

    Base Graphics basics (2 :24)

    Scatterplot (1:34)

    Adding plot components (8:45)

    Legend (1:34)

    Saving plot components and challenge (3:34)

    Line plot with secondary Y axis (7:39)

    Change Par settings (4:24)

    Histogram and bar charts (6:34)

    Box plot (3:20)

    Dot plot and density plot (5:34)

    Multiple plots and custom layouts (5:00)

    Intro to stringr (5:25)

    Sentences, punctuations, strings manipulations (6:45)

    Writing effective functions (7:20)

    Local and global namespace (12:45)

    Debugging R code (9:56)

    Error handling (7:40)

    Apply Function (3:00)

    Apply, sapply, vapply (4:10)

    Apply, sapply, vapply (4:10)

    Mapply (3:20)

    Summary (2:20)

    Assignment/ Challenges

    Certificate of completion

  • Dplyr for Data Wrangling
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Overview (02:00)

    Getting started with Dplyr pipes (06:20)

    T-pipe (01:20)

    Compound assignment and exposition pipe (02:10)

    Tibble (04:30)

    Types of joins in dplyr (03:10)

    Joins demo (04:40)

    Joins challenges (03:30)

    Assignment/ Challenges

    Certificate of completion

  • Wrangling Data with Data.Table
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Overview (01:09)

    Introduction to datatable (03:04)

    Creating datatable and importing (07:03)

    Datatable syntax (01:04)

    Filtering and subsetting (04:05)

    Creating new columns (07:06)

    Running multiple statements in one (02:07)

    Groupby operations (04:40)

    Special symbols (04:30)

    Applying functions (03:40)

    Go faster with keys (10:20)

    Fast loops with set function (07:30)

    Assignment/ Challenges

    Certificate of completion

  • GGPlot2 Visualization for Data Analysis
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Overview (04:08)

    Introduction to GGPlot2 (05:04)

    Scatterplot with geom point() (02:07)

    Line of Best Fit and Smoothing Lines (01:09)

    Adjust X and Y Limits (02:35)

    Confidence Interval Shading (02:23)

    Changing Color of Points (03:45)

    Changing Size (01:56)

    Removing Legend (01:45)

    Color Palette (01:45)

    Customize Axis Text (02:45)

    Look and Feel (04:10)

    Labels and Text (05:56)

    Custom Annotations (02:30)

    Legend (05:30)

    Better Represent Overlapping Points with Jitter and Counts Plot (03:40)

    Multiplot with Facets (06:40)

    Custom Layout (01:30)

    Histograms (08:50)

    Bar Charts (02:40)

    Box Plot and Violin Plots (04:33)

    Time Series Plots (06:45)

    Multiple Time Series in Same Plot (03:00)

    Assignment/ Challenges

    Certificate of completion

  • Regression Modeling in R
      ML with Python path  

    12 lessons, 2h 06m 18s

    Course Overview (01:45)

    What is Linear Regression (05:45)

    Understanding with Practical Examples (02:45)

    Examples of Industrial Applications (02:33)

    Statistical Modeling vs Machine Learning (02:23)

    Graphical Understanding (03:34)

    Formulate Line of Best Fit (04:34)

    Linear Regression from Scratch using Formula (07:24)

    R-Squared Explained (07:45)

    Pre-Model Build Analysis (05:45)

    Building and Interpreting Linear Regression Models (05:45)

    Problem with R-Squared (02:34)

    Adjusted R-Squared (01:34)

    F-Statistic, AIC, BIC (03:34)

    R-Demo (04:34)

    Assumptions (18:34)

    Problem Statement (03:20)

    Handling Missing Values (09:20)

    Outlier Analysis (04:27)

    Graphical and Statistical Analysis (10:20)

    Building Linear Regression (07:00)

    Good Model (03:05)

    Evaluation Measures (05:00)

    Need for Cross Validation (12:03)

    Cross Validation Approaches (05:30)

    Variable Transformations and Interactions (05:20)

    Variance Inflation Factor (04:30)

    Cooks Distance for Influential Points (06:10)

    Cooks Distance Demo (05:04)

    BoxCox and YeoJohnson Transformations (06:20)

    Residual Analysis (06:20)

    Overcoming Heteroscedasticity (08:20)

    Stepwise Regression for Model Search (05:05)

    Best Subsets Model Search (06:05)

    What Exactly is Gradient Descent (08:30)

    How Gradient Descent Learns (06:20)

    Comparing Types of Gradient Descent (03:10)

    Stopping Criteria and Scaling (02:20)

    Types of Gradient Descent (04:17)

    Introduction to Logistic Regression (03:40)

    One vs Rest Strategy (02:40)

    Use Case Examples (04:20)

    Math behind Logistic Regression Part 1 (04:20)

    Math behind Logistic Regression Part 2 (04:40)

    Why Negative Logloss (03:20)

    Problem Statement Marketing (01:20)

    Demo - EDA for Logit (09:11)

    Building Logistic Regression Model (05:33)

    McFadden's R-Squared (03:30)

    Confusion Matrix and Evaluation Metrics (09:30)

    Precision Recall Curve (03:20)

    Optimal Cutoff Score (05 :10)

    The ROC Curve (02:03)

    KS Statistic and Gain Curve (11:10)

    Concordant and Discordant pairs (06:20)

    Approaches to Handle Class Imbalance (06:33)

    Cost Sensitive Learning (06:28)

    Oversampling (03:17)

    Hybrid Sampling (05:40)

    Assignment/ Challenges

    Certificate of completion

  • Caret Package for ML in R
      ML with Python path  

    12 lessons, 2h 06m 18s

    What is Caret (04:00)

    Problem Statement (03:08)

    Sampling Approaches (08:04)

    Missing Value Treatment (07:05)

    One Hot Encoding (04:05)

    Box Cox Transformation Concept (02:05)

    Applying Box Cox and PCA (06:06)

    Feature Plots (05:06)

    Statistical Analysis (05:06)

    Recursive Feature Elimination (RFE) Algorithm (03:05)

    Recursive Feature Elimination (RFE Demo) (03:22)

    Genetic Algorithms (03:43)

    Genetic Algorithms part 2 - Feature Selection (09:33)

    Genetic Algorithms Demo (06:34)

    Simulated Annealing Concept (06:45)

    Simulated Annealing Demo (05:25)

    Simulated Annealing Demo (05:25)

    Building ML with train function (08:23)

    Automatic Hyperparameter Tuning (04:34)

    Customize Hyperparameter Search (02:24)

    Comparing Adaboost xgboost random forest and svm (02:34)

    Tuning Non Default Parameters with Caret (03:45)

    Caret Ensembles (04:00)

    Assignment/ Challenges

    Certificate of completion

  • Spacy for NLP
      Specialized Courses  

    12 lessons, 2h 06m 18s

    Overview of Spacy (05:07)

    Common Use Cases (04:19)

    Introduction to Spacy Models (08:11)

    What Happens Inside Spacy Models (03:02)

    Install Spacy and Models (04:05)

    Data Structures in Spacy (04:15)

    Data structures in Spacy - Make a doc from scratch (03:05)

    Data structures in Spacy - nlp.make.doc() (03:09)

    String Store (05:06)

    How Tokenization Works (04:18)

    Create Custom Tokenization Rules (06:19)

    Build Custom Tokenizer (04:09)

    Named Entity Recognition (07:06)

    POS Tagging (05:18)

    Dependency Parsing (01:05)

    Need for Dependency Parsing (02:13)

    Navigating the Parse Tree (02:20)

    Noun Chunks (04:13)

    Contextual Token Matching (08:15)

    Phrase Matching (07:10)

    Handling Large Number of Patterns (04:11)

    Course Review (01:01)

    Understanding Spacy Pipelines (04:00)

    How to Apply Particular Component (04:03)

    Manipulating the Pipeline (04:14)

    Entity Ruler (02:12)

    How to Create Custom Pipeline Components (03:12)

    Custom Sentence Segmenter (06:07)

    Efficient Text Processing Approaches (07:16)

    User Defined Attributes (04:07)

    Property Extensions (02:21)

    Method Extensions (3:08)

    Need for Word Vectors (11:03)

    Advantage of Vector Representation (05:00)

    How Word Vectors are Computed (09:08)

    Understanding Cosine Similarity (09:22)

    The Training Loop (04:11)

    Train a New NER Model from Scratch (07:10)

    Training Custom NER Models (05:09)

    Training Custom NER Models - Part 2 (03:04)

    Training Custom NER Models - Part 3 (03:00)

    Training Custom NER Models - Part 4 (3:10)

    Text Classification with TextCat - Part 1 (4:08)

    Text Classification with TextCat - Part 2 (2:02)

    Text Classification with TextCat - Part 3 (3:10)

    Text Classification with TextCat - Part 4 (2:00)

    Text Classification with TextCat - Part 5 (3:05)

    Text Classification with TextCat - Part 6 (5:15)

    Training Customer POS Tagger Model - Part 1 (4:09)

    Training Customer POS Tagger Model - Part 2 (5:07)

    Training Custom Intent Parser - Part 1 (5:06)

    Training Custom Intent Parser - Part 2 (3:07)

    Training Custom Intent Parser - Part 3 (3:02)

    Training Custom Intent Parser - Part 4 (5:20)

    Building Chatbot with Spacy and Rasa (6:20)

    Intent Classification (4:07)

    NLU Pipeline (2:09)

    Training NLU Model (3:00)

    RASA Core (2:11)

    Writing Stories (8:18)

    Assignment/ Challenges

    Certificate of completion

  • Data Processing and EDA
      ML with Python       Launching Oct21  

    12 lessons, 2h 06m 18s

    Measures of Central Tendency

    Measures of Dispersion

    Correlation and Correlation Test

    Chi-Squared Test

    ANOVA

    Types of missing values

    Identify missing values

    Visualise missing values using 'missingo'

    Dropping rows or columns with missing values (how and when)

    Replace with 0, mean, median, mode, most frequent, string

    Causes of missing values and when simple methods fail?

    Predict the missing value

    How to Validate the Accuracy of Your Missing Value Treatment Approach

    How to spot Outliers- Box plot interpretation - IQR

    How to spot Outliers-Z score

    Multivariate outliers- Multivariate

    Multivariate outliers- Series

    Local Outlier factor

    Isolation forest

    Feature Scaling

    Feature Encoding

    Standardization vs Normalization

    Plotting Correlation: Relationship between Two Numerical Variables- Scatterplot and Lines of Best Fit

    Plotting Correlation: Relationship between two numerical variables- Correlation matrix with Heatmap

    Histogram and Density plot

    Bar Plot, Stacked

    Box Plot and Violin Plot

    Multiple Box Plots

    Bubble Plots

    Pair Plots

    Trellis Chart or Facets

    Mosaic Plots

    Line Plots

    Line Plots with Multiple Lines

    Line Plots with Secondary Axis

    Autocorrelation Plot

    Time Series Seasonal Plots- Multi-Line chart

    Time Series Seasonal Plots- The monthly and yearly box plot

    Waterfall Chart (How to Create and Where is it Used?)

    3D Plots

    Problem Statement

    Data Overview

    Univariate Analysis

    Bivariate Analysis - Statistical Significance Tests

    EDA - Get Business Insight

    Problem Statement

    Data Overview

    Univariate Analysis

    Bivariate Analysis - Statistical Significance Tests

    EDA - Get Business Insight

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Regression and Regularization
      ML with Python       Launching Oct21  

    12 lessons, 2h 06m 18s

    Introduction to Linear Regression

    Industrial Applications of Regression

    Simple Linear Regression - Ordinary Least Square (OLS)

    Multiple Linear Regression - Intuition

    Cost Function

    Gradient Descent

    Model Diagnostics - R square and Adj R square

    Model Diagnostics - p-value

    Model Diagnostics - VIF

    Model Diagnostics - AIC/BIC

    Assumptions of Linear Regression

    Mean Absolute Error

    Mean Absolute Percentage Error

    Mean Squared Error

    Room Mean Squared Error

    Problem Statement

    Data Overview

    EDA

    Data Preprocessing

    Various Approaches to implementing Linear Regression

    Forward Stepwise Regression

    Backward Stepwise Regression

    Stepwise Regression

    Sklearn Implementation

    Model Evaluation

    Check Assumptions of Linear regression

    Intuition and Motivation

    Polynomial Regression Implementation

    How to Choose the Degree of the Polynomial

    Underfitting and Overfitting

    Cross-Validation

    L1 Regularization Intuition

    L2 Regularization Intuition

    Elastic net Regularization Intuition

    L1 Regularization Implementation

    L2 Regularization Implementation

    Elastic-net Regularization Implementation

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Foundations of Classification Algorithms
      ML with Python     Launching Oct21  

    12 lessons, 2h 06m 18s

    Introduction to Logistic Regression

    Industrial Applications of Logistic Regression/Classification

    Solve Multiclass Classification using Logistic Regression

    Graphical and Geometric Representation

    The Logistic Function

    Why use Logistic Regression over Linear Regression

    Linear Regression to Logistic Regression

    Maths behind Logistic Regression

    Accuracy

    Confusion Matrix - TP, TN, FP, FN

    Classification Report - Precision, Recall, F1-score

    ROC Curve

    AUC

    KS Statistic and Gain curve

    Concordant and Discordant Pair

    Problem Statement

    Data Overview

    EDA

    Data Preprocessing

    Forward Stepwise Regression

    Model Implementation

    Model Evaluation

    L1 Regularization Implementation

    L2 Regularization Implementation

    Elastic-net Regularization Implementation

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Handling Imbalanced Data for Classification
      ML with Python       Launching Oct21  

    12 lessons, 2h 06m 18s

    What is imbalanced data

    Why is an imbalanced data problem for classification

    How to fix the problem of class imbalance

    Upsampling minority class using resample

    Undersampling majority class using resample

    Random undersampling using imbalanced-learn

    Random oversampling using imbalanced-learn

    Oversampling using SMOTE

    Oversampling followed by under-sampling

    Using inbuilt class_wight parameter of algorithms

    ADASYN

    One-Class SVM

    Mahalanobis Distance

    LightGBM with the focal loss

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Supervised Learning
      ML with Python     Launching Oct21  

    12 lessons, 2h 06m 18s

    Introduction to KNN algorithm

    Intuition behind KNN

    Maths behind KNN

    How to choose the optimal value for k

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Processing

    Model Refinement Using Different Values for k

    Hyperparameter tuning to find the best value of k

    Hyperparameter tuning to find the best value of k

    The Intuition behind Naive Bayes

    Various Types of Naive Bayes

    Problem Statement

    Data Overview and Preprocessing

    Model Building - Gaussian Naive Bayes

    Model Building - Bernoulli Naive Bayes

    Model Evaluation

    Introduction to SVM Algorithm

    The Intuition Behind SVM Classification

    Maths Behind SVM

    Various Kernels of SVM

    Kernel Tricks

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement Using Different Kernels

    Model Improvement Using Hyperparameter Tuning

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using Different Kernels

    Introduction to Decision Tree Algorithm

    The Intuition Behind Decision Tree Classification

    The Intuition Behind Decision Tree Regression

    The Measure of Impurity Classification

    The Measure of Impurity Regression

    How to Visualize and Interpret Decision Tree

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using Different Kernels

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Ensemble Learning
      ML with Python     Launching Oct21  

    12 lessons, 2h 06m 18s

    What is Ensemble Learning

    Bagging vs Boosting

    Evolution of Boosting Algorithms

    Introduction to Random Forest

    The Intuition Behind Random Forest

    Maths Behind Random Forest

    Bootstrap Aggregation

    Various parameters of random forest

    Bias variance trade-off

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using various parameters

    Hyperparameter tuning

    Introduction to AdaBoost

    The Intuition Behind AdaBoost

    Maths behind AdaBoost

    Various parameters of AdaBoost

    Bias variance trade-off

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using various parameters

    Hyperparameter tuning

    Introduction to CatBoost

    The intuition behind CatBoost

    Maths behind CatBoost

    Various parameters of CatBoost

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using various parameters

    Hyperparameter tuning

    Introduction to Gradient Boosting

    The Intuition behind Gradient Boosting

    Maths behind Gradient Boosting

    Various parameters of Gradient Boosting

    Bias Variance trade-off

    The Intuition Behind XGBoost

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using various parameters

    Hyperparameter tuning

    Introduction to LightGBM

    The intuition behind LightGBM

    Maths behind LightGBM

    Various parameters of LightGBM

    Problem Statement

    Data Overview and Preprocessing

    Model Building

    Model Evaluation

    Model Refinement using various parameters

    Hyperparameter tuning

    Final Words (2:45)

    Assignments/challenges

    Certificate of completion

  • Feature Engineering
      ML with Python     Launching Oct21  

    12 lessons, 2h 06m 18s

    Overview of Numerical data

    Feature Standardization

    Feature Normalization

    Feature Binning

    Feature Transformation

    Feature Interactions

    Ranks

    Splines

    Overview of Categorical data

    Feature Encoding

    Dummy Coding

    Contrast Coding

    Bin Counting

    Feature Hashing

    Feature Interactions

    Overview of Text data

    Count Vectors

    Tf-idf Vectors

    Work Tokenization

    Bag of Words

    Stemming

    Lemmatization

    POS Tagging

    Reduce Dimensions using Feature Hashing

    Overview of Time Series data

    Date related feature engineering

    Time-related feature engineering

    Lag features

    Sliding window features

    Rolling window features

    Expanding window features

    Feature Interactions

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Feature Selection
      ML with Python     Launching Oct21  

    12 lessons, 2h 06m 18s

    What is Feature Selection

    Why Feature Selection

    Types of Feature Selection Algorithm

    Pearson Correlation Intuitions

    Pearson Correlation Implementation

    LDA Intuitions

    LDA Implementation

    ANOVA Intuitions

    ANOVA Implementation

    Chi-square Intuitions

    Chi-square Implementation

    Forward Selection Intuitions

    Forward Selection Implementation

    Backward Selection Intuitions

    Backward Selection Implementation

    Stepwise Selection Intuitions

    Stepwise Selection Implementation

    Recursive Features Selection Intuitions

    Recursive Feature Selection Implementation

    Lasso Regression Intuitions

    Lasso Regression Implementation

    Random Forest Intuitions

    Randorm Forest Implementation

    Summary

    Assignment/ Challenges

    Certificate of completion

  • Dimesionality Reduction
      ML with Python     Launching Oct21  

    12 lessons, 2h 06m 18s

    Curse of Dimensionality

    How Dimensionality Reduction Solves the Problem

    t1

    t2

    t3

Course reviews

My students have published novel research papers, changed their careers from developers to computer vision/deep learning practitioners, successfully applied CV/DL to their work projects, landed positions at R&D companies, and won grant/award funding for research. Take a look and see for yourself how PyImageSearch University can help you in your journey.

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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
Full Access Plan

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This course includes:

Full access to PyImageSearch University

Brand new courses released every month, ensuring you can keep up with state-of-the-art techniques

37h 19m on-demand video

25 courses on essential computer vision, deep learning, and OpenCV topics

25 Certificates of Completion

304 tutorials and downloadable resources

Pre-configured Jupyter Notebooks in Google Colab for 200+ PyImageSearch tutorials

Run all code examples in your web browser — works on Windows, macOS, and Linux (no dev environment configuration required!)

Access to centralized code repos for all 400+ tutorials on PyImageSearch

Easy one-click downloads for code, datasets, pre-trained models, etc.

Access on mobile, laptop, desktop, etc.

Frequently Asked Questions

Do I need programming experience before joining the University?

No. You are good to start with zero coding experience or background. We teach Python programming needed for data science, from scratch.

Do I need to know anything about machine learning to get started in Machine Learning Plus University?

No. The courses inside Machine Learning Plus University will teach you all concepts and implementation of Machine Learning. As long as you complete the courses and assignments diligently, you will be successful inside Machine Learning Plus University.

What happens affter I purchase?

After you purchase, you will be able to login and immediately access any code downloads, Jupyter Notebooks, video tutorials, courses, certificates of completion, etc.

Do I need any special software or hardware?

No. All of our courses, coding exercises, etc. can be completed inside your browser using our pre-configured Jupyter Notebooks running in Google Colab. If you prefer to instead configure your local development environment, we provide install instructions as well.

How will I be charged?

You will not be charged for the first 30 days. And you can cancel the trial anytime during the period, without any charges. If you continue the membership, you will be charged on a yearly or 6 monthly basis depending on the plan that you subscribed to.

Can I pause/cancel my subscription?

Yes. Once you login, click your ‘My profile’ icon on top right, followed by “Subscriptions”. From there you can edit your payment method or cancel/pause your membership.

Do you provide bulk Machine Learning Plus University memberships to business, colleges etc?

Yes. Just write an email to [email protected], and we can schedule a call to discuss getting your organization access to Machine Learning Plus University

Hi there — I’m Selva

About your teacher

Hey, I’m Selva Prabhakaran, a Principal Data Scientist of a global corporate and creator of machinelearningplus.com, who has spent his entire adult life studying machine learning and deploying multiple global products. Over the past 10 years I have:

  • Started the MachineLearningPlus.com blog and published over 300+ tutorials and articles aimed at teaching machine learning and deep learning – and impacting over 4 million readers annually
  • Authored multiple books and courses on Data Science that have collectively been taken up by over 10,000 students
  • Built competent data science teams for multi-national companies, and mentored over 50 data scientists, many of whom these data scientists have gone on to become data science leads in different firms
  • Answered over 20,000+ emails and helped 10,000s of developers, researchers, and students just like yourself learn the ropes of machine learning.

If having an actual experience of building data science models and products sounds interesting to you, join me inside Machine Learning Plus University. You’ll learn a ton about machine learning in a practical, hands-on way. And you’ll have fun doing it. See you on the other side!

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

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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