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Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 4,999
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction to the Course
Predict Movie Box Office Revenue with Linear Regression
- Introduction to Linear Regression & Specifying the Problem
- Gather & Clean the Data
- Explore & Visualise the Data with Python
- The Intuition behind the Linear Regression Model
- Analyse and Evaluate the Results
- Download the Complete Notebook Here
- Join the Student Community
- Any Feedback on this Section?
Python Programming for Data Science and Machine Learning
- Windows Users - Install Anaconda
- Mac Users - Install Anaconda
- Does LSD Make You Better at Maths?
- Download the 12 Rules to Learn to Code
- [Python] - Variables and Types
- Python Variable Coding Exercise
- [Python] - Lists and Arrays
- Python Lists Coding Exercise
- [Python & Pandas] - Dataframes and Series
- [Python] - Module Imports
- [Python] - Functions - Part 1: Defining and Calling Functions
- Python Functions Coding Exercise - Part 1
- [Python] - Functions - Part 2: Arguments & Parameters
- Python Functions Coding Exercise - Part 2
- [Python] - Functions - Part 3: Results & Return Values
- Python Functions Coding Exercise - Part 3
- [Python] - Objects - Understanding Attributes and Methods
- How to Make Sense of Python Documentation for Data Visualisation
- Working with Python Objects to Analyse Data
- [Python] - Tips, Code Style and Naming Conventions
- Download the Complete Notebook Here
- Any Feedback on this Section?
Introduction to Optimisation and the Gradient Descent Algorithm
- What's Coming Up?
- How a Machine Learns
- Introduction to Cost Functions
- LaTeX Markdown and Generating Data with Numpy
- Understanding the Power Rule & Creating Charts with Subplots
- [Python] - Loops and the Gradient Descent Algorithm
- Python Loops Coding Exercise
- [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)
- [Python] - Tuples and the Pitfalls of Optimisation (Part 2)
- Understanding the Learning Rate
- How to Create 3-Dimensional Charts
- Understanding Partial Derivatives and How to use SymPy
- Implementing Batch Gradient Descent with SymPy
- [Python] - Loops and Performance Considerations
- Reshaping and Slicing N-Dimensional Arrays
- Concatenating Numpy Arrays
- Introduction to the Mean Squared Error (MSE)
- Transposing and Reshaping Arrays
- Implementing a MSE Cost Function
- Understanding Nested Loops and Plotting the MSE Function (Part 1)
- Plotting the Mean Squared Error (MSE) on a Surface (Part 2)
- Running Gradient Descent with a MSE Cost Function
- Visualising the Optimisation on a 3D Surface
- Download the Complete Notebook Here
- Any Feedback on this Section?
Predict House Prices with Multivariable Linear Regression
- Defining the Problem
- Gathering the Boston House Price Data
- Clean and Explore the Data (Part 1): Understand the Nature of the Dataset
- Clean and Explore the Data (Part 2): Find Missing Values
- Visualising Data (Part 1): Historams, Distributions & Outliers
- Visualising Data (Part 2): Seaborn and Probability Density Functions
- Working with Index Data, Pandas Series, and Dummy Variables
- Understanding Descriptive Statistics: the Mean vs the Median
- Introduction to Correlation: Understanding Strength & Direction
- Calculating Correlations and the Problem posed by Multicollinearity
- Visualising Correlations with a Heatmap
- Techniques to Style Scatter Plots
- A Note for the Next Lesson
- Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques
- Understanding Multivariable Regression
- How to Shuffle and Split Training & Testing Data
- Running a Multivariable Regression
- How to Calculate the Model Fit with R-Squared
- Introduction to Model Evaluation
- Improving the Model by Transforming the Data
- How to Interpret Coefficients using p-Values and Statistical Significance
- Understanding VIF & Testing for Multicollinearity
- Model Simplification & Baysian Information Criterion
- How to Analyse and Plot Regression Residuals
- Residual Analysis (Part 1): Predicted vs Actual Values
- Residual Analysis (Part 2): Graphing and Comparing Regression Residuals
- Making Predictions (Part 1): MSE & R-Squared
- Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals
- Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays
- [Python] - Conditional Statements - Build a Valuation Tool (Part 2)
- Python Conditional Statement Coding Exercise
- Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module
- Download the Complete Notebook Here
- Any Feedback on this Section?
Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1
- How to Translate a Business Problem into a Machine Learning Problem
- Gathering Email Data and Working with Archives & Text Editors
- How to Add the Lesson Resources to the Project
- The Naive Bayes Algorithm and the Decision Boundary for a Classifier
- Basic Probability
- Joint & Conditional Probability
- Bayes Theorem
- Reading Files (Part 1): Absolute Paths and Relative Paths
- Reading Files (Part 2): Stream Objects and Email Structure
- Extracting the Text in the Email Body
- [Python] - Generator Functions & the yield Keyword
- Create a Pandas DataFrame of Email Bodies
- Cleaning Data (Part 1): Check for Empty Emails & Null Entries
- Cleaning Data (Part 2): Working with a DataFrame Index
- Saving a JSON File with Pandas
- Data Visualisation (Part 1): Pie Charts
- Data Visualisation (Part 2): Donut Charts
- Introduction to Natural Language Processing (NLP)
- Tokenizing, Removing Stop Words and the Python Set Data Structure
- Word Stemming & Removing Punctuation
- Removing HTML tags with BeautifulSoup
- Creating a Function for Text Processing
- A Note for the Next Lesson
- Advanced Subsetting on DataFrames: the apply() Function
- [Python] - Logical Operators to Create Subsets and Indices
- Word Clouds & How to install Additional Python Packages
- Creating your First Word Cloud
- Styling the Word Cloud with a Mask
- Solving the Hamlet Challenge
- Styling Word Clouds with Custom Fonts
- Create the Vocabulary for the Spam Classifier
- Coding Challenge: Check for Membership in a Collection
- Coding Challenge: Find the Longest Email
- Sparse Matrix (Part 1): Split the Training and Testing Data
- Sparse Matrix (Part 2): Data Munging with Nested Loops
- Sparse Matrix (Part 3): Using groupby() and Saving .txt Files
- Coding Challenge Solution: Preparing the Test Data
- Checkpoint: Understanding the Data
- Download the Complete Notebook Here
- Any Feedback on this Section?
Train a Naive Bayes Classifier to Create a Spam Filter: Part 2
- Setting up the Notebook and Understanding Delimiters in a Dataset
- Create a Full Matrix
- Count the Tokens to Train the Naive Bayes Model
- Sum the Tokens across the Spam and Ham Subsets
- Calculate the Token Probabilities and Save the Trained Model
- Coding Challenge: Prepare the Test Data
- Download the Complete Notebook Here
- Any Feedback on this Section?
Test and Evaluate a Naive Bayes Classifier: Part 3
- Set up the Testing Notebook
- Joint Conditional Probability (Part 1): Dot Product
- Joint Conditional Probablity (Part 2): Priors
- Making Predictions: Comparing Joint Probabilities
- The Accuracy Metric
- Visualising the Decision Boundary
- False Positive vs False Negatives
- The Recall Metric
- The Precision Metric
- The F-score or F1 Metric
- A Naive Bayes Implementation using SciKit Learn
- Download the Complete Notebook Here
- Any Feedback on this Section?
Introduction to Neural Networks and How to Use Pre-Trained Models
- The Human Brain and the Inspiration for Artificial Neural Networks
- Layers, Feature Generation and Learning
- Costs and Disadvantages of Neural Networks
- Preprocessing Image Data and How RGB Works
- Importing Keras Models and the Tensorflow Graph
- Making Predictions using InceptionResNet
- Coding Challenge Solution: Using other Keras Models
- Download the Complete Notebook Here
- Any Feedback on this Section?
Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
- Solving a Business Problem with Image Classification
- Installing Tensorflow and Keras for Jupyter
- Gathering the CIFAR 10 Dataset
- Exploring the CIFAR Data
- Pre-processing: Scaling Inputs and Creating a Validation Dataset
- Compiling a Keras Model and Understanding the Cross Entropy Loss Function
- Interacting with the Operating System and the Python Try-Catch Block
- Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems
- Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques
- Use the Model to Make Predictions
- Model Evaluation and the Confusion Matrix
- Model Evaluation and the Confusion Matrix
- Download the Complete Notebook Here
- Any Feedback on this Section?
Use Tensorflow to Classify Handwritten Digits
- What's coming up?
- Getting the Data and Loading it into Numpy Arrays
- Data Exploration and Understanding the Structure of the Input Data
- Data Preprocessing: One-Hot Encoding and Creating the Validation Dataset
- What is a Tensor?
- Creating Tensors and Setting up the Neural Network Architecture
- Defining the Cross Entropy Loss Function, the Optimizer and the Metrics
- TensorFlow Sessions and Batching Data
- Tensorboard Summaries and the Filewriter
- Understanding the Tensorflow Graph: Nodes and Edges
- Name Scoping and Image Visualisation in Tensorboard
- Different Model Architectures: Experimenting with Dropout
- Prediction and Model Evaluation
- Download the Complete Notebook Here
- Any Feedback on this Section?
Serving a Tensorflow Model through a Website
- What you'll make
- Saving Tensorflow Models
- Loading a SavedModel
- Converting a Model to Tensorflow.js
- Introducing the Website Project and Tooling
- HTML and CSS Styling
- Loading a Tensorflow.js Model and Starting your own Server
- Adding a Favicon
- Styling an HTML Canvas
- Drawing on an HTML Canvas
- Data Pre-Processing for Tensorflow.js
- Introduction to OpenCV
- Resizing and Adding Padding to Images
- Calculating the Centre of Mass and Shifting the Image
- Making a Prediction from a Digit drawn on the HTML Canvas
- Adding the Game Logic
- Publish and Share your Website!
- Any Feedback on this Section?
Next Steps
- Where next?
- What Modules Do You Want to See?
- Stay in Touch!
Instructors
Dr Angela Yu
Developer and Lead Instructor
Udemy
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