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    Complete 2022 Data Science & Machine Learning Bootcamp

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 4999

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 4,999
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction to the Course

    • What is Machine Learning?
    • What is Data Science?
    • Download the Syllabus
    • Top Tips for Succeeding on this Course
    • Course Resources List

    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

    Dr Angela Yu
    Developer and Lead Instructor
    Udemy

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