Introduction to the Course

What is Machine Learning?
What is Data Science?
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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!
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Next Steps

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Complete 2022 Data Science & Machine Learning Bootcamp

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Quick Facts

Course and certificate fees

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certificate availability

certificate providing authority

The syllabus

Introduction to the Course

Predict Movie Box Office Revenue with Linear Regression

Python Programming for Data Science and Machine Learning

Introduction to Optimisation and the Gradient Descent Algorithm

Predict House Prices with Multivariable Linear Regression

Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1

Train a Naive Bayes Classifier to Create a Spam Filter: Part 2

Test and Evaluate a Naive Bayes Classifier: Part 3

Introduction to Neural Networks and How to Use Pre-Trained Models

Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow

Use Tensorflow to Classify Handwritten Digits

Serving a Tensorflow Model through a Website

Next Steps

Instructors

Articles

Popular Articles

Latest Articles

Trending Courses

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Download Careers360 App

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Popular Searches

Complete 2022 Data Science & Machine Learning Bootcamp

Online

₹ 4999

Quick Facts

Course and certificate fees

Fees information

certificate availability

certificate providing authority

The syllabus

Introduction to the Course

Predict Movie Box Office Revenue with Linear Regression

Python Programming for Data Science and Machine Learning

Introduction to Optimisation and the Gradient Descent Algorithm

Predict House Prices with Multivariable Linear Regression

Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1

Train a Naive Bayes Classifier to Create a Spam Filter: Part 2

Test and Evaluate a Naive Bayes Classifier: Part 3

Introduction to Neural Networks and How to Use Pre-Trained Models

Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow

Use Tensorflow to Classify Handwritten Digits

Serving a Tensorflow Model through a Website

Next Steps

Instructors

Articles

Popular Articles

Latest Articles

Trending Courses

Popular Courses

Popular Platforms

Learn more about the Courses

Thank You!

Download Careers360 App

All this at the convenience of your phone