Complete 2020 Data Science & Machine Learning Bootcamp

Complete 2020 Data Science & Machine Learning Bootcamp

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 41 Hours | 17.1 GB


Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.

At over 35+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here’s why:
The course is a taught by the lead instructor at the App Brewery, London’s leading in-person programming bootcamp.
In the course, you’ll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
This course doesn’t cut any corners, there are beautiful animated explanation videos and real-world projects to build.
The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
You’ll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.

We’ll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.

The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.

  • In the curriculum, we cover a large number of important data science and machine learning topics, such as:
  • Data Cleaning and Pre-Processing
  • Data Exploration and Visualisation
  • Linear Regression
  • Multivariable Regression
  • Optimisation Algorithms and Gradient Descent
  • Naive Bayes Classification
  • Descriptive Statistics and Probability Theory
  • Neural Networks and Deep Learning
  • Model Evaluation and Analysis
  • Serving a Tensorflow Model

Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:

  • Python 3
  • Tensorflow
  • Pandas
  • Numpy
  • Scikit Learn
  • Keras
  • Matplotlib
  • Seaborn
  • SciPy
  • SymPy

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:

  • Data Types and Variables
  • String Manipulation
  • Functions
  • Objects
  • Lists, Tuples and Dictionaries
  • Loops and Iterators
  • Conditionals and Control Flow
  • Generator Functions
  • Context Managers and Name Scoping
  • Error Handling

By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.

What you’ll learn

  • You will learn how to program using Python through practical projects
  • Use data science algorithms to analyse data in real life projects such as spam classification and image
  • recognition
  • Build a portfolio of data science projects to apply for jobs in the industry
  • Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many
  • more
  • Create your own neural networks and understand how to use them to perform deep learning
  • Understand and apply data visualisation techniques to explore large datasets


+ Table of Contents

Introduction to the Course
1 What is Machine Learning
2 What is Data Science
3 Download the Syllabus
4 Top Tips for Succeeding on this Course
5 Course Resources List

Predict Movie Box Office Revenue with Linear Regression
6 Introduction to Linear Regression & Specifying the Problem
7 Gather & Clean the Data
8 Explore & Visualise the Data with Python
9 The Intuition behind the Linear Regression Model
10 Analyse and Evaluate the Results
11 Download the Complete Notebook Here
12 Join the Student Community
13 Any Feedback on this Section

Python Programming for Data Science and Machine Learning
14 Windows Users – Install Anaconda
15 Mac Users – Install Anaconda
16 Does LSD Make You Better at Maths
17 Download the 12 Rules to Learn to Code
18 [Python] – Variables and Types
19 [Python] – Lists and Arrays
20 [Python & Pandas] – Dataframes and Series
21 [Python] – Module Imports
22 [Python] – Functions – Part 1 Defining and Calling Functions
23 [Python] – Functions – Part 2 Arguments & Parameters
24 [Python] – Functions – Part 3 Results & Return Values
25 [Python] – Objects – Understanding Attributes and Methods
26 How to Make Sense of Python Documentation for Data Visualisation
27 Working with Python Objects to Analyse Data
28 [Python] – Tips, Code Style and Naming Conventions
29 Download the Complete Notebook Here
30 Any Feedback on this Section

Introduction to Optimisation and the Gradient Descent Algorithm
31 What’s Coming Up
32 How a Machine Learns
33 Introduction to Cost Functions
34 LaTeX Markdown and Generating Data with Numpy
35 Understanding the Power Rule & Creating Charts with Subplots
36 [Python] – Loops and the Gradient Descent Algorithm
37 [Python] – Advanced Functions and the Pitfalls of Optimisation (Part 1)
38 [Python] – Tuples and the Pitfalls of Optimisation (Part 2)
39 Understanding the Learning Rate
40 How to Create 3-Dimensional Charts
41 Understanding Partial Derivatives and How to use SymPy
42 Implementing Batch Gradient Descent with SymPy
43 [Python] – Loops and Performance Considerations
44 Reshaping and Slicing N-Dimensional Arrays
45 Concatenating Numpy Arrays
46 Introduction to the Mean Squared Error (MSE)
47 Transposing and Reshaping Arrays
48 Implementing a MSE Cost Function
49 Understanding Nested Loops and Plotting the MSE Function (Part 1)
50 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)
51 Running Gradient Descent with a MSE Cost Function
52 Visualising the Optimisation on a 3D Surface
53 Download the Complete Notebook Here
54 Any Feedback on this Section

Predict House Prices with Multivariable Linear Regression
55 Defining the Problem
56 Gathering the Boston House Price Data
57 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset
58 Clean and Explore the Data (Part 2) Find Missing Values
59 Visualising Data (Part 1) Historams, Distributions & Outliers
60 Visualising Data (Part 2) Seaborn and Probability Density Functions
61 Working with Index Data, Pandas Series, and Dummy Variables
62 Understanding Descriptive Statistics the Mean vs the Median
63 Introduction to Correlation Understanding Strength & Direction
64 Calculating Correlations and the Problem posed by Multicollinearity
65 Visualising Correlations with a Heatmap
66 Techniques to Style Scatter Plots
67 A Note for the Next Lesson
68 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques
69 Understanding Multivariable Regression
70 How to Shuffle and Split Training & Testing Data
71 Running a Multivariable Regression
72 How to Calculate the Model Fit with R-Squared
73 Introduction to Model Evaluation
74 Improving the Model by Transforming the Data
75 How to Interpret Coefficients using p-Values and Statistical Significance
76 Understanding VIF & Testing for Multicollinearity
77 Model Simplification & Baysian Information Criterion
78 How to Analyse and Plot Regression Residuals
79 Residual Analysis (Part 1) Predicted vs Actual Values
80 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals
81 Making Predictions (Part 1) MSE & R-Squared
82 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals
83 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays
84 [Python] – Conditional Statements – Build a Valuation Tool (Part 2)
85 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module
86 Download the Complete Notebook Here
87 Any Feedback on this Section

Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1
88 How to Translate a Business Problem into a Machine Learning Problem
89 Gathering Email Data and Working with Archives & Text Editors
90 How to Add the Lesson Resources to the Project
91 The Naive Bayes Algorithm and the Decision Boundary for a Classifier
92 Basic Probability
93 Joint & Conditional Probability
94 Bayes Theorem
95 Reading Files (Part 1) Absolute Paths and Relative Paths
96 Reading Files (Part 2) Stream Objects and Email Structure
97 Extracting the Text in the Email Body
98 [Python] – Generator Functions & the yield Keyword
99 Create a Pandas DataFrame of Email Bodies
100 Cleaning Data (Part 1) Check for Empty Emails & Null Entries
101 Cleaning Data (Part 2) Working with a DataFrame Index
102 Saving a JSON File with Pandas
103 Data Visualisation (Part 1) Pie Charts
104 Data Visualisation (Part 2) Donut Charts
105 Introduction to Natural Language Processing (NLP)
106 Tokenizing, Removing Stop Words and the Python Set Data Structure
107 Word Stemming & Removing Punctuation
108 Removing HTML tags with BeautifulSoup
109 Creating a Function for Text Processing
110 A Note for the Next Lesson
111 Advanced Subsetting on DataFrames the apply() Function
112 [Python] – Logical Operators to Create Subsets and Indices
113 Word Clouds & How to install Additional Python Packages
114 Creating your First Word Cloud
115 Styling the Word Cloud with a Mask
116 Solving the Hamlet Challenge
117 Styling Word Clouds with Custom Fonts
118 Create the Vocabulary for the Spam Classifier
119 Coding Challenge Check for Membership in a Collection
120 Coding Challenge Find the Longest Email
121 Sparse Matrix (Part 1) Split the Training and Testing Data
122 Sparse Matrix (Part 2) Data Munging with Nested Loops
123 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files
124 Coding Challenge Solution Preparing the Test Data
125 Checkpoint Understanding the Data
126 Download the Complete Notebook Here
127 Any Feedback on this Section

Train a Naive Bayes Classifier to Create a Spam Filter Part 2
128 Setting up the Notebook and Understanding Delimiters in a Dataset
129 Create a Full Matrix
130 Count the Tokens to Train the Naive Bayes Model
131 Sum the Tokens across the Spam and Ham Subsets
132 Calculate the Token Probabilities and Save the Trained Model
133 Coding Challenge Prepare the Test Data
134 Download the Complete Notebook Here
135 Any Feedback on this Section

Test and Evaluate a Naive Bayes Classifier Part 3
136 Set up the Testing Notebook
137 Joint Conditional Probability (Part 1) Dot Product
138 Joint Conditional Probablity (Part 2) Priors
139 Making Predictions Comparing Joint Probabilities
140 The Accuracy Metric
141 Visualising the Decision Boundary
142 False Positive vs False Negatives
143 The Recall Metric
144 The Precision Metric
145 The F-score or F1 Metric
146 A Naive Bayes Implementation using SciKit Learn
147 Download the Complete Notebook Here
148 Any Feedback on this Section

Introduction to Neural Networks and How to Use Pre-Trained Models
149 The Human Brain and the Inspiration for Artificial Neural Networks
150 Layers, Feature Generation and Learning
151 Costs and Disadvantages of Neural Networks
152 Preprocessing Image Data and How RGB Works
153 Importing Keras Models and the Tensorflow Graph
154 Making Predictions using InceptionResNet
155 Coding Challenge Solution Using other Keras Models
156 Download the Complete Notebook Here
157 Any Feedback on this Section

Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
158 Solving a Business Problem with Image Classification
159 Installing Tensorflow and Keras for Jupyter
160 Gathering the CIFAR 10 Dataset
161 Exploring the CIFAR Data
162 Pre-processing Scaling Inputs and Creating a Validation Dataset
163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function
164 Interacting with the Operating System and the Python Try-Catch Block
165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems
166 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques
167 Use the Model to Make Predictions
168 Model Evaluation and the Confusion Matrix
169 Model Evaluation and the Confusion Matrix
170 Download the Complete Notebook Here
171 Any Feedback on this Section

Use Tensorflow to Classify Handwritten Digits
172 What’s coming up
173 Getting the Data and Loading it into Numpy Arrays
174 Data Exploration and Understanding the Structure of the Input Data
175 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset
176 What is a Tensor
177 Creating Tensors and Setting up the Neural Network Architecture
178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics
179 TensorFlow Sessions and Batching Data
180 Tensorboard Summaries and the Filewriter
181 Understanding the Tensorflow Graph Nodes and Edges
182 Name Scoping and Image Visualisation in Tensorboard
183 Different Model Architectures Experimenting with Dropout
184 Prediction and Model Evaluation
185 Download the Complete Notebook Here
186 Any Feedback on this Section

Serving a Tensorflow Model through a Website
187 What you’ll make
188 Saving Tensorflow Models
189 Loading a SavedModel
190 Converting a Model to Tensorflow.js
191 Introducing the Website Project and Tooling
192 HTML and CSS Styling
193 Loading a Tensorflow.js Model and Starting your own Server
194 Adding a Favicon
195 Styling an HTML Canvas
196 Drawing on an HTML Canvas
197 Data Pre-Processing for Tensorflow.js
198 Introduction to OpenCV
199 Resizing and Adding Padding to Images
200 Calculating the Centre of Mass and Shifting the Image
201 Making a Prediction from a Digit drawn on the HTML Canvas
202 Adding the Game Logic
203 Publish and Share your Website!
204 Any Feedback on this Section

Next Steps
205 Where next
206 What Modules Do You Want to See
207 Stay in Touch!