Complete 2020 Data Science & Machine Learning Bootcamp

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
eLearning | Skill level: All Levels

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

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

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

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

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

Train a Naive Bayes Classifier to Create a Spam Filter Part 2
123 Setting up the Notebook and Understanding Delimiters in a Dataset
124 Create a Full Matrix
125 Count the Tokens to Train the Naive Bayes Model
126 Sum the Tokens across the Spam and Ham Subsets
127 Calculate the Token Probabilities and Save the Trained Model
128 Coding Challenge Prepare the Test Data
129 Download the Complete Notebook Here

Test and Evaluate a Naive Bayes Classifier Part 3
130 Set up the Testing Notebook
131 The F-score or F1 Metric
132 A Naive Bayes Implementation using SciKit Learn
133 Download the Complete Notebook Here
134 Joint Conditional Probability (Part 1) Dot Product
135 Joint Conditional Probablity (Part 2) Priors
136 Making Predictions Comparing Joint Probabilities
137 The Accuracy Metric
138 Visualising the Decision Boundary
139 False Positive vs False Negatives
140 The Recall Metric
141 The Precision Metric

Introduction to Neural Networks and How to Use Pre-Trained Models
142 The Human Brain and the Inspiration for Artificial Neural Networks
143 Layers, Feature Generation and Learning
144 Costs and Disadvantages of Neural Networks
145 Preprocessing Image Data and How RGB Works
146 Importing Keras Models and the Tensorflow Graph
147 Making Predictions using InceptionResNet
148 Coding Challenge Solution Using other Keras Models
149 Download the Complete Notebook Here

Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
150 Solving a Business Problem with Image Classification
151 Use the Model to Make Predictions
152 Model Evaluation and the Confusion Matrix
153 Model Evaluation and the Confusion Matrix
154 Download the Complete Notebook Here
155 Installing Tensorflow and Keras for Jupyter
156 Gathering the CIFAR 10 Dataset
157 Exploring the CIFAR Data
158 Pre-processing Scaling Inputs and Creating a Validation Dataset
159 Compiling a Keras Model and Understanding the Cross Entropy Loss Function
160 Interacting with the Operating System and the Python Try-Catch Block
161 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems
162 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques

Use Tensorflow to Classify Handwritten Digits
163 What’s coming up
164 Understanding the Tensorflow Graph Nodes and Edges
165 Name Scoping and Image Visualisation in Tensorboard
166 Different Model Architectures Experimenting with Dropout
167 Prediction and Model Evaluation
168 Download the Complete Notebook Here
169 Getting the Data and Loading it into Numpy Arrays
170 Data Exploration and Understanding the Structure of the Input Data
171 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset
172 What is a Tensor
173 Creating Tensors and Setting up the Neural Network Architecture
174 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics
175 TensorFlow Sessions and Batching Data
176 Tensorboard Summaries and the Filewriter

Serving a Tensorflow Model through a Website
177 What you’ll make
178 Drawing on an HTML Canvas
179 Data Pre-Processing for Tensorflow.js
180 Introduction to OpenCV
181 Resizing and Addign Padding to Images
182 Calculating the Centre of Mass and Shifting the Image
183 Making a Prediction from a Digit drawn on the HTML Canvas
184 Adding the Game Logic
185 Publish and Share your Website!
186 Saving Tensorflow Models
187 Loading a SavedModel
188 Converting a Model to Tensorflow.js
189 Introducing the Website Project and Tooling
190 HTML and CSS Styling
191 Loading a Tensorflow.js Model and Starting your own Server
192 Adding a Favicon
193 Styling an HTML Canvas

Next Steps
194 Where next
195 What Modules Do You Want to See
196 Stay in Touch!