**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!

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