**Complete Data Science & Machine Learning Bootcamp – Python 3**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 35.5 Hours | 15.0 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

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

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

**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 Mac Users – Install Anaconda

15 Does LSD Make You Better at Maths?

16 Download the 12 Rules to Learn to Code

17 [Python] – Variables and Types

18 [Python] – Lists and Arrays

19 [Python & Pandas] – Dataframes and Series

20 [Python] – Module Imports

21 [Python] – Functions – Part 1: Defining and Calling Functions

22 [Python] – Functions – Part 2: Arguments & Parameters

23 [Python] – Functions – Part 3: Results & Return Values

24 [Python] – Objects – Understanding Attributes and Methods

25 How to Make Sense of Python Documentation for Data Visualisation

26 Working with Python Objects to Analyse Data

27 [Python] – Tips, Code Style and Naming Conventions

28 Download the Complete Notebook Here

**Introduction to Optimisation and the Gradient Descent Algorithm**

29 What’s Coming Up?

30 How a Machine Learns

31 Introduction to Cost Functions

32 LaTeX Markdown and Generating Data with Numpy

33 Understanding the Power Rule & Creating Charts with Subplots

34 [Python] – Loops and the Gradient Descent Algorithm

35 [Python] – Advanced Functions and the Pitfalls of Optimisation (Part 1)

36 [Python] – Tuples and the Pitfalls of Optimisation (Part 2)

37 Understanding the Learning Rate

38 How to Create 3-Dimensional Charts

39 Understanding Partial Derivatives and How to use SymPy

40 Implementing Batch Gradient Descent with SymPy

41 [Python] – Loops and Performance Considerations

42 Reshaping and Slicing N-Dimensional Arrays

43 Concatenating Numpy Arrays

44 Introduction to the Mean Squared Error (MSE)

45 Transposing and Reshaping Arrays

46 Implementing a MSE Cost Function

47 Understanding Nested Loops and Plotting the MSE Function (Part 1)

48 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)

49 Running Gradient Descent with a MSE Cost Function

50 Visualising the Optimisation on a 3D Surface

51 Download the Complete Notebook Here

**Predict House Prices with Multivariable Linear Regression**

52 Defining the Problem

53 Gathering the Boston House Price Data

54 Clean and Explore the Data (Part 1): Understand the Nature of the Dataset

55 Clean and Explore the Data (Part 2): Find Missing Values

56 Visualising Data (Part 1): Historams, Distributions & Outliers

57 Visualising Data (Part 2): Seaborn and Probability Density Functions

58 Working with Index Data, Pandas Series, and Dummy Variables

59 Understanding Descriptive Statistics: the Mean vs the Median

60 Introduction to Correlation: Understanding Strength & Direction

61 Calculating Correlations and the Problem posed by Multicollinearity

62 Visualising Correlations with a Heatmap

63 Techniques to Style Scatter Plots

64 A Note for the Next Lesson

65 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques

66 Understanding Multivariable Regression

67 How to Shuffle and Split Training & Testing Data

68 Running a Multivariable Regression

69 How to Calculate the Model Fit with R-Squared

70 Introduction to Model Evaluation

71 Improving the Model by Transforming the Data

72 How to Interpret Coefficients using p-Values and Statistical Significance

73 Understanding VIF & Testing for Multicollinearity

74 Model Simiplication & Baysian Information Criterion

75 How to Analyse and Plot Regression Residuals

76 Residual Analysis (Part 1): Predicted vs Actual Values

77 Residual Analysis (Part 2): Graphing and Comparing Regression Residuals

78 Making Predictions (Part 1): MSE & R-Squared

79 Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals

80 Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays

81 [Python] – Conditional Statements – Build a Valuation Tool (Part 2)

82 Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module

83 Download the Complete Notebook Here

**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 Gathering Email Data and Working with Archives & Text Editors

86 How to Add the Lesson Resources to the Project

87 The Naive Bayes Algorithm and the Decision Boundary for a Classifier

88 Basic Probability

89 Joint & Conditional Probability

90 Bayes Theorem

91 Reading Files (Part 1): Absolute Paths and Relative Paths

92 Reading Files (Part 2): Stream Objects and Email Structure

93 Extracting the Text in the Email Body

94 [Python] – Generator Functions & the yield Keyword

95 Create a Pandas DataFrame of Email Bodies

96 Cleaning Data (Part 1): Check for Empty Emails & Null Entries

97 Cleaning Data (Part 2): Working with a DataFrame Index

98 Saving a JSON File with Pandas

99 Data Visualisation (Part 1): Pie Charts

100 Data Visualisation (Part 2): Donut Charts

101 Introduction to Natural Language Processing (NLP)

102 Tokenizing, Removing Stop Words and the Python Set Data Structure

103 Word Stemming & Removing Punctuation

104 Removing HTML tags with BeautifulSoup

105 Creating a Function for Text Processing

106 A Note for the Next Lesson

107 Advanced Subsetting on DataFrames: the apply() Function

108 [Python] – Logical Operators to Create Subsets and Indices

109 Word Clouds & How to install Additional Python Packages

110 Creating your First Word Cloud

111 Styling the Word Cloud with a Mask

112 Solving the Hamlet Challenge

113 Styling Word Clouds with Custom Fonts

114 Create the Vocabulary for the Spam Classifier

115 Coding Challenge: Check for Membership in a Collection

116 Coding Challenge: Find the Longest Email

117 Sparse Matrix (Part 1): Split the Training and Testing Data

118 Sparse Matrix (Part 2): Data Munging with Nested Loops

119 Sparse Matrix (Part 3): Using groupby() and Saving .txt Files

120 Coding Challenge Solution: Preparing the Test Data

121 Checkpoint: Understanding the Data

122 Download the Complete Notebook Here

**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 Joint Conditional Probability (Part 1): Dot Product

132 Joint Conditional Probablity (Part 2): Priors

133 Making Predictions: Comparing Joint Probabilities

134 The Accuracy Metric

135 Visualising the Decision Boundary

136 False Positive vs False Negatives

137 The Recall Metric

138 The Precision Metric

139 The F-score or F1 Metric

140 A Naive Bayes Implementation using SciKit Learn

141 Download the Complete Notebook Here

**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 Installing Tensorflow and Keras for Jupyter

152 Gathering the CIFAR 10 Dataset

153 Exploring the CIFAR Data

154 Pre-processing: Scaling Inputs and Creating a Validation Dataset

155 Compiling a Keras Model and Understanding the Cross Entropy Loss Function

156 Interacting with the Operating System and the Python Try-Catch Block

157 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems

158 Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques

159 Use the Model to Make Predictions

160 Model Evaluation and the Confusion Matrix

161 Model Evaluation and the Confusion Matrix

162 Download the Complete Notebook Here

**Use Tensorflow to Classify Handwritten Digits**

163 What’s coming up?

164 Getting the Data and Loading it into Numpy Arrays

165 Data Exploration and Understanding the Structure of the Input Data

166 Data Preprocessing: One-Hot Encoding and Creating the Validation Dataset

167 What is a Tensor?

168 Creating Tensors and Setting up the Neural Network Architecture

169 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics

170 TensorFlow Sessions and Batching Data

171 Tensorboard Summaries and the Filewriter

172 Understanding the Tensorflow Graph: Nodes and Edges

173 Name Scoping and Image Visualisation in Tensorboard

174 Different Model Architectures: Experimenting with Dropout

175 Prediction and Model Evaluation

176 Download the Complete Notebook Here

**Next Steps**

177 Where next?

178 What Modules Do You Want to See?

179 Stay in Touch!

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