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Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

- Data Exploration and Visualizations
- Neural Networks and Deep Learning
- Model Evaluation and Analysis
- Python 3
- Tensorflow 2.0
- Numpy
- Scikit-Learn
- Data Science and Machine Learning Projects and Workflows
- Data Visualization in Python with MatPlotLib and Seaborn
- Transfer Learning
- Image recognition and classification
- Train/Test and cross validation
- Supervised Learning: Classification, Regression and Time Series
- Decision Trees and Random Forests
- Ensemble Learning
- Hyperparameter Tuning
- Using Pandas Data Frames to solve complex tasks
- Use Pandas to handle CSV Files
- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
- Using Kaggle and entering Machine Learning competitions
- How to present your findings and impress your boss
- How to clean and prepare your data for analysis
- K Nearest Neighbours
- Support Vector Machines
- Regression analysis (Linear Regression/Polynomial Regression)
- How Hadoop, Apache Spark, Kafka, and Apache Flink are used
- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
- Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don’t really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

What you’ll learn

- Become a Data Scientist and get hired
- Master Machine Learning and use it on the job
- Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
- Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
- Present Data Science projects to management and stakeholders
- Learn which Machine Learning model to choose for each type of problem
- Real life case studies and projects to understand how things are done in the real world
- Learn best practices when it comes to Data Science Workflow
- Implement Machine Learning algorithms
- Learn how to program in Python using the latest Python 3
- How to improve your Machine Learning Models
- Learn to pre process data, clean data, and analyze large data.
- Build a portfolio of work to have on your resume
- Developer Environment setup for Data Science and Machine Learning
- Supervised and Unsupervised Learning
- Machine Learning on Time Series data
- Explore large datasets using data visualization tools like Matplotlib and Seaborn
- Explore large datasets and wrangle data using Pandas
- Learn NumPy and how it is used in Machine Learning
- A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
- Learn to use the popular library Scikit-learn in your projects
- Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
- Learn to perform Classification and Regression modelling
- Learn how to apply Transfer Learning

Table of Contents

**Introduction**

1 Course Outline

2 Join Our Online Classroom!

3 Exercise Meet The Community

4 Your First Day

**Machine Learning 101**

5 What Is Machine Learning

6 AIMachine LearningData Science

7 Exercise Machine Learning Playground

8 How Did We Get Here

9 Exercise YouTube Recommendation Engine

10 Types of Machine Learning

11 Are You Getting It Yet

12 What Is Machine Learning Round 2

13 Section Review

**Machine Learning and Data Science Framework**

14 Section Overview

15 Introducing Our Framework

16 Step Machine Learning Framework

17 Types of Machine Learning Problems

18 Types of Data

19 Types of Evaluation

20 Features In Data

21 Modelling – Splitting Data

22 Modelling – Picking the Model

23 Modelling – Tuning

24 Modelling – Comparison

25 Overfitting and Underfitting Definitions

26 Experimentation

27 Tools We Will Use

28 Optional Elements of AI

**The 2 Paths**

29 The 2 Paths

30 Python + Machine Learning Monthly

31 Endorsements On LinkedIN

**Data Science Environment Setup**

32 Section Overview

33 Introducing Our Tools

34 What is Conda

35 Conda Environments

36 Mac Environment Setup

37 Mac Environment Setup 2

38 Windows Environment Setup

39 Windows Environment Setup 2

40 Linux Environment Setup

41 Sharing your Conda Environment

42 Jupyter Notebook Walkthrough

43 Jupyter Notebook Walkthrough 2

44 Jupyter Notebook Walkthrough 3

**Pandas Data Analysis**

45 Section Overview

46 Downloading Workbooks and Assignments

47 Pandas Introduction

48 Series, Data Frames and CSVs

49 Data from URLs

50 Describing Data with Pandas

51 Selecting and Viewing Data with Pandas

52 Selecting and Viewing Data with Pandas Part 2

53 Manipulating Data

54 Manipulating Data 2

55 Manipulating Data 3

56 Assignment Pandas Practice

57 How To Download The Course Assignments

**NumPy**

58 Section Overview

59 NumPy Introduction

60 Quick Note Correction In Next Video

61 NumPy DataTypes and Attributes

62 Creating NumPy Arrays

63 NumPy Random Seed

64 Viewing Arrays and Matrices

65 Manipulating Arrays

66 Manipulating Arrays 2

67 Standard Deviation and Variance

68 Reshape and Transpose

69 Dot Product vs Element Wise

70 Exercise Nut Butter Store Sales

71 Comparison Operators

72 Sorting Arrays

73 Turn Images Into NumPy Arrays

74 Assignment NumPy Practice

75 Optional Extra NumPy resources

**Matplotlib Plotting and Data Visualization**

76 Section Overview

77 Matplotlib Introduction

78 Importing And Using Matplotlib

79 Anatomy Of A Matplotlib Figure

80 Scatter Plot And Bar Plot

81 Histograms And Subplots

82 Subplots Option 2

83 Quick Tip Data Visualizations

84 Plotting From Pandas DataFrames

85 Quick Note Regular Expressions

86 Plotting From Pandas DataFrames 2

87 Plotting from Pandas DataFrames 3

88 Plotting from Pandas DataFrames 4

89 Plotting from Pandas DataFrames 5

90 Plotting from Pandas DataFrames 6

91 Plotting from Pandas DataFrames 7

92 Customizing Your Plots

93 Customizing Your Plots 2

94 Saving And Sharing Your Plots

95 Assignment Matplotlib Practice

**Scikit-learn Creating Machine Learning Models**

96 Section Overview

97 Scikit-learn Introduction

98 Quick Note Upcoming Video

99 Refresher What Is Machine Learning

100 Quick Note Upcoming Videos

101 Scikit-learn Cheatsheet

102 Typical scikit-learn Workflow

103 Optional Debugging Warnings In Jupyter

104 Getting Your Data Ready Splitting Your Data

105 Quick Tip Clean, Transform, Reduce

106 Getting Your Data Ready Convert Data To Numbers

107 Getting Your Data Ready Handling Missing Values With Pandas

108 Extension Feature Scaling

109 Note Correction in the upcoming video (splitting data)

110 Getting Your Data Ready Handling Missing Values With Scikit-learn

111 Choosing The Right Model For Your Data

112 Choosing The Right Model For Your Data 2 (Regression)

113 Quick Note Decision Trees

114 Quick Tip How ML Algorithms Work

115 Choosing The Right Model For Your Data 3 (Classification)

116 Fitting A Model To The Data

117 Making Predictions With Our Model

118 predict() vs predict proba()

119 Making Predictions With Our Model (Regression)

120 Evaluating A Machine Learning Model (Score)

121 Evaluating A Machine Learning Model 2 (Cross Validation)

122 Evaluating A Classification Model 1 (Accuracy)

123 Evaluating A Classification Model 2 (ROC Curve)

124 Evaluating A Classification Model 3 (ROC Curve)

125 Reading Extension ROC Curve + AUC

126 Evaluating A Classification Model 4 (Confusion Matrix)

127 Evaluating A Classification Model 5 (Confusion Matrix)

128 Evaluating A Classification Model 6 (Classification Report)

129 Evaluating A Regression Model 1 (R2 Score)

130 Evaluating A Regression Model 2 (MAE)

131 Evaluating A Regression Model 3 (MSE)

132 Machine Learning Model Evaluation

133 Evaluating A Model With Cross Validation and Scoring Parameter

134 Evaluating A Model With Scikit-learn Functions

135 Improving A Machine Learning Model

136 Tuning Hyperparameters

137 Tuning Hyperparameters 2

138 Tuning Hyperparameters 3

139 Note Metric Comparison Improvement

140 Quick Tip Correlation Analysis

141 Saving And Loading A Model

142 Saving And Loading A Model 2

143 Putting It All Together

144 Putting It All Together 2

145 Scikit-Learn Practice

**Supervised Learning Classification + Regression**

146 Milestone Projects!

**Milestone Project 1 Supervised Learning (Classification)**

147 Section Overview

148 Project Overview

149 Project Environment Setup

150 Optional Windows Project Environment Setup

151 Step 1~4 Framework Setup

152 Getting Our Tools Ready

153 Exploring Our Data

154 Finding Patterns

155 Finding Patterns 2

156 Finding Patterns 3

157 Preparing Our Data For Machine Learning

158 Choosing The Right Models

159 Experimenting With Machine Learning Models

160 TuningImproving Our Model

161 Tuning Hyperparameters

162 Tuning Hyperparameters 2

163 Tuning Hyperparameters 3

164 Quick Note Confusion Matrix Labels

165 Evaluating Our Model

166 Evaluating Our Model 2

167 Evaluating Our Model 3

168 Finding The Most Important Features

169 Reviewing The Project

**Milestone Project 2 Supervised Learning (Time Series Data)**

170 Section Overview

171 Project Overview

172 Project Environment Setup

173 Step 1~4 Framework Setup

174 Downloading the data for the next two projects

175 Exploring Our Data

176 Exploring Our Data 2

177 Feature Engineering

178 Turning Data Into Numbers

179 Filling Missing Numerical Values

180 Filling Missing Categorical Values

181 Fitting A Machine Learning Model

182 Splitting Data

183 Challenge What’s wrong with splitting data after filling it

184 Custom Evaluation Function

185 Reducing Data

186 RandomizedSearchCV

187 Improving Hyperparameters

188 Preproccessing Our Data

189 Making Predictions

190 Feature Importance

**Data Engineering**

191 Data Engineering Introduction

192 What Is Data

193 What Is A Data Engineer

194 What Is A Data Engineer 2

195 What Is A Data Engineer 3

196 What Is A Data Engineer 4

197 Types Of Databases

198 Quick Note Upcoming Video

199 Optional OLTP Databases

200 Optional Learn SQL

201 Hadoop, HDFS and MapReduce

202 Apache Spark and Apache Flink

203 Kafka and Stream Processing

**Neural Networks Deep Learning, Transfer Learning and TensorFlow 2**

204 Section Overview

205 Deep Learning and Unstructured Data

206 Setting Up With Google

207 Setting Up Google Colab

208 Google Colab Workspace

209 Uploading Project Data

210 Setting Up Our Data

211 Setting Up Our Data 2

212 Importing TensorFlow 2

213 Optional TensorFlow 2.0 Default Issue

214 Using A GPU

215 Optional GPU and Google Colab

216 Optional Reloading Colab Notebook

217 Loading Our Data Labels

218 Preparing The Images

219 Turning Data Labels Into Numbers

220 Creating Our Own Validation Set

221 Preprocess Images

222 Preprocess Images 2

223 Turning Data Into Batches

224 Turning Data Into Batches 2

225 Visualizing Our Data

226 Preparing Our Inputs and Outputs

227 Optional How machines learn and what’s going on behind the scenes

228 Building A Deep Learning Model

229 Building A Deep Learning Model 2

230 Building A Deep Learning Model 3

231 Building A Deep Learning Model 4

232 Summarizing Our Model

233 Evaluating Our Model

234 Preventing Overfitting

235 Training Your Deep Neural Network

236 Evaluating Performance With TensorBoard

237 Make And Transform Predictions

238 Transform Predictions To Text

239 Visualizing Model Predictions

240 Visualizing And Evaluate Model Predictions 2

241 Visualizing And Evaluate Model Predictions 3

242 Saving And Loading A Trained Model

243 Training Model On Full Dataset

244 Making Predictions On Test Images

245 Submitting Model to Kaggle

246 Making Predictions On Our Images

247 Finishing Dog Vision Where to next

**Storytelling + Communication How To Present Your Work**

248 Section Overview

249 Communicating Your Work

250 Communicating With Managers

251 Communicating With Co-Workers

252 Weekend Project Principle

253 Communicating With Outside World

254 Storytelling

255 Communicating and sharing your work Further reading

**Career Advice + Extra Bits**

256 Endorsements On LinkedIn

257 Quick Note Upcoming Video

258 What If I Don’t Have Enough Experience

259 Learning Guideline

260 Quick Note Upcoming Videos

261 JTS Learn to Learn

262 JTS Start With Why

263 Quick Note Upcoming Videos

264 CWD Git + Github

265 CWD Git + Github 2

266 Contributing To Open Source

267 Contributing To Open Source 2

268 Coding Challenges

269 Exercise Contribute To Open Source

**Learn Python**

270 What Is A Programming Language

271 Python Interpreter

272 How To Run Python Code

273 Our First Python Program

274 Latest Version Of Python

275 Python 2 vs Python 3

276 Exercise How Does Python Work

277 Learning Python

278 Python Data Types

279 How To Succeed

280 Numbers

281 Math Functions

282 DEVELOPER FUNDAMENTALS I

283 Operator Precedence

284 Exercise Operator Precedence

285 Optional bin() and complex

286 Variables

287 Expressions vs Statements

288 Augmented Assignment Operator

289 Strings

290 String Concatenation

291 Type Conversion

292 Escape Sequences

293 Formatted Strings

294 String Indexes

295 Immutability

296 Built-In Functions + Methods

297 Booleans

298 Exercise Type Conversion

299 DEVELOPER FUNDAMENTALS II

300 Exercise Password Checker

301 Lists

302 List Slicing

303 Matrix

304 List Methods

305 List Methods 2

306 List Methods 3

307 Common List Patterns

308 List Unpacking

309 None

310 Dictionaries

311 DEVELOPER FUNDAMENTALS III

312 Dictionary Keys

313 Dictionary Methods

314 Dictionary Methods 2

315 Tuples

316 Tuples 2

317 Sets

318 Sets 2

**Learn Python Part 2**

319 Breaking The Flow

320 Conditional Logic

321 Indentation In Python

322 Truthy vs Falsey

323 Ternary Operator

324 Short Circuiting

325 Logical Operators

326 Exercise Logical Operators

327 is vs ==

328 For Loops

329 Iterables

330 Exercise Tricky Counter

331 range()

332 enumerate()

333 While Loops

334 While Loops 2

335 break, continue, pass

336 Our First GUI

337 DEVELOPER FUNDAMENTALS IV

338 Exercise Find Duplicates

339 Functions

340 Parameters and Arguments

341 Default Parameters and Keyword Arguments

342 return

343 Exercise Tesla

344 Methods vs Functions

345 Docstrings

346 Clean Code

347 args and kwargs

348 Exercise Functions

349 Scope

350 Scope Rules

351 global Keyword

352 nonlocal Keyword

353 Why Do We Need Scope

354 Pure Functions

355 map()

356 filter()

357 zip()

358 reduce()

359 List Comprehensions

360 Set Comprehensions

361 Exercise Comprehensions

362 Python Exam Testing Your Understanding

363 Modules in Python

364 Quick Note Upcoming Videos

365 Optional PyCharm

366 Packages in Python

367 Different Ways To Import

368 Next Steps

369 Bonus Resource Python Cheatsheet

**Bonus Learn Advanced Statistics and Mathematics for FREE!**

370 Statistics and Mathematics

**Where To Go From Here**

371 Become An Alumni

372 Thank You

**BONUS SECTION**

373 Bonus Lecture

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