Complete Machine Learning and Data Science: Zero to Mastery

43h 22m 23s
English
Paid
November 22, 2023

This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000+ 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.

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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 This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore.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!

Taught By:

Andrei Neagoie is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. 

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. 

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. 

Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.  

See you inside the course!

Requirements:
  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don't.
  • All tools used in this course are free for you to use.
Who this course is for:
  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python
  • You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

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

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# Title Duration
1 Course Outline 06:00
2 Join Our Online Classroom! 04:02
3 Your First Day 03:49
4 What Is Machine Learning? 06:53
5 AI/Machine Learning/Data Science 04:52
6 ZTM Resources 04:24
7 Exercise: Machine Learning Playground 06:17
8 How Did We Get Here? 06:04
9 Exercise: YouTube Recommendation Engine 04:25
10 Types of Machine Learning 04:42
11 What Is Machine Learning? Round 2 04:46
12 Section Review 01:49
13 Section Overview 03:09
14 Introducing Our Framework 02:39
15 6 Step Machine Learning Framework 05:00
16 Types of Machine Learning Problems 10:33
17 Types of Data 04:52
18 Types of Evaluation 03:32
19 Features In Data 05:23
20 Modelling - Splitting Data 05:59
21 Modelling - Picking the Model 04:36
22 Modelling - Tuning 03:18
23 Modelling - Comparison 09:33
24 Experimentation 03:36
25 Tools We Will Use 04:01
26 The 2 Paths 03:28
27 Section Overview 01:10
28 Introducing Our Tools 03:29
29 What is Conda? 02:36
30 Conda Environments 04:31
31 Mac Environment Setup 17:27
32 Mac Environment Setup 2 14:12
33 Windows Environment Setup 05:18
34 Windows Environment Setup 2 23:18
35 Jupyter Notebook Walkthrough 10:21
36 Jupyter Notebook Walkthrough 2 16:19
37 Jupyter Notebook Walkthrough 3 08:11
38 Section Overview 02:28
39 Pandas Introduction 04:30
40 Series, Data Frames and CSVs 13:22
41 Describing Data with Pandas 09:49
42 Selecting and Viewing Data with Pandas 11:09
43 Selecting and Viewing Data with Pandas Part 2 13:08
44 Manipulating Data 13:57
45 Manipulating Data 2 09:58
46 Manipulating Data 3 10:13
47 How To Download The Course Assignments 07:44
48 Section Overview 02:41
49 NumPy Introduction 05:18
50 NumPy DataTypes and Attributes 14:06
51 Creating NumPy Arrays 09:23
52 NumPy Random Seed 07:18
53 Viewing Arrays and Matrices 09:36
54 Manipulating Arrays 11:33
55 Manipulating Arrays 2 09:45
56 Standard Deviation and Variance 07:11
57 Reshape and Transpose 07:27
58 Dot Product vs Element Wise 11:46
59 Exercise: Nut Butter Store Sales 13:05
60 Comparison Operators 03:34
61 Sorting Arrays 06:20
62 Turn Images Into NumPy Arrays 07:38
63 Exercise: Imposter Syndrome 02:57
64 Section Overview 01:51
65 Matplotlib Introduction 05:17
66 Importing And Using Matplotlib 11:37
67 Anatomy Of A Matplotlib Figure 09:20
68 Scatter Plot And Bar Plot 10:10
69 Histograms And Subplots 08:41
70 Subplots Option 2 04:16
71 Quick Tip: Data Visualizations 01:49
72 Plotting From Pandas DataFrames 05:59
73 Plotting From Pandas DataFrames 2 10:34
74 Plotting from Pandas DataFrames 3 08:33
75 Plotting from Pandas DataFrames 4 06:37
76 Plotting from Pandas DataFrames 5 08:30
77 Plotting from Pandas DataFrames 6 08:29
78 Plotting from Pandas DataFrames 7 11:21
79 Customizing Your Plots 10:10
80 Customizing Your Plots 2 09:42
81 Saving And Sharing Your Plots 04:15
82 Section Overview 02:30
83 Scikit-learn Introduction 06:42
84 Refresher: What Is Machine Learning? 05:41
85 Scikit-learn Cheatsheet 06:14
86 Typical scikit-learn Workflow 23:15
87 Optional: Debugging Warnings In Jupyter 18:58
88 Getting Your Data Ready: Splitting Your Data 08:38
89 Quick Tip: Clean, Transform, Reduce 05:04
90 Getting Your Data Ready: Convert Data To Numbers 16:55
91 Getting Your Data Ready: Handling Missing Values With Pandas 12:23
92 Getting Your Data Ready: Handling Missing Values With Scikit-learn 17:30
93 NEW: Choosing The Right Model For Your Data 20:15
94 NEW: Choosing The Right Model For Your Data 2 (Regression) 11:22
95 Quick Tip: How ML Algorithms Work 01:26
96 Choosing The Right Model For Your Data 3 (Classification) 12:46
97 Fitting A Model To The Data 06:46
98 Making Predictions With Our Model 08:25
99 predict() vs predict_proba() 08:34
100 NEW: Making Predictions With Our Model (Regression) 08:49
101 NEW: Evaluating A Machine Learning Model (Score) Part 1 09:42
102 NEW: Evaluating A Machine Learning Model (Score) Part 2 06:48
103 Evaluating A Machine Learning Model 2 (Cross Validation) 13:17
104 Evaluating A Classification Model 1 (Accuracy) 04:47
105 Evaluating A Classification Model 2 (ROC Curve) 09:05
106 Evaluating A Classification Model 3 (ROC Curve) 07:45
107 Evaluating A Classification Model 4 (Confusion Matrix) 11:02
108 NEW: Evaluating A Classification Model 5 (Confusion Matrix) 14:23
109 Evaluating A Classification Model 6 (Classification Report) 10:17
110 NEW: Evaluating A Regression Model 1 (R2 Score) 10:00
111 NEW: Evaluating A Regression Model 2 (MAE) 07:23
112 NEW: Evaluating A Regression Model 3 (MSE) 09:50
113 NEW: Evaluating A Model With Cross Validation and Scoring Parameter 25:20
114 NEW: Evaluating A Model With Scikit-learn Functions 14:03
115 Improving A Machine Learning Model 11:17
116 Tuning Hyperparameters 23:16
117 Tuning Hyperparameters 2 14:24
118 Tuning Hyperparameters 3 15:00
119 Quick Tip: Correlation Analysis 02:29
120 Saving And Loading A Model 07:30
121 Saving And Loading A Model 2 06:21
122 Putting It All Together 20:20
123 Putting It All Together 2 11:35
124 Section Overview 02:10
125 Project Overview 06:10
126 Project Environment Setup 11:00
127 Optional: Windows Project Environment Setup 04:53
128 Step 1~4 Framework Setup 12:07
129 Getting Our Tools Ready 09:05
130 Exploring Our Data 08:34
131 Finding Patterns 10:03
132 Finding Patterns 2 16:48
133 Finding Patterns 3 13:38
134 Preparing Our Data For Machine Learning 08:52
135 Choosing The Right Models 10:16
136 Experimenting With Machine Learning Models 06:32
137 Tuning/Improving Our Model 13:50
138 Tuning Hyperparameters 11:28
139 Tuning Hyperparameters 2 11:50
140 Tuning Hyperparameters 3 07:07
141 Evaluating Our Model 11:00
142 Evaluating Our Model 2 05:56
143 Evaluating Our Model 3 08:50
144 Finding The Most Important Features 16:08
145 Reviewing The Project 09:14
146 Section Overview 01:08
147 Project Overview 04:25
148 Project Environment Setup 10:53
149 Step 1~4 Framework Setup 08:37
150 Exploring Our Data 14:17
151 Exploring Our Data 2 06:17
152 Feature Engineering 15:25
153 Turning Data Into Numbers 15:39
154 Filling Missing Numerical Values 12:50
155 Filling Missing Categorical Values 08:28
156 Fitting A Machine Learning Model 07:17
157 Splitting Data 10:01
158 Custom Evaluation Function 11:14
159 Reducing Data 10:37
160 RandomizedSearchCV 09:33
161 Improving Hyperparameters 08:12
162 Preproccessing Our Data 13:16
163 Making Predictions 09:18
164 Feature Importance 13:51
165 Data Engineering Introduction 03:25
166 What Is Data? 06:43
167 What Is A Data Engineer? 04:21
168 What Is A Data Engineer 2? 05:37
169 What Is A Data Engineer 3? 05:04
170 What Is A Data Engineer 4? 03:23
171 Types Of Databases 06:51
172 Optional: OLTP Databases 10:55
173 Hadoop, HDFS and MapReduce 04:23
174 Apache Spark and Apache Flink 02:08
175 Kafka and Stream Processing 04:34
176 Section Overview 02:07
177 Deep Learning and Unstructured Data 13:37
178 Setting Up Google Colab 07:18
179 Google Colab Workspace 04:24
180 Uploading Project Data 06:53
181 Setting Up Our Data 04:41
182 Setting Up Our Data 2 01:33
183 Importing TensorFlow 2 12:44
184 Optional: TensorFlow 2.0 Default Issue 03:40
185 Using A GPU 09:00
186 Optional: GPU and Google Colab 04:28
187 Optional: Reloading Colab Notebook 06:50
188 Loading Our Data Labels 12:05
189 Preparing The Images 12:33
190 Turning Data Labels Into Numbers 12:12
191 Creating Our Own Validation Set 09:19
192 Preprocess Images 10:26
193 Preprocess Images 2 11:01
194 Turning Data Into Batches 09:38
195 Turning Data Into Batches 2 17:55
196 Visualizing Our Data 12:42
197 Preparing Our Inputs and Outputs 06:39
198 Building A Deep Learning Model 11:43
199 Building A Deep Learning Model 2 10:54
200 Building A Deep Learning Model 3 09:06
201 Building A Deep Learning Model 4 09:13
202 Summarizing Our Model 04:53
203 Evaluating Our Model 09:27
204 Preventing Overfitting 04:21
205 Training Your Deep Neural Network 19:10
206 Evaluating Performance With TensorBoard 07:31
207 Make And Transform Predictions 15:05
208 Transform Predictions To Text 15:21
209 Visualizing Model Predictions 14:47
210 Visualizing And Evaluate Model Predictions 2 15:53
211 Visualizing And Evaluate Model Predictions 3 10:40
212 Saving And Loading A Trained Model 13:35
213 Training Model On Full Dataset 15:03
214 Making Predictions On Test Images 16:55
215 Submitting Model to Kaggle 14:15
216 Making Predictions On Our Images 15:16
217 Section Overview 02:20
218 Communicating Your Work 03:23
219 Communicating With Managers 02:59
220 Communicating With Co-Workers 03:43
221 Weekend Project Principle 06:33
222 Communicating With Outside World 03:30
223 Storytelling 03:07
224 What If I Don't Have Enough Experience? 15:04
225 JTS: Learn to Learn 02:00
226 JTS: Start With Why 02:44
227 CWD: Git + Github 17:41
228 CWD: Git + Github 2 16:53
229 Contributing To Open Source 14:09
230 Contributing To Open Source 2 09:41
231 What Is A Programming Language 06:25
232 Python Interpreter 07:05
233 How To Run Python Code 04:54
234 Latest Version Of Python 01:29
235 Our First Python Program 07:44
236 Python 2 vs Python 3 06:41
237 Exercise: How Does Python Work? 02:10
238 Learning Python 02:06
239 Python Data Types 04:47
240 Numbers 11:10
241 Math Functions 04:30
242 DEVELOPER FUNDAMENTALS: I 04:08
243 Operator Precedence 03:11
244 Optional: bin() and complex 04:03
245 Variables 13:13
246 Expressions vs Statements 01:37
247 Augmented Assignment Operator 02:50
248 Strings 05:30
249 String Concatenation 01:17
250 Type Conversion 03:04
251 Escape Sequences 04:24
252 Formatted Strings 08:25
253 String Indexes 08:58
254 Immutability 03:14
255 Built-In Functions + Methods 10:04
256 Booleans 03:22
257 Exercise: Type Conversion 08:23
258 DEVELOPER FUNDAMENTALS: II 04:43
259 Exercise: Password Checker 07:22
260 Lists 05:02
261 List Slicing 07:49
262 Matrix 04:12
263 List Methods 10:29
264 List Methods 2 04:25
265 List Methods 3 04:53
266 Common List Patterns 05:58
267 List Unpacking 02:42
268 None 01:52
269 Dictionaries 06:22
270 DEVELOPER FUNDAMENTALS: III 02:41
271 Dictionary Keys 03:38
272 Dictionary Methods 04:38
273 Dictionary Methods 2 07:05
274 Tuples 04:47
275 Tuples 2 03:15
276 Sets 07:25
277 Sets 2 08:46
278 Breaking The Flow 02:36
279 Conditional Logic 13:19
280 Indentation In Python 04:39
281 Truthy vs Falsey 05:19
282 Ternary Operator 04:15
283 Short Circuiting 04:03
284 Logical Operators 06:57
285 Exercise: Logical Operators 07:48
286 is vs == 07:37
287 For Loops 07:02
288 Iterables 06:44
289 Exercise: Tricky Counter 03:24
290 range() 05:39
291 enumerate() 04:38
292 While Loops 06:29
293 While Loops 2 05:50
294 break, continue, pass 04:16
295 Our First GUI 08:49
296 DEVELOPER FUNDAMENTALS: IV 06:35
297 Exercise: Find Duplicates 03:55
298 Functions 07:42
299 Parameters and Arguments 04:26
300 Default Parameters and Keyword Arguments 05:41
301 return 13:12
302 Methods vs Functions 04:34
303 Docstrings 03:48
304 Clean Code 04:39
305 *args and **kwargs 07:57
306 Exercise: Functions 04:19
307 Scope 03:39
308 Scope Rules 06:56
309 global Keyword 06:14
310 nonlocal Keyword 03:22
311 Why Do We Need Scope? 03:39
312 Pure Functions 09:24
313 map() 06:31
314 filter() 04:24
315 zip() 03:29
316 reduce() 07:32
317 List Comprehensions 08:38
318 Set Comprehensions 06:27
319 Exercise: Comprehensions 04:37
320 Modules in Python 10:55
321 Optional: PyCharm 08:20
322 Packages in Python 10:46
323 Different Ways To Import 07:04
324 Thank You 02:45

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