Complete A.I. & Machine Learning, Data Science Bootcamp
Last updated 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 30.37 GB | Duration: 43h 55m

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

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 Meta 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

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.

Description

Become a complete A.I., Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ 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, Meta, + 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 ColabBy 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!Click "Enroll Now" and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!Taught By:Daniel Bourke:A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.Questions are always welcome.Andrei Neagoie:Andrei 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!

Overview

Section 1: Introduction

Lecture 1 Course Outline

Lecture 2 Join Our Online Classroom!

Lecture 3 Exercise: Meet Your Classmates & Instructor

Lecture 4 Asking Questions + Getting Help

Lecture 5 Your First Day

Section 2: Machine Learning 101

Lecture 6 What Is Machine Learning?

Lecture 7 AI/Machine Learning/Data Science

Lecture 8 ZTM Resources

Lecture 9 Exercise: Machine Learning Playground

Lecture 10 How Did We Get Here?

Lecture 11 Exercise: YouTube Recommendation Engine

Lecture 12 Types of Machine Learning

Lecture 13 Are You Getting It Yet?

Lecture 14 What Is Machine Learning? Round 2

Lecture 15 Section Review

Lecture 16 Monthly Coding Challenges, Free Resources and Guides

Section 3: Machine Learning and Data Science Framework

Lecture 17 Section Overview

Lecture 18 Introducing Our Framework

Lecture 19 6 Step Machine Learning Framework

Lecture 20 Types of Machine Learning Problems

Lecture 21 Types of Data

Lecture 22 Types of Evaluation

Lecture 23 Features In Data

Lecture 24 Modelling - Splitting Data

Lecture 25 Modelling - Picking the Model

Lecture 26 Modelling - Tuning

Lecture 27 Modelling - Comparison

Lecture 28 Overfitting and Underfitting Definitions

Lecture 29 Experimentation

Lecture 30 Tools We Will Use

Lecture 31 Optional: Elements of AI

Section 4: The 2 Paths

Lecture 32 The 2 Paths

Lecture 33 Python + Machine Learning Monthly

Lecture 34 Endorsements On LinkedIN

Section 5: Data Science Environment Setup

Lecture 35 Section Overview

Lecture 36 Introducing Our Tools

Lecture 37 What is Conda?

Lecture 38 Conda Environments

Lecture 39 Mac Environment Setup

Lecture 40 Mac Environment Setup 2

Lecture 41 Windows Environment Setup

Lecture 42 Windows Environment Setup 2

Lecture 43 Linux Environment Setup

Lecture 44 Sharing your Conda Environment

Lecture 45 Jupyter Notebook Walkthrough

Lecture 46 Jupyter Notebook Walkthrough 2

Lecture 47 Jupyter Notebook Walkthrough 3

Section 6: Pandas: Data Analysis

Lecture 48 Section Overview

Lecture 49 Downloading Workbooks and Assignments

Lecture 50 Pandas Introduction

Lecture 51 Series, Data Frames and CSVs

Lecture 52 Data from URLs

Lecture 53 Quick Note: Upcoming Videos

Lecture 54 Describing Data with Pandas

Lecture 55 Selecting and Viewing Data with Pandas

Lecture 56 Quick Note: Upcoming Videos

Lecture 57 Selecting and Viewing Data with Pandas Part 2

Lecture 58 Manipulating Data

Lecture 59 Manipulating Data 2

Lecture 60 Manipulating Data 3

Lecture 61 Assignment: Pandas Practice

Lecture 62 How To Download The Course Assignments

Section 7: NumPy

Lecture 63 Section Overview

Lecture 64 NumPy Introduction

Lecture 65 Quick Note: Correction In Next Video

Lecture 66 NumPy DataTypes and Attributes

Lecture 67 Creating NumPy Arrays

Lecture 68 NumPy Random Seed

Lecture 69 Viewing Arrays and Matrices

Lecture 70 Manipulating Arrays

Lecture 71 Manipulating Arrays 2

Lecture 72 Standard Deviation and Variance

Lecture 73 Reshape and Transpose

Lecture 74 Dot Product vs Element Wise

Lecture 75 Exercise: Nut Butter Store Sales

Lecture 76 Comparison Operators

Lecture 77 Sorting Arrays

Lecture 78 Turn Images Into NumPy Arrays

Lecture 79 Exercise: Imposter Syndrome

Lecture 80 Assignment: NumPy Practice

Lecture 81 Optional: Extra NumPy resources

Section 8: Matplotlib: Plotting and Data Visualization

Lecture 82 Section Overview

Lecture 83 Matplotlib Introduction

Lecture 84 Importing And Using Matplotlib

Lecture 85 Anatomy Of A Matplotlib Figure

Lecture 86 Scatter Plot And Bar Plot

Lecture 87 Histograms And Subplots

Lecture 88 Subplots Option 2

Lecture 89 Quick Tip: Data Visualizations

Lecture 90 Plotting From Pandas DataFrames

Lecture 91 Quick Note: Regular Expressions

Lecture 92 Plotting From Pandas DataFrames 2

Lecture 93 Plotting from Pandas DataFrames 3

Lecture 94 Plotting from Pandas DataFrames 4

Lecture 95 Plotting from Pandas DataFrames 5

Lecture 96 Plotting from Pandas DataFrames 6

Lecture 97 Plotting from Pandas DataFrames 7

Lecture 98 Customizing Your Plots

Lecture 99 Customizing Your Plots 2

Lecture 100 Saving And Sharing Your Plots

Lecture 101 Assignment: Matplotlib Practice

Section 9: Scikit-learn: Creating Machine Learning Models

Lecture 102 Section Overview

Lecture 103 Scikit-learn Introduction

Lecture 104 Quick Note: Upcoming Video

Lecture 105 Refresher: What Is Machine Learning?

Lecture 106 Quick Note: Upcoming Videos

Lecture 107 Scikit-learn Cheatsheet

Lecture 108 Typical scikit-learn Workflow

Lecture 109 Optional: Debugging Warnings In Jupyter

Lecture 110 Getting Your Data Ready: Splitting Your Data

Lecture 111 Quick Tip: Clean, Transform, Reduce

Lecture 112 Getting Your Data Ready: Convert Data To Numbers

Lecture 113 Note: Update to next video (OneHotEncoder can handle NaN/None values)

Lecture 114 Getting Your Data Ready: Handling Missing Values With Pandas

Lecture 115 Extension: Feature Scaling

Lecture 116 Note: Correction in the upcoming video (splitting data)

Lecture 117 Getting Your Data Ready: Handling Missing Values With Scikit-learn

Lecture 118 NEW: Choosing The Right Model For Your Data

Lecture 119 NEW: Choosing The Right Model For Your Data 2 (Regression)

Lecture 120 Quick Note: Decision Trees

Lecture 121 Quick Tip: How ML Algorithms Work

Lecture 122 Choosing The Right Model For Your Data 3 (Classification)

Lecture 123 Fitting A Model To The Data

Lecture 124 Making Predictions With Our Model

Lecture 125 predict() vs predict_proba()

Lecture 126 NEW: Making Predictions With Our Model (Regression)

Lecture 127 NEW: Evaluating A Machine Learning Model (Score) Part 1

Lecture 128 NEW: Evaluating A Machine Learning Model (Score) Part 2

Lecture 129 Evaluating A Machine Learning Model 2 (Cross Validation)

Lecture 130 Evaluating A Classification Model 1 (Accuracy)

Lecture 131 Evaluating A Classification Model 2 (ROC Curve)

Lecture 132 Evaluating A Classification Model 3 (ROC Curve)

Lecture 133 Reading Extension: ROC Curve + AUC

Lecture 134 Evaluating A Classification Model 4 (Confusion Matrix)

Lecture 135 NEW: Evaluating A Classification Model 5 (Confusion Matrix)

Lecture 136 Evaluating A Classification Model 6 (Classification Report)

Lecture 137 NEW: Evaluating A Regression Model 1 (R2 Score)

Lecture 138 NEW: Evaluating A Regression Model 2 (MAE)

Lecture 139 NEW: Evaluating A Regression Model 3 (MSE)

Lecture 140 Machine Learning Model Evaluation

Lecture 141 NEW: Evaluating A Model With Cross Validation and Scoring Parameter

Lecture 142 NEW: Evaluating A Model With Scikit-learn Functions

Lecture 143 Improving A Machine Learning Model

Lecture 144 Tuning Hyperparameters

Lecture 145 Tuning Hyperparameters 2

Lecture 146 Tuning Hyperparameters 3

Lecture 147 Note: Metric Comparison Improvement

Lecture 148 Quick Tip: Correlation Analysis

Lecture 149 Saving And Loading A Model

Lecture 150 Saving And Loading A Model 2

Lecture 151 Putting It All Together

Lecture 152 Putting It All Together 2

Lecture 153 Scikit-Learn Practice

Section 10: Supervised Learning: Classification + Regression

Lecture 154 Milestone Projects!

Section 11: Milestone Project 1: Supervised Learning (Classification)

Lecture 155 Section Overview

Lecture 156 Project Overview

Lecture 157 Project Environment Setup

Lecture 158 Optional: Windows Project Environment Setup

Lecture 159 Step 1~4 Framework Setup

Lecture 160 Note: Code update for next video

Lecture 161 Getting Our Tools Ready

Lecture 162 Exploring Our Data

Lecture 163 Finding Patterns

Lecture 164 Finding Patterns 2

Lecture 165 Finding Patterns 3

Lecture 166 Preparing Our Data For Machine Learning

Lecture 167 Choosing The Right Models

Lecture 168 Experimenting With Machine Learning Models

Lecture 169 Tuning/Improving Our Model

Lecture 170 Tuning Hyperparameters

Lecture 171 Tuning Hyperparameters 2

Lecture 172 Tuning Hyperparameters 3

Lecture 173 Quick Note: Confusion Matrix Labels

Lecture 174 Evaluating Our Model

Lecture 175 Note: Code change in upcoming video

Lecture 176 Evaluating Our Model 2

Lecture 177 Evaluating Our Model 3

Lecture 178 Finding The Most Important Features

Lecture 179 Reviewing The Project

Section 12: Milestone Project 2: Supervised Learning (Time Series Data)

Lecture 180 Section Overview

Lecture 181 Project Overview

Lecture 182 Downloading the data for the next two projects

Lecture 183 Project Environment Setup

Lecture 184 Step 1~4 Framework Setup

Lecture 185 Exploring Our Data

Lecture 186 Exploring Our Data 2

Lecture 187 Feature Engineering

Lecture 188 Turning Data Into Numbers

Lecture 189 Filling Missing Numerical Values

Lecture 190 Filling Missing Categorical Values

Lecture 191 Fitting A Machine Learning Model

Lecture 192 Splitting Data

Lecture 193 Challenge: What's wrong with splitting data after filling it?

Lecture 194 Custom Evaluation Function

Lecture 195 Reducing Data

Lecture 196 RandomizedSearchCV

Lecture 197 Improving Hyperparameters

Lecture 198 Preproccessing Our Data

Lecture 199 Making Predictions

Lecture 200 Feature Importance

Section 13: Data Engineering

Lecture 201 Data Engineering Introduction

Lecture 202 What Is Data?

Lecture 203 What Is A Data Engineer?

Lecture 204 What Is A Data Engineer 2?

Lecture 205 What Is A Data Engineer 3?

Lecture 206 What Is A Data Engineer 4?

Lecture 207 Types Of Databases

Lecture 208 Quick Note: Upcoming Video

Lecture 209 Optional: OLTP Databases

Lecture 210 Optional: Learn SQL

Lecture 211 Hadoop, HDFS and MapReduce

Lecture 212 Apache Spark and Apache Flink

Lecture 213 Kafka and Stream Processing

Section 14: Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

Lecture 214 Section Overview

Lecture 215 Deep Learning and Unstructured Data

Lecture 216 Setting Up With Google

Lecture 217 Setting Up Google Colab

Lecture 218 Google Colab Workspace

Lecture 219 Uploading Project Data

Lecture 220 Setting Up Our Data

Lecture 221 Setting Up Our Data 2

Lecture 222 Importing TensorFlow 2

Lecture 223 Optional: TensorFlow 2.0 Default Issue

Lecture 224 Using A GPU

Lecture 225 Optional: GPU and Google Colab

Lecture 226 Optional: Reloading Colab Notebook

Lecture 227 Loading Our Data Labels

Lecture 228 Preparing The Images

Lecture 229 Turning Data Labels Into Numbers

Lecture 230 Creating Our Own Validation Set

Lecture 231 Preprocess Images

Lecture 232 Preprocess Images 2

Lecture 233 Turning Data Into Batches

Lecture 234 Turning Data Into Batches 2

Lecture 235 Visualizing Our Data

Lecture 236 Preparing Our Inputs and Outputs

Lecture 237 Optional: How machines learn and what's going on behind the scenes?

Lecture 238 Building A Deep Learning Model

Lecture 239 Building A Deep Learning Model 2

Lecture 240 Building A Deep Learning Model 3

Lecture 241 Building A Deep Learning Model 4

Lecture 242 Summarizing Our Model

Lecture 243 Evaluating Our Model

Lecture 244 Preventing Overfitting

Lecture 245 Training Your Deep Neural Network

Lecture 246 Evaluating Performance With TensorBoard

Lecture 247 Make And Transform Predictions

Lecture 248 Transform Predictions To Text

Lecture 249 Visualizing Model Predictions

Lecture 250 Visualizing And Evaluate Model Predictions 2

Lecture 251 Visualizing And Evaluate Model Predictions 3

Lecture 252 Saving And Loading A Trained Model

Lecture 253 Training Model On Full Dataset

Lecture 254 Making Predictions On Test Images

Lecture 255 Submitting Model to Kaggle

Lecture 256 Making Predictions On Our Images

Lecture 257 Finishing Dog Vision: Where to next?

Section 15: Storytelling + Communication: How To Present Your Work

Lecture 258 Section Overview

Lecture 259 Communicating Your Work

Lecture 260 Communicating With Managers

Lecture 261 Communicating With Co-Workers

Lecture 262 Weekend Project Principle

Lecture 263 Communicating With Outside World

Lecture 264 Storytelling

Lecture 265 Communicating and sharing your work: Further reading

Section 16: Career Advice + Extra Bits

Lecture 266 Endorsements On LinkedIn

Lecture 267 Quick Note: Upcoming Video

Lecture 268 What If I Don't Have Enough Experience?

Lecture 269 Learning Guideline

Lecture 270 Quick Note: Upcoming Videos

Lecture 271 JTS: Learn to Learn

Lecture 272 JTS: Start With Why

Lecture 273 Quick Note: Upcoming Videos

Lecture 274 CWD: Git + Github

Lecture 275 CWD: Git + Github 2

Lecture 276 Contributing To Open Source

Lecture 277 Contributing To Open Source 2

Lecture 278 Exercise: Contribute To Open Source

Lecture 279 Coding Challenges

Section 17: Learn Python

Lecture 280 What Is A Programming Language

Lecture 281 Python Interpreter

Lecture 282 How To Run Python Code

Lecture 283 Latest Version Of Python

Lecture 284 Our First Python Program

Lecture 285 Python 2 vs Python 3

Lecture 286 Exercise: How Does Python Work?

Lecture 287 Learning Python

Lecture 288 Python Data Types

Lecture 289 How To Succeed

Lecture 290 Numbers

Lecture 291 Math Functions

Lecture 292 DEVELOPER FUNDAMENTALS: I

Lecture 293 Operator Precedence

Lecture 294 Exercise: Operator Precedence

Lecture 295 Optional: bin() and complex

Lecture 296 Variables

Lecture 297 Expressions vs Statements

Lecture 298 Augmented Assignment Operator

Lecture 299 Strings

Lecture 300 String Concatenation

Lecture 301 Type Conversion

Lecture 302 Escape Sequences

Lecture 303 Formatted Strings

Lecture 304 String Indexes

Lecture 305 Immutability

Lecture 306 Built-In Functions + Methods

Lecture 307 Booleans

Lecture 308 Exercise: Type Conversion

Lecture 309 DEVELOPER FUNDAMENTALS: II

Lecture 310 Exercise: Password Checker

Lecture 311 Lists

Lecture 312 List Slicing

Lecture 313 Matrix

Lecture 314 List Methods

Lecture 315 List Methods 2

Lecture 316 List Methods 3

Lecture 317 Common List Patterns

Lecture 318 List Unpacking

Lecture 319 None

Lecture 320 Dictionaries

Lecture 321 DEVELOPER FUNDAMENTALS: III

Lecture 322 Dictionary Keys

Lecture 323 Dictionary Methods

Lecture 324 Dictionary Methods 2

Lecture 325 Tuples

Lecture 326 Tuples 2

Lecture 327 Sets

Lecture 328 Sets 2

Section 18: Learn Python Part 2

Lecture 329 Breaking The Flow

Lecture 330 Conditional Logic

Lecture 331 Indentation In Python

Lecture 332 Truthy vs Falsey

Lecture 333 Ternary Operator

Lecture 334 Short Circuiting

Lecture 335 Logical Operators

Lecture 336 Exercise: Logical Operators

Lecture 337 is vs ==

Lecture 338 For Loops

Lecture 339 Iterables

Lecture 340 Exercise: Tricky Counter

Lecture 341 range()

Lecture 342 enumerate()

Lecture 343 While Loops

Lecture 344 While Loops 2

Lecture 345 break, continue, pass

Lecture 346 Our First GUI

Lecture 347 DEVELOPER FUNDAMENTALS: IV

Lecture 348 Exercise: Find Duplicates

Lecture 349 Functions

Lecture 350 Parameters and Arguments

Lecture 351 Default Parameters and Keyword Arguments

Lecture 352 return

Lecture 353 Exercise: Tesla

Lecture 354 Methods vs Functions

Lecture 355 Docstrings

Lecture 356 Clean Code

Lecture 357 *args and **kwargs

Lecture 358 Exercise: Functions

Lecture 359 Scope

Lecture 360 Scope Rules

Lecture 361 global Keyword

Lecture 362 nonlocal Keyword

Lecture 363 Why Do We Need Scope?

Lecture 364 Pure Functions

Lecture 365 map()

Lecture 366 filter()

Lecture 367 zip()

Lecture 368 reduce()

Lecture 369 List Comprehensions

Lecture 370 Set Comprehensions

Lecture 371 Exercise: Comprehensions

Lecture 372 Python Exam: Testing Your Understanding

Lecture 373 Modules in Python

Lecture 374 Quick Note: Upcoming Videos

Lecture 375 Optional: PyCharm

Lecture 376 Packages in Python

Lecture 377 Different Ways To Import

Lecture 378 Next Steps

Lecture 379 Bonus Resource: Python Cheatsheet

Section 19: Extra: Learn Advanced Statistics and Mathematics for FREE!

Lecture 380 Statistics and Mathematics

Section 20: Where To Go From Here?

Lecture 381 Become An Alumni

Lecture 382 Thank You

Lecture 383 Thank You Part 2

Section 21: BONUS SECTION

Lecture 384 Special Bonus Lecture

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.



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