The Data Science Course 2023: Complete Data Science Bootcamp

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn

The course provides the entire toolbox you need to become a data scientist

Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow

Impress interviewers by showing an understanding of the data science field

Learn how to pre-process data

Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

Start coding in Python and learn how to use it for statistical analysis

Perform linear and logistic regressions in Python

Carry out cluster and factor analysis

Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

Apply your skills to real-life business cases

Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data

Unfold the power of deep neural networks

Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance

Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Requirements

No prior experience is required. We will start from the very basics

You’ll need to install Anaconda. We will show you how to do that step by step

Microsoft Excel 2003, 2010, 2013, 2016, or 365

Description

The ProblemData scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.     However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.    And how can you do that?   Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)   Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture   The Solution   Data science is a multidisciplinary field. It encompasses a wide range of topics.    Understanding of the data science field and the type of analysis carried out   Mathematics   Statistics   Python   Applying advanced statistical techniques in Python   Data Visualization   Machine Learning   Deep Learning   Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.   So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2023.   We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.   Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).   The Skills   1. Intro to Data and Data ScienceBig data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?     Why learn it?
As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
     2. Mathematics Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.   We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.   Why learn it?   Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.   3. Statistics You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.   Why learn it?   This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.   4. PythonPython is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.Why learn it?   When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.      5. TableauData scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.Why learn it?   A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.      6. Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.   Why learn it?   Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.      7. Machine Learning The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.   Why learn it?   Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.   ***What you get***A $1250 data science training program   Active Q&A support   All the knowledge to get hired as a data scientist   A community of data science learners   A certificate of completion   Access to future updates   Solve real-life business cases that will get you the job    You will become a data scientist from scratch

  We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.Why wait? Every day is a missed opportunity.Click the “Buy Now” button and become a part of our data scientist program today.

   


Overview

Section 1: Part 1: Introduction

Lecture 1 A Practical Example: What You Will Learn in This Course

Lecture 2 What Does the Course Cover

Lecture 3 Download All Resources and Important FAQ

Section 2: The Field of Data Science - The Various Data Science Disciplines

Lecture 4 Data Science and Business Buzzwords: Why are there so Many?

Lecture 5 What is the difference between Analysis and Analytics

Lecture 6 Business Analytics, Data Analytics, and Data Science: An Introduction

Lecture 7 Continuing with BI, ML, and AI

Lecture 8 A Breakdown of our Data Science Infographic

Section 3: The Field of Data Science - Connecting the Data Science Disciplines

Lecture 9 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

Section 4: The Field of Data Science - The Benefits of Each Discipline

Lecture 10 The Reason Behind These Disciplines

Section 5: The Field of Data Science - Popular Data Science Techniques

Lecture 11 Techniques for Working with Traditional Data

Lecture 12 Real Life Examples of Traditional Data

Lecture 13 Techniques for Working with Big Data

Lecture 14 Real Life Examples of Big Data

Lecture 15 Business Intelligence (BI) Techniques

Lecture 16 Real Life Examples of Business Intelligence (BI)

Lecture 17 Techniques for Working with Traditional Methods

Lecture 18 Real Life Examples of Traditional Methods

Lecture 19 Machine Learning (ML) Techniques

Lecture 20 Types of Machine Learning

Lecture 21 Real Life Examples of Machine Learning (ML)

Section 6: The Field of Data Science - Popular Data Science Tools

Lecture 22 Necessary Programming Languages and Software Used in Data Science

Section 7: The Field of Data Science - Careers in Data Science

Lecture 23 Finding the Job - What to Expect and What to Look for

Section 8: The Field of Data Science - Debunking Common Misconceptions

Lecture 24 Debunking Common Misconceptions

Section 9: Part 2: Probability

Lecture 25 The Basic Probability Formula

Lecture 26 Computing Expected Values

Lecture 27 Frequency

Lecture 28 Events and Their Complements

Section 10: Probability - Combinatorics

Lecture 29 Fundamentals of Combinatorics

Lecture 30 Permutations and How to Use Them

Lecture 31 Simple Operations with Factorials

Lecture 32 Solving Variations with Repetition

Lecture 33 Solving Variations without Repetition

Lecture 34 Solving Combinations

Lecture 35 Symmetry of Combinations

Lecture 36 Solving Combinations with Separate Sample Spaces

Lecture 37 Combinatorics in Real-Life: The Lottery

Lecture 38 A Recap of Combinatorics

Lecture 39 A Practical Example of Combinatorics

Section 11: Probability - Bayesian Inference

Lecture 40 Sets and Events

Lecture 41 Ways Sets Can Interact

Lecture 42 Intersection of Sets

Lecture 43 Union of Sets

Lecture 44 Mutually Exclusive Sets

Lecture 45 Dependence and Independence of Sets

Lecture 46 The Conditional Probability Formula

Lecture 47 The Law of Total Probability

Lecture 48 The Additive Rule

Lecture 49 The Multiplication Law

Lecture 50 Bayes' Law

Lecture 51 A Practical Example of Bayesian Inference

Section 12: Probability - Distributions

Lecture 52 Fundamentals of Probability Distributions

Lecture 53 Types of Probability Distributions

Lecture 54 Characteristics of Discrete Distributions

Lecture 55 Discrete Distributions: The Uniform Distribution

Lecture 56 Discrete Distributions: The Bernoulli Distribution

Lecture 57 Discrete Distributions: The Binomial Distribution

Lecture 58 Discrete Distributions: The Poisson Distribution

Lecture 59 Characteristics of Continuous Distributions

Lecture 60 Continuous Distributions: The Normal Distribution

Lecture 61 Continuous Distributions: The Standard Normal Distribution

Lecture 62 Continuous Distributions: The Students' T Distribution

Lecture 63 Continuous Distributions: The Chi-Squared Distribution

Lecture 64 Continuous Distributions: The Exponential Distribution

Lecture 65 Continuous Distributions: The Logistic Distribution

Lecture 66 A Practical Example of Probability Distributions

Section 13: Probability - Probability in Other Fields

Lecture 67 Probability in Finance

Lecture 68 Probability in Statistics

Lecture 69 Probability in Data Science

Section 14: Part 3: Statistics

Lecture 70 Population and Sample

Section 15: Statistics - Descriptive Statistics

Lecture 71 Types of Data

Lecture 72 Levels of Measurement

Lecture 73 Categorical Variables - Visualization Techniques

Lecture 74 Categorical Variables Exercise

Lecture 75 Numerical Variables - Frequency Distribution Table

Lecture 76 Numerical Variables Exercise

Lecture 77 The Histogram

Lecture 78 Histogram Exercise

Lecture 79 Cross Tables and Scatter Plots

Lecture 80 Cross Tables and Scatter Plots Exercise

Lecture 81 Mean, median and mode

Lecture 82 Mean, Median and Mode Exercise

Lecture 83 Skewness

Lecture 84 Skewness Exercise

Lecture 85 Variance

Lecture 86 Variance Exercise

Lecture 87 Standard Deviation and Coefficient of Variation

Lecture 88 Standard Deviation and Coefficient of Variation Exercise

Lecture 89 Covariance

Lecture 90 Covariance Exercise

Lecture 91 Correlation Coefficient

Lecture 92 Correlation Coefficient Exercise

Section 16: Statistics - Practical Example: Descriptive Statistics

Lecture 93 Practical Example: Descriptive Statistics

Lecture 94 Practical Example: Descriptive Statistics Exercise

Section 17: Statistics - Inferential Statistics Fundamentals

Lecture 95 Introduction

Lecture 96 What is a Distribution

Lecture 97 The Normal Distribution

Lecture 98 The Standard Normal Distribution

Lecture 99 The Standard Normal Distribution Exercise

Lecture 100 Central Limit Theorem

Lecture 101 Standard error

Lecture 102 Estimators and Estimates

Section 18: Statistics - Inferential Statistics: Confidence Intervals

Lecture 103 What are Confidence Intervals?

Lecture 104 Confidence Intervals; Population Variance Known; Z-score

Lecture 105 Confidence Intervals; Population Variance Known; Z-score; Exercise

Lecture 106 Confidence Interval Clarifications

Lecture 107 Student's T Distribution

Lecture 108 Confidence Intervals; Population Variance Unknown; T-score

Lecture 109 Confidence Intervals; Population Variance Unknown; T-score; Exercise

Lecture 110 Margin of Error

Lecture 111 Confidence intervals. Two means. Dependent samples

Lecture 112 Confidence intervals. Two means. Dependent samples Exercise

Lecture 113 Confidence intervals. Two means. Independent Samples (Part 1)

Lecture 114 Confidence intervals. Two means. Independent Samples (Part 1). Exercise

Lecture 115 Confidence intervals. Two means. Independent Samples (Part 2)

Lecture 116 Confidence intervals. Two means. Independent Samples (Part 2). Exercise

Lecture 117 Confidence intervals. Two means. Independent Samples (Part 3)

Section 19: Statistics - Practical Example: Inferential Statistics

Lecture 118 Practical Example: Inferential Statistics

Lecture 119 Practical Example: Inferential Statistics Exercise

Section 20: Statistics - Hypothesis Testing

Lecture 120 Null vs Alternative Hypothesis

Lecture 121 Further Reading on Null and Alternative Hypothesis

Lecture 122 Rejection Region and Significance Level

Lecture 123 Type I Error and Type II Error

Lecture 124 Test for the Mean. Population Variance Known

Lecture 125 Test for the Mean. Population Variance Known Exercise

Lecture 126 p-value

Lecture 127 Test for the Mean. Population Variance Unknown

Lecture 128 Test for the Mean. Population Variance Unknown Exercise

Lecture 129 Test for the Mean. Dependent Samples

Lecture 130 Test for the Mean. Dependent Samples Exercise

Lecture 131 Test for the mean. Independent Samples (Part 1)

Lecture 132 Test for the mean. Independent Samples (Part 1). Exercise

Lecture 133 Test for the mean. Independent Samples (Part 2)

Lecture 134 Test for the mean. Independent Samples (Part 2). Exercise

Section 21: Statistics - Practical Example: Hypothesis Testing

Lecture 135 Practical Example: Hypothesis Testing

Lecture 136 Practical Example: Hypothesis Testing Exercise

Section 22: Part 4: Introduction to Python

Lecture 137 Introduction to Programming

Lecture 138 Why Python?

Lecture 139 Why Jupyter?

Lecture 140 Installing Python and Jupyter

Lecture 141 Understanding Jupyter's Interface - the Notebook Dashboard

Lecture 142 Prerequisites for Coding in the Jupyter Notebooks

Section 23: Python - Variables and Data Types

Lecture 143 Variables

Lecture 144 Numbers and Boolean Values in Python

Lecture 145 Python Strings

Section 24: Python - Basic Python Syntax

Lecture 146 Using Arithmetic Operators in Python

Lecture 147 The Double Equality Sign

Lecture 148 How to Reassign Values

Lecture 149 Add Comments

Lecture 150 Understanding Line Continuation

Lecture 151 Indexing Elements

Lecture 152 Structuring with Indentation

Section 25: Python - Other Python Operators

Lecture 153 Comparison Operators

Lecture 154 Logical and Identity Operators

Section 26: Python - Conditional Statements

Lecture 155 The IF Statement

Lecture 156 The ELSE Statement

Lecture 157 The ELIF Statement

Lecture 158 A Note on Boolean Values

Section 27: Python - Python Functions

Lecture 159 Defining a Function in Python

Lecture 160 How to Create a Function with a Parameter

Lecture 161 Defining a Function in Python - Part II

Lecture 162 How to Use a Function within a Function

Lecture 163 Conditional Statements and Functions

Lecture 164 Functions Containing a Few Arguments

Lecture 165 Built-in Functions in Python

Section 28: Python - Sequences

Lecture 166 Lists

Lecture 167 Using Methods

Lecture 168 List Slicing

Lecture 169 Tuples

Lecture 170 Dictionaries

Section 29: Python - Iterations

Lecture 171 For Loops

Lecture 172 While Loops and Incrementing

Lecture 173 Lists with the range() Function

Lecture 174 Conditional Statements and Loops

Lecture 175 Conditional Statements, Functions, and Loops

Lecture 176 How to Iterate over Dictionaries

Section 30: Python - Advanced Python Tools

Lecture 177 Object Oriented Programming

Lecture 178 Modules and Packages

Lecture 179 What is the Standard Library?

Lecture 180 Importing Modules in Python

Section 31: Part 5: Advanced Statistical Methods in Python

Lecture 181 Introduction to Regression Analysis

Section 32: Advanced Statistical Methods - Linear Regression with StatsModels

Lecture 182 The Linear Regression Model

Lecture 183 Correlation vs Regression

Lecture 184 Geometrical Representation of the Linear Regression Model

Lecture 185 Python Packages Installation

Lecture 186 First Regression in Python

Lecture 187 First Regression in Python Exercise

Lecture 188 Using Seaborn for Graphs

Lecture 189 How to Interpret the Regression Table

Lecture 190 Decomposition of Variability

Lecture 191 What is the OLS?

Lecture 192 R-Squared

Section 33: Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Lecture 193 Multiple Linear Regression

Lecture 194 Adjusted R-Squared

Lecture 195 Multiple Linear Regression Exercise

Lecture 196 Test for Significance of the Model (F-Test)

Lecture 197 OLS Assumptions

Lecture 198 A1: Linearity

Lecture 199 A2: No Endogeneity

Lecture 200 A3: Normality and Homoscedasticity

Lecture 201 A4: No Autocorrelation

Lecture 202 A5: No Multicollinearity

Lecture 203 Dealing with Categorical Data - Dummy Variables

Lecture 204 Dealing with Categorical Data - Dummy Variables

Lecture 205 Making Predictions with the Linear Regression

Section 34: Advanced Statistical Methods - Linear Regression with sklearn

Lecture 206 What is sklearn and How is it Different from Other Packages

Lecture 207 How are we Going to Approach this Section?

Lecture 208 Simple Linear Regression with sklearn

Lecture 209 Simple Linear Regression with sklearn - A StatsModels-like Summary Table

Lecture 210 A Note on Normalization

Lecture 211 Simple Linear Regression with sklearn - Exercise

Lecture 212 Multiple Linear Regression with sklearn

Lecture 213 Calculating the Adjusted R-Squared in sklearn

Lecture 214 Calculating the Adjusted R-Squared in sklearn - Exercise

Lecture 215 Feature Selection (F-regression)

Lecture 216 A Note on Calculation of P-values with sklearn

Lecture 217 Creating a Summary Table with P-values

Lecture 218 Multiple Linear Regression - Exercise

Lecture 219 Feature Scaling (Standardization)

Lecture 220 Feature Selection through Standardization of Weights

Lecture 221 Predicting with the Standardized Coefficients

Lecture 222 Feature Scaling (Standardization) - Exercise

Lecture 223 Underfitting and Overfitting

Lecture 224 Train - Test Split Explained

Section 35: Advanced Statistical Methods - Practical Example: Linear Regression

Lecture 225 Practical Example: Linear Regression (Part 1)

Lecture 226 Practical Example: Linear Regression (Part 2)

Lecture 227 A Note on Multicollinearity

Lecture 228 Practical Example: Linear Regression (Part 3)

Lecture 229 Dummies and Variance Inflation Factor - Exercise

Lecture 230 Practical Example: Linear Regression (Part 4)

Lecture 231 Dummy Variables - Exercise

Lecture 232 Practical Example: Linear Regression (Part 5)

Lecture 233 Linear Regression - Exercise

Section 36: Advanced Statistical Methods - Logistic Regression

Lecture 234 Introduction to Logistic Regression

Lecture 235 A Simple Example in Python

Lecture 236 Logistic vs Logit Function

Lecture 237 Building a Logistic Regression

Lecture 238 Building a Logistic Regression - Exercise

Lecture 239 An Invaluable Coding Tip

Lecture 240 Understanding Logistic Regression Tables

Lecture 241 Understanding Logistic Regression Tables - Exercise

Lecture 242 What do the Odds Actually Mean

Lecture 243 Binary Predictors in a Logistic Regression

Lecture 244 Binary Predictors in a Logistic Regression - Exercise

Lecture 245 Calculating the Accuracy of the Model

Lecture 246 Calculating the Accuracy of the Model

Lecture 247 Underfitting and Overfitting

Lecture 248 Testing the Model

Lecture 249 Testing the Model - Exercise

Section 37: Advanced Statistical Methods - Cluster Analysis

Lecture 250 Introduction to Cluster Analysis

Lecture 251 Some Examples of Clusters

Lecture 252 Difference between Classification and Clustering

Lecture 253 Math Prerequisites

Section 38: Advanced Statistical Methods - K-Means Clustering

Lecture 254 K-Means Clustering

Lecture 255 A Simple Example of Clustering

Lecture 256 A Simple Example of Clustering - Exercise

Lecture 257 Clustering Categorical Data

Lecture 258 Clustering Categorical Data - Exercise

Lecture 259 How to Choose the Number of Clusters

Lecture 260 How to Choose the Number of Clusters - Exercise

Lecture 261 Pros and Cons of K-Means Clustering

Lecture 262 To Standardize or not to Standardize

Lecture 263 Relationship between Clustering and Regression

Lecture 264 Market Segmentation with Cluster Analysis (Part 1)

Lecture 265 Market Segmentation with Cluster Analysis (Part 2)

Lecture 266 How is Clustering Useful?

Lecture 267 EXERCISE: Species Segmentation with Cluster Analysis (Part 1)

Lecture 268 EXERCISE: Species Segmentation with Cluster Analysis (Part 2)

Section 39: Advanced Statistical Methods - Other Types of Clustering

Lecture 269 Types of Clustering

Lecture 270 Dendrogram

Lecture 271 Heatmaps

Section 40: Part 6: Mathematics

Lecture 272 What is a Matrix?

Lecture 273 Scalars and Vectors

Lecture 274 Linear Algebra and Geometry

Lecture 275 Arrays in Python - A Convenient Way To Represent Matrices

Lecture 276 What is a Tensor?

Lecture 277 Addition and Subtraction of Matrices

Lecture 278 Errors when Adding Matrices

Lecture 279 Transpose of a Matrix

Lecture 280 Dot Product

Lecture 281 Dot Product of Matrices

Lecture 282 Why is Linear Algebra Useful?

Section 41: Part 7: Deep Learning

Lecture 283 What to Expect from this Part?

Section 42: Deep Learning - Introduction to Neural Networks

Lecture 284 Introduction to Neural Networks

Lecture 285 Training the Model

Lecture 286 Types of Machine Learning

Lecture 287 The Linear Model (Linear Algebraic Version)

Lecture 288 The Linear Model with Multiple Inputs

Lecture 289 The Linear model with Multiple Inputs and Multiple Outputs

Lecture 290 Graphical Representation of Simple Neural Networks

Lecture 291 What is the Objective Function?

Lecture 292 Common Objective Functions: L2-norm Loss

Lecture 293 Common Objective Functions: Cross-Entropy Loss

Lecture 294 Optimization Algorithm: 1-Parameter Gradient Descent

Lecture 295 Optimization Algorithm: n-Parameter Gradient Descent

Section 43: Deep Learning - How to Build a Neural Network from Scratch with NumPy

Lecture 296 Basic NN Example (Part 1)

Lecture 297 Basic NN Example (Part 2)

Lecture 298 Basic NN Example (Part 3)

Lecture 299 Basic NN Example (Part 4)

Lecture 300 Basic NN Example Exercises

Section 44: Deep Learning - TensorFlow 2.0: Introduction

Lecture 301 How to Install TensorFlow 2.0

Lecture 302 TensorFlow Outline and Comparison with Other Libraries

Lecture 303 TensorFlow 1 vs TensorFlow 2

Lecture 304 A Note on TensorFlow 2 Syntax

Lecture 305 Types of File Formats Supporting TensorFlow

Lecture 306 Outlining the Model with TensorFlow 2

Lecture 307 Interpreting the Result and Extracting the Weights and Bias

Lecture 308 Customizing a TensorFlow 2 Model

Lecture 309 Basic NN with TensorFlow: Exercises

Section 45: Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks

Lecture 310 What is a Layer?

Lecture 311 What is a Deep Net?

Lecture 312 Digging into a Deep Net

Lecture 313 Non-Linearities and their Purpose

Lecture 314 Activation Functions

Lecture 315 Activation Functions: Softmax Activation

Lecture 316 Backpropagation

Lecture 317 Backpropagation Picture

Lecture 318 Backpropagation - A Peek into the Mathematics of Optimization

Section 46: Deep Learning - Overfitting

Lecture 319 What is Overfitting?

Lecture 320 Underfitting and Overfitting for Classification

Lecture 321 What is Validation?

Lecture 322 Training, Validation, and Test Datasets

Lecture 323 N-Fold Cross Validation

Lecture 324 Early Stopping or When to Stop Training

Section 47: Deep Learning - Initialization

Lecture 325 What is Initialization?

Lecture 326 Types of Simple Initializations

Lecture 327 State-of-the-Art Method - (Xavier) Glorot Initialization

Section 48: Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

Lecture 328 Stochastic Gradient Descent

Lecture 329 Problems with Gradient Descent

Lecture 330 Momentum

Lecture 331 Learning Rate Schedules, or How to Choose the Optimal Learning Rate

Lecture 332 Learning Rate Schedules Visualized

Lecture 333 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

Lecture 334 Adam (Adaptive Moment Estimation)

Section 49: Deep Learning - Preprocessing

Lecture 335 Preprocessing Introduction

Lecture 336 Types of Basic Preprocessing

Lecture 337 Standardization

Lecture 338 Preprocessing Categorical Data

Lecture 339 Binary and One-Hot Encoding

Section 50: Deep Learning - Classifying on the MNIST Dataset

Lecture 340 MNIST: The Dataset

Lecture 341 MNIST: How to Tackle the MNIST

Lecture 342 MNIST: Importing the Relevant Packages and Loading the Data

Lecture 343 MNIST: Preprocess the Data - Create a Validation Set and Scale It

Lecture 344 MNIST: Preprocess the Data - Scale the Test Data - Exercise

Lecture 345 MNIST: Preprocess the Data - Shuffle and Batch

Lecture 346 MNIST: Preprocess the Data - Shuffle and Batch - Exercise

Lecture 347 MNIST: Outline the Model

Lecture 348 MNIST: Select the Loss and the Optimizer

Lecture 349 MNIST: Learning

Lecture 350 MNIST - Exercises

Lecture 351 MNIST: Testing the Model

Section 51: Deep Learning - Business Case Example

Lecture 352 Business Case: Exploring the Dataset and Identifying Predictors

Lecture 353 Business Case: Outlining the Solution

Lecture 354 Business Case: Balancing the Dataset

Lecture 355 Business Case: Preprocessing the Data

Lecture 356 Business Case: Preprocessing the Data - Exercise

Lecture 357 Business Case: Load the Preprocessed Data

Lecture 358 Business Case: Load the Preprocessed Data - Exercise

Lecture 359 Business Case: Learning and Interpreting the Result

Lecture 360 Business Case: Setting an Early Stopping Mechanism

Lecture 361 Setting an Early Stopping Mechanism - Exercise

Lecture 362 Business Case: Testing the Model

Lecture 363 Business Case: Final Exercise

Section 52: Deep Learning - Conclusion

Lecture 364 Summary on What You've Learned

Lecture 365 What's Further out there in terms of Machine Learning

Lecture 366 DeepMind and Deep Learning

Lecture 367 An overview of CNNs

Lecture 368 An Overview of RNNs

Lecture 369 An Overview of non-NN Approaches

Section 53: Appendix: Deep Learning - TensorFlow 1: Introduction

Lecture 370 READ ME!!!!

Lecture 371 How to Install TensorFlow 1

Lecture 372 A Note on Installing Packages in Anaconda

Lecture 373 TensorFlow Intro

Lecture 374 Actual Introduction to TensorFlow

Lecture 375 Types of File Formats, supporting Tensors

Lecture 376 Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases

Lecture 377 Basic NN Example with TF: Loss Function and Gradient Descent

Lecture 378 Basic NN Example with TF: Model Output

Lecture 379 Basic NN Example with TF Exercises

Section 54: Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset

Lecture 380 MNIST: What is the MNIST Dataset?

Lecture 381 MNIST: How to Tackle the MNIST

Lecture 382 MNIST: Relevant Packages

Lecture 383 MNIST: Model Outline

Lecture 384 MNIST: Loss and Optimization Algorithm

Lecture 385 Calculating the Accuracy of the Model

Lecture 386 MNIST: Batching and Early Stopping

Lecture 387 MNIST: Learning

Lecture 388 MNIST: Results and Testing

Lecture 389 MNIST: Exercises

Lecture 390 MNIST: Solutions

Section 55: Appendix: Deep Learning - TensorFlow 1: Business Case

Lecture 391 Business Case: Getting Acquainted with the Dataset

Lecture 392 Business Case: Outlining the Solution

Lecture 393 The Importance of Working with a Balanced Dataset

Lecture 394 Business Case: Preprocessing

Lecture 395 Business Case: Preprocessing Exercise

Lecture 396 Creating a Data Provider

Lecture 397 Business Case: Model Outline

Lecture 398 Business Case: Optimization

Lecture 399 Business Case: Interpretation

Lecture 400 Business Case: Testing the Model

Lecture 401 Business Case: A Comment on the Homework

Lecture 402 Business Case: Final Exercise

Section 56: Software Integration

Lecture 403 What are Data, Servers, Clients, Requests, and Responses

Lecture 404 What are Data Connectivity, APIs, and Endpoints?

Lecture 405 Taking a Closer Look at APIs

Lecture 406 Communication between Software Products through Text Files

Lecture 407 Software Integration - Explained

Section 57: Case Study - What's Next in the Course?

Lecture 408 Game Plan for this Python, SQL, and Tableau Business Exercise

Lecture 409 The Business Task

Lecture 410 Introducing the Data Set

Section 58: Case Study - Preprocessing the 'Absenteeism_data'

Lecture 411 What to Expect from the Following Sections?

Lecture 412 Importing the Absenteeism Data in Python

Lecture 413 Checking the Content of the Data Set

Lecture 414 Introduction to Terms with Multiple Meanings

Lecture 415 What's Regression Analysis - a Quick Refresher

Lecture 416 Using a Statistical Approach towards the Solution to the Exercise

Lecture 417 Dropping a Column from a DataFrame in Python

Lecture 418 EXERCISE - Dropping a Column from a DataFrame in Python

Lecture 419 SOLUTION - Dropping a Column from a DataFrame in Python

Lecture 420 Analyzing the Reasons for Absence

Lecture 421 Obtaining Dummies from a Single Feature

Lecture 422 EXERCISE - Obtaining Dummies from a Single Feature

Lecture 423 SOLUTION - Obtaining Dummies from a Single Feature

Lecture 424 Dropping a Dummy Variable from the Data Set

Lecture 425 More on Dummy Variables: A Statistical Perspective

Lecture 426 Classifying the Various Reasons for Absence

Lecture 427 Using .concat() in Python

Lecture 428 EXERCISE - Using .concat() in Python

Lecture 429 SOLUTION - Using .concat() in Python

Lecture 430 Reordering Columns in a Pandas DataFrame in Python

Lecture 431 EXERCISE - Reordering Columns in a Pandas DataFrame in Python

Lecture 432 SOLUTION - Reordering Columns in a Pandas DataFrame in Python

Lecture 433 Creating Checkpoints while Coding in Jupyter

Lecture 434 EXERCISE - Creating Checkpoints while Coding in Jupyter

Lecture 435 SOLUTION - Creating Checkpoints while Coding in Jupyter

Lecture 436 Analyzing the Dates from the Initial Data Set

Lecture 437 Extracting the Month Value from the "Date" Column

Lecture 438 Extracting the Day of the Week from the "Date" Column

Lecture 439 EXERCISE - Removing the "Date" Column

Lecture 440 Analyzing Several "Straightforward" Columns for this Exercise

Lecture 441 Working on "Education", "Children", and "Pets"

Lecture 442 Final Remarks of this Section

Lecture 443 A Note on Exporting Your Data as a *.csv File

Section 59: Case Study - Applying Machine Learning to Create the 'absenteeism_module'

Lecture 444 Exploring the Problem with a Machine Learning Mindset

Lecture 445 Creating the Targets for the Logistic Regression

Lecture 446 Selecting the Inputs for the Logistic Regression

Lecture 447 Standardizing the Data

Lecture 448 Splitting the Data for Training and Testing

Lecture 449 Fitting the Model and Assessing its Accuracy

Lecture 450 Creating a Summary Table with the Coefficients and Intercept

Lecture 451 Interpreting the Coefficients for Our Problem

Lecture 452 Standardizing only the Numerical Variables (Creating a Custom Scaler)

Lecture 453 Interpreting the Coefficients of the Logistic Regression

Lecture 454 Backward Elimination or How to Simplify Your Model

Lecture 455 Testing the Model We Created

Lecture 456 Saving the Model and Preparing it for Deployment

Lecture 457 ARTICLE - A Note on 'pickling'

Lecture 458 EXERCISE - Saving the Model (and Scaler)

Lecture 459 Preparing the Deployment of the Model through a Module

Section 60: Case Study - Loading the 'absenteeism_module'

Lecture 460 Are You Sure You're All Set?

Lecture 461 Deploying the 'absenteeism_module' - Part I

Lecture 462 Deploying the 'absenteeism_module' - Part II

Lecture 463 Exporting the Obtained Data Set as a *.csv

Section 61: Case Study - Analyzing the Predicted Outputs in Tableau

Lecture 464 EXERCISE - Age vs Probability

Lecture 465 Analyzing Age vs Probability in Tableau

Lecture 466 EXERCISE - Reasons vs Probability

Lecture 467 Analyzing Reasons vs Probability in Tableau

Lecture 468 EXERCISE - Transportation Expense vs Probability

Lecture 469 Analyzing Transportation Expense vs Probability in Tableau

Section 62: Appendix - Additional Python Tools

Lecture 470 Using the .format() Method

Lecture 471 Iterating Over Range Objects

Lecture 472 Introduction to Nested For Loops

Lecture 473 Triple Nested For Loops

Lecture 474 List Comprehensions

Lecture 475 Anonymous (Lambda) Functions

Section 63: Appendix - pandas Fundamentals

Lecture 476 Introduction to pandas Series

Lecture 477 Working with Methods in Python - Part I

Lecture 478 Working with Methods in Python - Part II

Lecture 479 Parameters and Arguments in pandas

Lecture 480 Using .unique() and .nunique()

Lecture 481 Using .sort_values()

Lecture 482 Introduction to pandas DataFrames - Part I

Lecture 483 Introduction to pandas DataFrames - Part II

Lecture 484 pandas DataFrames - Common Attributes

Lecture 485 Data Selection in pandas DataFrames

Lecture 486 pandas DataFrames - Indexing with .iloc[]

Lecture 487 pandas DataFrames - Indexing with .loc[]

Section 64: Appendix - Working with Text Files in Python

Lecture 488 An Introduction to Working with Files in Python

Lecture 489 File vs File Object, Reading vs Parsing Data

Lecture 490 Structured, Semi-Structured and Unstructured Data

Lecture 491 Text Files and Data Connectivity

Lecture 492 Importing Data in Python - Principles

Lecture 493 Plain Text Files, Flat Files and More

Lecture 494 Text Files of Fixed Width

Lecture 495 Common Naming Conventions

Lecture 496 Importing Text Files - open()

Lecture 497 Importing Text Files - with open()

Lecture 498 Importing *.csv Files - Part I

Lecture 499 Importing *.csv Files - Part II

Lecture 500 Importing *.csv Files - Part III

Lecture 501 Importing Data with index_col

Lecture 502 Importing Data with .loadtxt() and .genfromtxt()

Lecture 503 Importing Data - Partial Cleaning While Importing Data

Lecture 504 Importing Data with NumPy - Exercise

Lecture 505 Importing Data from *.json Files

Lecture 506 An Introduction to Working with Excel Files in Python

Lecture 507 Working with Excel (*.xlsx) Data

Lecture 508 Importing Data in Python - an Important Exercise

Lecture 509 Importing Data with the .squeeze() Method

Lecture 510 Importing Files in Jupyter

Lecture 511 Saving Your Data with pandas

Lecture 512 Saving Your Data with NumPy - Part I - *.npy

Lecture 513 Saving Your Data with NumPy - Part II - *.npz

Lecture 514 Saving Your Data with NumPy - Part III - *.csv

Lecture 515 Saving Data with Numpy - Exercise

Lecture 516 Working with Text Files in Python - Conclusion

Section 65: Bonus Lecture

Lecture 517 Bonus Lecture: Next Steps

You should take this course if you want to become a Data Scientist or if you want to learn about the field,This course is for you if you want a great career,The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills