The Data Science Course: Complete Data Science Bootcamp 2023
Data 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).
More
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 2020.
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 Science
Big 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. Python
Python 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. Tableau
Data 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
Why wait? Every day is a missed opportunity.
- 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
- 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
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
Watch Online The Data Science Course: Complete Data Science Bootcamp 2023
| # | Title | Duration |
|---|---|---|
| 1 | A Practical Example: What You Will Learn in This Course | 05:07 |
| 2 | What Does the Course Cover | 03:35 |
| 3 | Data Science and Business Buzzwords: Why are there so Many? | 05:22 |
| 4 | What is the difference between Analysis and Analytics | 03:51 |
| 5 | Business Analytics, Data Analytics, and Data Science: An Introduction | 08:27 |
| 6 | Continuing with BI, ML, and AI | 09:32 |
| 7 | A Breakdown of our Data Science Infographic | 04:04 |
| 8 | Applying Traditional Data, Big Data, BI, Traditional Data Science and ML | 07:20 |
| 9 | The Reason Behind These Disciplines | 04:46 |
| 10 | Techniques for Working with Traditional Data | 08:15 |
| 11 | Real Life Examples of Traditional Data | 01:45 |
| 12 | Techniques for Working with Big Data | 04:27 |
| 13 | Real Life Examples of Big Data | 01:33 |
| 14 | Business Intelligence (BI) Techniques | 06:47 |
| 15 | Real Life Examples of Business Intelligence (BI) | 01:43 |
| 16 | Techniques for Working with Traditional Methods | 09:09 |
| 17 | Real Life Examples of Traditional Methods | 02:47 |
| 18 | Machine Learning (ML) Techniques | 06:56 |
| 19 | Types of Machine Learning | 08:14 |
| 20 | Real Life Examples of Machine Learning (ML) | 02:12 |
| 21 | Necessary Programming Languages and Software Used in Data Science | 05:52 |
| 22 | Finding the Job - What to Expect and What to Look for | 03:30 |
| 23 | Debunking Common Misconceptions | 04:11 |
| 24 | The Basic Probability Formula | 07:10 |
| 25 | Computing Expected Values | 05:30 |
| 26 | Frequency | 05:01 |
| 27 | Events and Their Complements | 05:27 |
| 28 | Fundamentals of Combinatorics | 01:05 |
| 29 | Permutations and How to Use Them | 03:22 |
| 30 | Simple Operations with Factorials | 03:36 |
| 31 | Solving Variations with Repetition | 03:00 |
| 32 | Solving Variations without Repetition | 03:49 |
| 33 | Solving Combinations | 04:52 |
| 34 | Symmetry of Combinations | 03:27 |
| 35 | Solving Combinations with Separate Sample Spaces | 02:53 |
| 36 | Combinatorics in Real-Life: The Lottery | 03:13 |
| 37 | A Recap of Combinatorics | 02:56 |
| 38 | A Practical Example of Combinatorics | 10:54 |
| 39 | Sets and Events | 04:26 |
| 40 | Ways Sets Can Interact | 03:46 |
| 41 | Intersection of Sets | 02:07 |
| 42 | Union of Sets | 04:52 |
| 43 | Mutually Exclusive Sets | 02:10 |
| 44 | Dependence and Independence of Sets | 03:02 |
| 45 | The Conditional Probability Formula | 04:17 |
| 46 | The Law of Total Probability | 03:04 |
| 47 | The Additive Rule | 02:22 |
| 48 | The Multiplication Law | 04:06 |
| 49 | Bayes' Law | 05:45 |
| 50 | A Practical Example of Bayesian Inference | 14:53 |
| 51 | Fundamentals of Probability Distributions | 06:30 |
| 52 | Types of Probability Distributions | 07:33 |
| 53 | Characteristics of Discrete Distributions | 02:01 |
| 54 | Discrete Distributions: The Uniform Distribution | 02:14 |
| 55 | Discrete Distributions: The Bernoulli Distribution | 03:28 |
| 56 | Discrete Distributions: The Binomial Distribution | 07:05 |
| 57 | Discrete Distributions: The Poisson Distribution | 05:28 |
| 58 | Characteristics of Continuous Distributions | 07:13 |
| 59 | Continuous Distributions: The Normal Distribution | 04:09 |
| 60 | Continuous Distributions: The Standard Normal Distribution | 04:26 |
| 61 | Continuous Distributions: The Students' T Distribution | 02:30 |
| 62 | Continuous Distributions: The Chi-Squared Distribution | 02:23 |
| 63 | Continuous Distributions: The Exponential Distribution | 03:16 |
| 64 | Continuous Distributions: The Logistic Distribution | 04:08 |
| 65 | A Practical Example of Probability Distributions | 15:04 |
| 66 | Probability in Finance | 07:47 |
| 67 | Probability in Statistics | 06:19 |
| 68 | Probability in Data Science | 04:48 |
| 69 | Population and Sample | 04:03 |
| 70 | Types of Data | 04:34 |
| 71 | Levels of Measurement | 03:44 |
| 72 | Categorical Variables - Visualization Techniques | 04:53 |
| 73 | Numerical Variables - Frequency Distribution Table | 03:10 |
| 74 | The Histogram | 02:15 |
| 75 | Cross Tables and Scatter Plots | 04:45 |
| 76 | Mean, median and mode | 04:21 |
| 77 | Skewness | 02:38 |
| 78 | Variance | 05:56 |
| 79 | Standard Deviation and Coefficient of Variation | 04:41 |
| 80 | Covariance | 03:24 |
| 81 | Correlation Coefficient | 03:18 |
| 82 | Practical Example: Descriptive Statistics | 16:17 |
| 83 | Introduction | 01:02 |
| 84 | What is a Distribution | 04:34 |
| 85 | The Normal Distribution | 03:55 |
| 86 | The Standard Normal Distribution | 03:32 |
| 87 | Central Limit Theorem | 04:21 |
| 88 | Standard error | 01:28 |
| 89 | Estimators and Estimates | 03:08 |
| 90 | What are Confidence Intervals? | 02:42 |
| 91 | Confidence Intervals; Population Variance Known; Z-score | 08:02 |
| 92 | Confidence Interval Clarifications | 04:39 |
| 93 | Student's T Distribution | 03:24 |
| 94 | Confidence Intervals; Population Variance Unknown; T-score | 04:37 |
| 95 | Margin of Error | 04:54 |
| 96 | Confidence intervals. Two means. Dependent samples | 06:05 |
| 97 | Confidence intervals. Two means. Independent Samples (Part 1) | 04:32 |
| 98 | Confidence intervals. Two means. Independent Samples (Part 2) | 03:58 |
| 99 | Confidence intervals. Two means. Independent Samples (Part 3) | 01:28 |
| 100 | Practical Example: Inferential Statistics | 10:07 |
| 101 | Null vs Alternative Hypothesis | 05:53 |
| 102 | Rejection Region and Significance Level | 07:06 |
| 103 | Type I Error and Type II Error | 04:15 |
| 104 | Test for the Mean. Population Variance Known | 06:35 |
| 105 | p-value | 04:14 |
| 106 | Test for the Mean. Population Variance Unknown | 04:50 |
| 107 | Test for the Mean. Dependent Samples | 05:19 |
| 108 | Test for the mean. Independent Samples (Part 1) | 04:23 |
| 109 | Test for the mean. Independent Samples (Part 2) | 04:27 |
| 110 | Practical Example: Hypothesis Testing | 07:17 |
| 111 | Introduction to Programming | 05:05 |
| 112 | Why Python? | 05:12 |
| 113 | Why Jupyter? | 03:30 |
| 114 | Installing Python and Jupyter | 06:50 |
| 115 | Understanding Jupyter's Interface - the Notebook Dashboard | 03:16 |
| 116 | Prerequisites for Coding in the Jupyter Notebooks | 06:16 |
| 117 | Variables | 03:38 |
| 118 | Numbers and Boolean Values in Python | 03:06 |
| 119 | Python Strings | 05:41 |
| 120 | Using Arithmetic Operators in Python | 03:24 |
| 121 | The Double Equality Sign | 01:34 |
| 122 | How to Reassign Values | 01:09 |
| 123 | Add Comments | 01:35 |
| 124 | Understanding Line Continuation | 00:50 |
| 125 | Indexing Elements | 01:19 |
| 126 | Structuring with Indentation | 01:45 |
| 127 | Comparison Operators | 02:11 |
| 128 | Logical and Identity Operators | 05:37 |
| 129 | The IF Statement | 03:02 |
| 130 | The ELSE Statement | 02:46 |
| 131 | The ELIF Statement | 05:35 |
| 132 | A Note on Boolean Values | 02:15 |
| 133 | Defining a Function in Python | 02:03 |
| 134 | How to Create a Function with a Parameter | 03:50 |
| 135 | Defining a Function in Python - Part II | 02:37 |
| 136 | How to Use a Function within a Function | 01:50 |
| 137 | Conditional Statements and Functions | 03:07 |
| 138 | Functions Containing a Few Arguments | 01:18 |
| 139 | Built-in Functions in Python | 03:57 |
| 140 | Lists | 08:19 |
| 141 | List Slicing | 04:32 |
| 142 | Tuples | 06:41 |
| 143 | Dictionaries | 08:28 |
| 144 | For Loops | 05:41 |
| 145 | While Loops and Incrementing | 05:11 |
| 146 | Lists with the range() Function | 06:23 |
| 147 | Conditional Statements and Loops | 06:31 |
| 148 | Conditional Statements, Functions, and Loops | 02:28 |
| 149 | How to Iterate over Dictionaries | 06:22 |
| 150 | Object Oriented Programming | 05:01 |
| 151 | Modules and Packages | 01:07 |
| 152 | What is the Standard Library? | 02:48 |
| 153 | Importing Modules in Python | 04:05 |
| 154 | Introduction to Regression Analysis | 01:28 |
| 155 | The Linear Regression Model | 05:51 |
| 156 | Correlation vs Regression | 01:45 |
| 157 | Geometrical Representation of the Linear Regression Model | 01:26 |
| 158 | Python Packages Installation | 04:40 |
| 159 | First Regression in Python | 07:12 |
| 160 | Using Seaborn for Graphs | 01:22 |
| 161 | How to Interpret the Regression Table | 05:48 |
| 162 | Decomposition of Variability | 03:39 |
| 163 | What is the OLS? | 03:14 |
| 164 | R-Squared | 05:31 |
| 165 | Multiple Linear Regression | 02:57 |
| 166 | Adjusted R-Squared | 06:01 |
| 167 | Test for Significance of the Model (F-Test) | 02:02 |
| 168 | OLS Assumptions | 02:22 |
| 169 | A1: Linearity | 01:51 |
| 170 | A2: No Endogeneity | 04:10 |
| 171 | A3: Normality and Homoscedasticity | 05:48 |
| 172 | A4: No Autocorrelation | 03:32 |
| 173 | A5: No Multicollinearity | 03:27 |
| 174 | Dealing with Categorical Data - Dummy Variables | 06:44 |
| 175 | Making Predictions with the Linear Regression | 03:30 |
| 176 | What is sklearn and How is it Different from Other Packages | 02:15 |
| 177 | How are we Going to Approach this Section? | 01:57 |
| 178 | Simple Linear Regression with sklearn | 05:39 |
| 179 | Simple Linear Regression with sklearn - A StatsModels-like Summary Table | 04:50 |
| 180 | Multiple Linear Regression with sklearn | 03:11 |
| 181 | Calculating the Adjusted R-Squared in sklearn | 04:47 |
| 182 | Feature Selection (F-regression) | 04:42 |
| 183 | Creating a Summary Table with P-values | 02:11 |
| 184 | Feature Scaling (Standardization) | 05:39 |
| 185 | Feature Selection through Standardization of Weights | 05:23 |
| 186 | Predicting with the Standardized Coefficients | 03:54 |
| 187 | Underfitting and Overfitting | 02:43 |
| 188 | Train - Test Split Explained | 06:55 |
| 189 | Practical Example: Linear Regression (Part 1) | 12:00 |
| 190 | Practical Example: Linear Regression (Part 2) | 06:13 |
| 191 | Practical Example: Linear Regression (Part 3) | 03:17 |
| 192 | Practical Example: Linear Regression (Part 4) | 08:11 |
| 193 | Practical Example: Linear Regression (Part 5) | 07:35 |
| 194 | Introduction to Logistic Regression | 01:20 |
| 195 | A Simple Example in Python | 04:43 |
| 196 | Logistic vs Logit Function | 04:01 |
| 197 | Building a Logistic Regression | 02:49 |
| 198 | An Invaluable Coding Tip | 02:27 |
| 199 | Understanding Logistic Regression Tables | 04:07 |
| 200 | What do the Odds Actually Mean | 04:31 |
| 201 | Binary Predictors in a Logistic Regression | 04:33 |
| 202 | Calculating the Accuracy of the Model | 03:22 |
| 203 | Underfitting and Overfitting | 03:44 |
| 204 | Testing the Model | 05:06 |
| 205 | Introduction to Cluster Analysis | 03:42 |
| 206 | Some Examples of Clusters | 04:32 |
| 207 | Difference between Classification and Clustering | 02:33 |
| 208 | Math Prerequisites | 03:21 |
| 209 | K-Means Clustering | 04:42 |
| 210 | A Simple Example of Clustering | 07:49 |
| 211 | Clustering Categorical Data | 02:51 |
| 212 | How to Choose the Number of Clusters | 06:12 |
| 213 | Pros and Cons of K-Means Clustering | 03:24 |
| 214 | To Standardize or not to Standardize | 04:34 |
| 215 | Relationship between Clustering and Regression | 01:32 |
| 216 | Market Segmentation with Cluster Analysis (Part 1) | 06:05 |
| 217 | Market Segmentation with Cluster Analysis (Part 2) | 06:59 |
| 218 | How is Clustering Useful? | 04:49 |
| 219 | Types of Clustering | 03:40 |
| 220 | Dendrogram | 05:22 |
| 221 | Heatmaps | 04:35 |
| 222 | What is a Matrix? | 03:38 |
| 223 | Scalars and Vectors | 02:59 |
| 224 | Linear Algebra and Geometry | 03:07 |
| 225 | Arrays in Python - A Convenient Way To Represent Matrices | 05:10 |
| 226 | What is a Tensor? | 03:01 |
| 227 | Addition and Subtraction of Matrices | 03:37 |
| 228 | Errors when Adding Matrices | 02:02 |
| 229 | Transpose of a Matrix | 05:14 |
| 230 | Dot Product | 03:49 |
| 231 | Dot Product of Matrices | 08:24 |
| 232 | Why is Linear Algebra Useful? | 10:11 |
| 233 | What to Expect from this Part? | 03:09 |
| 234 | Introduction to Neural Networks | 04:10 |
| 235 | Training the Model | 02:55 |
| 236 | Types of Machine Learning | 03:44 |
| 237 | The Linear Model (Linear Algebraic Version) | 03:09 |
| 238 | The Linear Model with Multiple Inputs | 02:26 |
| 239 | The Linear model with Multiple Inputs and Multiple Outputs | 04:27 |
| 240 | Graphical Representation of Simple Neural Networks | 01:48 |
| 241 | What is the Objective Function? | 01:28 |
| 242 | Common Objective Functions: L2-norm Loss | 02:05 |
| 243 | Common Objective Functions: Cross-Entropy Loss | 03:56 |
| 244 | Optimization Algorithm: 1-Parameter Gradient Descent | 06:34 |
| 245 | Optimization Algorithm: n-Parameter Gradient Descent | 06:09 |
| 246 | Basic NN Example (Part 1) | 03:07 |
| 247 | Basic NN Example (Part 2) | 05:00 |
| 248 | Basic NN Example (Part 3) | 03:26 |
| 249 | Basic NN Example (Part 4) | 08:16 |
| 250 | How to Install TensorFlow 2.0 | 05:03 |
| 251 | TensorFlow Outline and Comparison with Other Libraries | 03:29 |
| 252 | TensorFlow 1 vs TensorFlow 2 | 02:34 |
| 253 | A Note on TensorFlow 2 Syntax | 00:59 |
| 254 | Types of File Formats Supporting TensorFlow | 02:35 |
| 255 | Outlining the Model with TensorFlow 2 | 05:49 |
| 256 | Interpreting the Result and Extracting the Weights and Bias | 04:10 |
| 257 | Customizing a TensorFlow 2 Model | 02:52 |
| 258 | What is a Layer? | 01:54 |
| 259 | What is a Deep Net? | 02:19 |
| 260 | Digging into a Deep Net | 04:59 |
| 261 | Non-Linearities and their Purpose | 03:00 |
| 262 | Activation Functions | 03:38 |
| 263 | Activation Functions: Softmax Activation | 03:25 |
| 264 | Backpropagation | 03:13 |
| 265 | Backpropagation Picture | 03:03 |
| 266 | What is Overfitting? | 03:52 |
| 267 | Underfitting and Overfitting for Classification | 01:53 |
| 268 | What is Validation? | 03:23 |
| 269 | Training, Validation, and Test Datasets | 02:31 |
| 270 | N-Fold Cross Validation | 03:08 |
| 271 | Early Stopping or When to Stop Training | 04:55 |
| 272 | What is Initialization? | 02:33 |
| 273 | Types of Simple Initializations | 02:48 |
| 274 | State-of-the-Art Method - (Xavier) Glorot Initialization | 02:46 |
| 275 | Stochastic Gradient Descent | 03:25 |
| 276 | Problems with Gradient Descent | 02:03 |
| 277 | Momentum | 02:31 |
| 278 | Learning Rate Schedules, or How to Choose the Optimal Learning Rate | 04:26 |
| 279 | Learning Rate Schedules Visualized | 01:33 |
| 280 | Adaptive Learning Rate Schedules (AdaGrad and RMSprop ) | 04:09 |
| 281 | Adam (Adaptive Moment Estimation) | 02:40 |
| 282 | Preprocessing Introduction | 02:52 |
| 283 | Types of Basic Preprocessing | 01:18 |
| 284 | Standardization | 04:32 |
| 285 | Preprocessing Categorical Data | 02:16 |
| 286 | Binary and One-Hot Encoding | 03:40 |
| 287 | MNIST: The Dataset | 02:26 |
| 288 | MNIST: How to Tackle the MNIST | 02:45 |
| 289 | MNIST: Importing the Relevant Packages and Loading the Data | 02:12 |
| 290 | MNIST: Preprocess the Data - Create a Validation Set and Scale It | 04:44 |
| 291 | MNIST: Preprocess the Data - Shuffle and Batch | 06:31 |
| 292 | MNIST: Outline the Model | 04:55 |
| 293 | MNIST: Select the Loss and the Optimizer | 02:06 |
| 294 | MNIST: Learning | 05:39 |
| 295 | MNIST: Testing the Model | 03:57 |
| 296 | Business Case: Exploring the Dataset and Identifying Predictors | 07:55 |
| 297 | Business Case: Outlining the Solution | 01:32 |
| 298 | Business Case: Balancing the Dataset | 03:40 |
| 299 | Business Case: Preprocessing the Data | 11:33 |
| 300 | Business Case: Load the Preprocessed Data | 03:24 |
| 301 | Business Case: Learning and Interpreting the Result | 04:16 |
| 302 | Business Case: Setting an Early Stopping Mechanism | 05:02 |
| 303 | Business Case: Testing the Model | 01:24 |
| 304 | Summary on What You've Learned | 03:42 |
| 305 | What's Further out there in terms of Machine Learning | 01:48 |
| 306 | An overview of CNNs | 04:57 |
| 307 | An Overview of RNNs | 02:51 |
| 308 | An Overview of non-NN Approaches | 03:53 |
| 309 | How to Install TensorFlow 1 | 02:21 |
| 310 | TensorFlow Intro | 03:47 |
| 311 | Actual Introduction to TensorFlow | 01:41 |
| 312 | Types of File Formats, supporting Tensors | 02:39 |
| 313 | Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases | 06:06 |
| 314 | Basic NN Example with TF: Loss Function and Gradient Descent | 03:42 |
| 315 | Basic NN Example with TF: Model Output | 06:06 |
| 316 | MNIST: What is the MNIST Dataset? | 02:27 |
| 317 | MNIST: How to Tackle the MNIST | 02:49 |
| 318 | MNIST: Relevant Packages | 01:36 |
| 319 | MNIST: Model Outline | 06:52 |
| 320 | MNIST: Loss and Optimization Algorithm | 02:40 |
| 321 | Calculating the Accuracy of the Model | 04:19 |
| 322 | MNIST: Batching and Early Stopping | 02:09 |
| 323 | MNIST: Learning | 07:36 |
| 324 | MNIST: Results and Testing | 06:12 |
| 325 | Business Case: Getting Acquainted with the Dataset | 07:56 |
| 326 | Business Case: Outlining the Solution | 01:58 |
| 327 | The Importance of Working with a Balanced Dataset | 03:40 |
| 328 | Business Case: Preprocessing | 11:36 |
| 329 | Creating a Data Provider | 06:38 |
| 330 | Business Case: Model Outline | 05:36 |
| 331 | Business Case: Optimization | 05:11 |
| 332 | Business Case: Interpretation | 02:06 |
| 333 | Business Case: Testing the Model | 02:05 |
| 334 | Business Case: A Comment on the Homework | 03:52 |
| 335 | What are Data, Servers, Clients, Requests, and Responses | 04:45 |
| 336 | What are Data Connectivity, APIs, and Endpoints? | 07:06 |
| 337 | Taking a Closer Look at APIs | 08:06 |
| 338 | Communication between Software Products through Text Files | 04:22 |
| 339 | Software Integration - Explained | 05:26 |
| 340 | Game Plan for this Python, SQL, and Tableau Business Exercise | 04:09 |
| 341 | The Business Task | 02:49 |
| 342 | Introducing the Data Set | 03:19 |
| 343 | Importing the Absenteeism Data in Python | 03:24 |
| 344 | Checking the Content of the Data Set | 05:54 |
| 345 | Introduction to Terms with Multiple Meanings | 03:29 |
| 346 | Using a Statistical Approach towards the Solution to the Exercise | 02:18 |
| 347 | Dropping a Column from a DataFrame in Python | 06:28 |
| 348 | Analyzing the Reasons for Absence | 05:05 |
| 349 | Obtaining Dummies from a Single Feature | 08:38 |
| 350 | More on Dummy Variables: A Statistical Perspective | 01:29 |
| 351 | Classifying the Various Reasons for Absence | 08:36 |
| 352 | Using .concat() in Python | 04:36 |
| 353 | Reordering Columns in a Pandas DataFrame in Python | 01:44 |
| 354 | Creating Checkpoints while Coding in Jupyter | 02:53 |
| 355 | Analyzing the Dates from the Initial Data Set | 07:50 |
| 356 | Extracting the Month Value from the "Date" Column | 07:01 |
| 357 | Extracting the Day of the Week from the "Date" Column | 03:37 |
| 358 | Analyzing Several "Straightforward" Columns for this Exercise | 03:18 |
| 359 | Working on "Education", "Children", and "Pets" | 04:39 |
| 360 | Final Remarks of this Section | 02:00 |
| 361 | Exploring the Problem with a Machine Learning Mindset | 03:21 |
| 362 | Creating the Targets for the Logistic Regression | 06:33 |
| 363 | Selecting the Inputs for the Logistic Regression | 02:43 |
| 364 | Standardizing the Data | 03:27 |
| 365 | Splitting the Data for Training and Testing | 06:14 |
| 366 | Fitting the Model and Assessing its Accuracy | 05:40 |
| 367 | Creating a Summary Table with the Coefficients and Intercept | 05:17 |
| 368 | Interpreting the Coefficients for Our Problem | 06:15 |
| 369 | Standardizing only the Numerical Variables (Creating a Custom Scaler) | 04:13 |
| 370 | Interpreting the Coefficients of the Logistic Regression | 05:12 |
| 371 | Backward Elimination or How to Simplify Your Model | 04:03 |
| 372 | Testing the Model We Created | 04:44 |
| 373 | Saving the Model and Preparing it for Deployment | 04:07 |
| 374 | Preparing the Deployment of the Model through a Module | 04:05 |
| 375 | Deploying the 'absenteeism_module' - Part I | 03:51 |
| 376 | Deploying the 'absenteeism_module' - Part II | 06:25 |
| 377 | Analyzing Age vs Probability in Tableau | 08:50 |
| 378 | Analyzing Reasons vs Probability in Tableau | 07:50 |
| 379 | Analyzing Transportation Expense vs Probability in Tableau | 06:02 |
| 380 | Using the .format() Method | 09:04 |
| 381 | Iterating Over Range Objects | 04:18 |
| 382 | Introduction to Nested For Loops | 06:00 |
| 383 | Triple Nested For Loops | 05:38 |
| 384 | List Comprehensions | 08:31 |
| 385 | Anonymous (Lambda) Functions | 07:01 |
| 386 | Introduction to pandas Series | 08:34 |
| 387 | Working with Methods in Python - Part I | 04:50 |
| 388 | Working with Methods in Python - Part II | 02:33 |
| 389 | Parameters and Arguments in pandas | 04:10 |
| 390 | Using .unique() and .nunique() | 03:50 |
| 391 | Using .sort_values() | 03:59 |
| 392 | Introduction to pandas DataFrames - Part I | 04:42 |
| 393 | Introduction to pandas DataFrames - Part II | 05:06 |
| 394 | pandas DataFrames - Common Attributes | 04:16 |
| 395 | Data Selection in pandas DataFrames | 06:56 |
| 396 | pandas DataFrames - Indexing with .iloc[] | 05:57 |
| 397 | pandas DataFrames - Indexing with .loc[] | 03:53 |
| 398 | An Introduction to Working with Files in Python | 03:47 |
| 399 | File vs File Object, Reading vs Parsing Data | 02:53 |
| 400 | Structured, Semi-Structured and Unstructured Data | 03:11 |
| 401 | Text Files and Data Connectivity | 03:07 |
| 402 | Importing Data in Python - Principles | 04:51 |
| 403 | Plain Text Files, Flat Files and More | 04:34 |
| 404 | Text Files of Fixed Width | 01:27 |
| 405 | Common Naming Conventions | 03:50 |
| 406 | Importing Text Files - open() | 09:01 |
| 407 | Importing Text Files - with open() | 04:54 |
| 408 | Importing *.csv Files - Part I | 05:36 |
| 409 | Importing *.csv Files - Part II | 02:38 |
| 410 | Importing *.csv Files - Part III | 05:58 |
| 411 | Importing Data with index_col | 02:36 |
| 412 | Importing Data with .loadtxt() and .genfromtxt() | 10:45 |
| 413 | Importing Data - Partial Cleaning While Importing Data | 07:22 |
| 414 | Importing Data from *.json Files | 05:16 |
| 415 | An Introduction to Working with Excel Files in Python | 03:41 |
| 416 | Working with Excel (*.xlsx) Data | 01:56 |
| 417 | Importing Data in Python - an Important Exercise | 05:45 |
| 418 | Importing Data with the .squeeze() Method | 03:24 |
| 419 | Importing Files in Jupyter | 03:11 |
| 420 | Saving Your Data with pandas | 03:12 |
| 421 | Saving Your Data with NumPy - Part I - *.npy | 05:24 |
| 422 | Saving Your Data with NumPy - Part II - *.npz | 05:13 |
| 423 | Saving Your Data with NumPy - Part III - *.csv | 03:59 |
| 424 | Working with Text Files in Python - Conclusion | 00:43 |
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