Machine Learning for Aspiring Data Scientists: Zero to Hero
Machine Learning for Aspiring Data Scientists: Zero to Hero
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Overview
Empower your career journey with our in-demand course: Machine Learning for Aspiring Data Scientists: Zero to Hero
Boost your proficiency and propel your career forward with our meticulously crafted course, designed to be your ultimate guide to professional development. Our super-accessible modules break down complex topics into bite-sized, easy-to-understand lessons, filling your knowledge gaps and equipping you with real-world, practical skills.
Seeking career advancement and application of your skills? You’ve found the right place. This Machine Learning for Aspiring Data Scientists: Zero to Hero is your exclusive passport to unlocking your full potential.
Enroll today and enjoy:
- 100% online flexibility: Learn at your own pace, from anywhere in the world.
- Expert support: We're by your side throughout your journey, providing guidance and assistance.
- Comprehensive curriculum: Dive deep into relevant topics aligned with industry standards.
- Practical focus: Apply your learnings to real-world scenarios and situations.
This sought-after course is your key to a successful and lucrative career. Don’t miss out on this transformative opportunity. Enroll now and take your professional life to the next level!
Course design
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
- Video lessons
- Online study materials
Exam & Retakes
It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.
Certification
Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).
Course Curriculum
| Machine Learning Models | |||
| Modeling an epidemic | 00:08:00 | ||
| The machine learning recipe | 00:06:00 | ||
| The components of a machine learning model | 00:02:00 | ||
| Why model? | 00:03:00 | ||
| On assumptions and can we get rid of them? | 00:09:00 | ||
| The case of AlphaZero | 00:11:00 | ||
| Overfitting/underfitting/bias/variance | 00:11:00 | ||
| Why use machine learning | 00:05:00 | ||
| Linear regression | |||
| The InsureMe challenge | 00:06:00 | ||
| Supervised learning | 00:05:00 | ||
| Linear assumption | 00:03:00 | ||
| Linear regression template | 00:07:00 | ||
| Non-linear vs proportional vs linear | 00:05:00 | ||
| Linear regression template revisited | 00:04:00 | ||
| Loss function | 00:08:00 | ||
| Training algorithm | 00:08:00 | ||
| Code time | 00:15:00 | ||
| R squared | 00:06:00 | ||
| Why use a linear model? | 00:04:00 | ||
| Scaling and Pipelines | |||
| Introduction to scaling | 00:06:00 | ||
| Min-max scaling | 00:03:00 | ||
| Code time (min-max scaling) | 00:09:00 | ||
| The problem with min-max scaling | 00:03:00 | ||
| What’s your IQ? | 00:11:00 | ||
| Standard scaling | 00:04:00 | ||
| Code time (standard scaling) | 00:02:00 | ||
| Model before and after scaling | 00:05:00 | ||
| Inference time | 00:07:00 | ||
| Pipelines | 00:03:00 | ||
| Code time (pipelines) | 00:05:00 | ||
| Regularization | |||
| Spurious correlations | 00:04:00 | ||
| L2 regularization | 00:10:00 | ||
| Code time (L2 regularization) | 00:05:00 | ||
| L2 results | 00:02:00 | ||
| L1 regularization | 00:06:00 | ||
| Code time (L1 regularization) | 00:04:00 | ||
| L1 results | 00:02:00 | ||
| Why does L1 encourage zeros? | 00:09:00 | ||
| L1 vs L2: Which one is best? | 00:01:00 | ||
| Validation | |||
| Introduction to validation | 00:02:00 | ||
| Why not evaluate model on training data | 00:06:00 | ||
| The validation set | 00:05:00 | ||
| Code time (validation set) | 00:08:00 | ||
| Error curves | 00:06:00 | ||
| Model selection | 00:06:00 | ||
| The problem with model selection | 00:06:00 | ||
| Tainted validation set | 00:05:00 | ||
| Monkeys with typewriters | 00:03:00 | ||
| My own validation epic fail | 00:07:00 | ||
| The test set | 00:06:00 | ||
| What if the model doesn’t pass the test? | 00:05:00 | ||
| How not to be fooled by randomness | 00:02:00 | ||
| Cross-validation | 00:04:00 | ||
| Code time (cross validation) | 00:07:00 | ||
| Cross-validation results summary | 00:02:00 | ||
| AutoML | 00:05:00 | ||
| Is AutoML a good idea? | 00:05:00 | ||
| Red flags: Don’t do this! | 00:07:00 | ||
| Red flags summary and what to do instead | 00:05:00 | ||
| Your job as a data scientist | 00:03:00 | ||
| Common Mistakes | |||
| Intro and recap | 00:02:00 | ||
| Mistake #1: Data leakage | 00:05:00 | ||
| The golden rule | 00:04:00 | ||
| Helpful trick (feature importance) | 00:02:00 | ||
| Real example of data leakage (part 1) | 00:05:00 | ||
| Real example of data leakage (part 2) | 00:05:00 | ||
| Another (funny) example of data leakage | 00:02:00 | ||
| Mistake #2: Random split of dependent data | 00:05:00 | ||
| Another example (insurance data) | 00:05:00 | ||
| Mistake #3: Look-Ahead Bias | 00:06:00 | ||
| Example solutions to Look-Ahead Bias | 00:02:00 | ||
| Consequences of Look-Ahead Bias | 00:02:00 | ||
| How to split data to avoid Look-Ahead Bias | 00:03:00 | ||
| Cross-validation with temporally related data | 00:03:00 | ||
| Mistake #4: Building model for one thing, using it for something else | 00:04:00 | ||
| Sketchy rationale | 00:06:00 | ||
| Why this matters for your career and job search | 00:04:00 | ||
| Classification - Part 1: Logistic Model | |||
| Classifying images of handwritten digits | 00:07:00 | ||
| Why the usual regression doesn’t work | 00:04:00 | ||
| Machine learning recipe recap | 00:02:00 | ||
| Logistic model template (binary) | 00:13:00 | ||
| Decision function and boundary (binary) | 00:05:00 | ||
| Logistic model template (multiclass) | 00:14:00 | ||
| Decision function and boundary (multi-class) | 00:01:00 | ||
| Summary: binary vs multiclass | 00:01:00 | ||
| Code time! | 00:20:00 | ||
| Why the logistic model is often called logistic regression | 00:05:00 | ||
| One vs Rest, One vs One | 00:05:00 | ||
| Classification - Part 2: Maximum Likelihood Estimation | |||
| Where we’re at | 00:02:00 | ||
| Brier score and why it doesn’t work | 00:06:00 | ||
| The likelihood function | 00:11:00 | ||
| Optimization task and numerical stability | 00:03:00 | ||
| Let’s improve the loss function | 00:09:00 | ||
| Loss value examples | 00:05:00 | ||
| Adding regularization | 00:02:00 | ||
| Binary cross-entropy loss | 00:03:00 | ||
| Classification - Part 3: Gradient Descent | |||
| Recap | 00:03:00 | ||
| No closed-form solution | 00:02:00 | ||
| Naive algorithm | 00:04:00 | ||
| Fog analogy | 00:05:00 | ||
| Gradient descent overview | 00:03:00 | ||
| The gradient | 00:06:00 | ||
| Numerical calculation | 00:02:00 | ||
| Parameter update | 00:04:00 | ||
| Convergence | 00:02:00 | ||
| Analytical solution | 00:02:00 | ||
| [Optional] Interpreting analytical solution | 00:05:00 | ||
| Gradient descent conditions | 00:03:00 | ||
| Beyond vanilla gradient descent | 00:03:00 | ||
| Code time | 00:07:00 | ||
| Reading the documentation | 00:11:00 | ||
| Classification metrics and class imbalance | |||
| Binary classification and class imbalance | 00:06:00 | ||
| Assessing performance | 00:04:00 | ||
| Accuracy | 00:07:00 | ||
| Accuracy with different class importance | 00:04:00 | ||
| Precision and Recall | 00:07:00 | ||
| Sensitivity and Specificity | 00:03:00 | ||
| F-measure and other combined metrics | 00:05:00 | ||
| ROC curve | 00:07:00 | ||
| Area under the ROC curve | 00:06:00 | ||
| Custom metric (important stuff!) | 00:06:00 | ||
| Other custom metrics | 00:03:00 | ||
| Bad data science process | 00:04:00 | ||
| Data rebalancing (avoid doing this!) | 00:06:00 | ||
| Stratified split | 00:03:00 | ||
| Neural Networks | |||
| The inverted MNIST dataset | 00:04:00 | ||
| The problem with linear models | 00:05:00 | ||
| Neurons | 00:03:00 | ||
| Multi-layer perceptron (MLP) for binary classification | 00:05:00 | ||
| MLP for regression | 00:02:00 | ||
| MLP for multi-class classification | 00:01:00 | ||
| Hidden layers | 00:01:00 | ||
| Activation functions | 00:03:00 | ||
| Decision boundary | 00:02:00 | ||
| Loss function | 00:03:00 | ||
| Intro to neural network training | 00:03:00 | ||
| Parameter initialization | 00:03:00 | ||
| Saturation | 00:05:00 | ||
| Non-convexity | 00:04:00 | ||
| Stochastic gradient descent (SGD) | 00:05:00 | ||
| More on SGD | 00:07:00 | ||
| Code time! | 00:13:00 | ||
| Backpropagation | 00:11:00 | ||
| The problem with MLPs | 00:04:00 | ||
| Deep learning | 00:09:00 | ||
| Tree-Based Models | |||
| Decision trees | 00:04:00 | ||
| Building decision trees | 00:09:00 | ||
| Stopping tree growth | 00:03:00 | ||
| Pros and cons of decision trees | 00:08:00 | ||
| Decision trees for classification | 00:07:00 | ||
| Decision boundary | 00:01:00 | ||
| Bagging | 00:04:00 | ||
| Random forests | 00:06:00 | ||
| Gradient-boosted trees for regression | 00:07:00 | ||
| Gradient-boosted trees for classification [optional] | 00:04:00 | ||
| How to use gradient-boosted trees | 00:03:00 | ||
| K-nn and SVM | |||
| Nearest neighbor classification | 00:03:00 | ||
| K nearest neighbors | 00:03:00 | ||
| Disadvantages of k-NN | 00:04:00 | ||
| Recommendation systems (collaborative filtering) | 00:03:00 | ||
| Introduction to Support Vector Machines (SVMs) | 00:05:00 | ||
| Maximum margin | 00:02:00 | ||
| Soft margin | 00:02:00 | ||
| SVM vs Logistic Model (support vectors) | 00:03:00 | ||
| Alternative SVM formulation | 00:06:00 | ||
| Dot product | 00:02:00 | ||
| Non-linearly separable data | 00:03:00 | ||
| Kernel trick (polynomial) | 00:10:00 | ||
| RBF kernel | 00:02:00 | ||
| SVM remarks | 00:06:00 | ||
| Unsupervised Learning | |||
| Intro to unsupervised learning | 00:01:00 | ||
| Clustering | 00:03:00 | ||
| K-means clustering | 00:10:00 | ||
| K-means application example | 00:03:00 | ||
| Elbow method | 00:02:00 | ||
| Clustering remarks | 00:07:00 | ||
| Intro to dimensionality reduction | 00:05:00 | ||
| PCA (principal component analysis) | 00:08:00 | ||
| PCA remarks | 00:03:00 | ||
| Code time (PCA) | 00:13:00 | ||
| Feature Engineering | |||
| Missing data | 00:02:00 | ||
| Imputation | 00:04:00 | ||
| Imputer within pipeline | 00:04:00 | ||
| One-Hot encoding | 00:05:00 | ||
| Ordinal encoding | 00:03:00 | ||
| How to combine pipelines | 00:04:00 | ||
| Code sample | 00:08:00 | ||
| Feature Engineering | 00:07:00 | ||
| Features for Natural Language Processing (NLP) | 00:11:00 | ||
| Anatomy of a Data Science Project | 00:01:00 | ||
With a team of industry professionals producing and delivering our course content, you can be sure the skills and knowledge you learn are applicable to your career aspirations, whether that’s in management, administration or beyond.
