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Machine Learning for Aspiring Data Scientists
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Register on the today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study …
Register on the Machine Learning for Aspiring Data Scientists today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.
The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.
Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion.
The Machine Learning for Aspiring Data Scientists course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones.
The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!
What You Get With The Machine Learning for Aspiring Data Scientists course
- Receive a digital certificate upon successful completion of the course
- Get taught by experienced, professional instructors
- Study at a time and pace that suits your learning style
- Get instant feedback on assessments
- 24/7 help and advice via email or live chat
- Get full tutor support on weekdays (Monday to Friday)
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
Certification
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.
Who Is This Course For:
The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.
Requirements:
The online training is open to all students and has no formal entry requirements. To study the Machine Learning for Aspiring Data Scientists course, all you need is a passion for learning, A good understanding of English, numeracy, and IT skills. You must also be over the age of 16.
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 | 00:08:00 | ||
| 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 | 00:03:00 | ||
| Analytical solution | 00:03:00 | ||
| [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 | ||
| Order Your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course.
You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.
If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.