Machine Learning for Aspiring Data Scientists: Zero to Hero

Machine Learning for Aspiring Data Scientists: Zero to Hero

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Overview of Sleep Therapy

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:

This sought-after Sleep Therapy course is your key to a successful and lucrative career. Don’t miss out on this transformative opportunity. Enrol now and take your professional life to the next level!

Sleep Therapy 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

Exam & Retakes

It is to inform our learners that the initial exam for this Sleep Therapy course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.

Certification of Sleep Therapy

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
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
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

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