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Whether you’re looking to enter a new field, upskill in your current job, or give your resume a boost, our CPD Accredited Machine Learning for Aspiring Data Scientists: Zero to Hero Online Training Program is the key to unlocking new career opportunities.

This engaging and flexible Machine Learning for Aspiring Data Scientists: Zero to Hero online course is designed to provide you with the professional qualification and hands-on expertise that employers value. No complicated textbooks or stressful schedules—just practical, real-world knowledge you can use right away. Finish with confidence and earn the CPD-accredited certificate that shows you’re ready to succeed.

Course Curriculum

• Module 01: Modeling an epidemic
• Module 02: The machine learning recipe
• Module 03: The components of a machine learning model
• Module 04: Why model?
• Module 05: On assumptions and can we get rid of them?
• Module 06: The case of AlphaZero
• Module 07: Overfitting/underfitting/bias/variance
• Module 08: Why use machine learning
• Module 09: The InsureMe challenge
• Module 10: Supervised learning
• Module 11: Linear assumption
• Module 12: Linear regression template
• Module 13: Non-linear vs proportional vs linear
• Module 14: Linear regression template revisited
• Module 15: Loss function
• Module 16: Training algorithm
• Module 17: Code time
• Module 18: R squared
• Module 19: Why use a linear model?
• Module 20: Introduction to scaling
• Module 21: Min-max scaling
• Module 22: Code time (min-max scaling)
• Module 23: The problem with min-max scaling
• Module 24: What’s your IQ?
• Module 25: Standard scaling
• Module 26: Code time (standard scaling)
• Module 27: Model before and after scaling
• Module 28: Inference time
• Module 29: Pipelines
• Module 30: Code time (pipelines)
• Module 31: Spurious correlations
• Module 32: L2 regularization
• Module 33: Code time (L2 regularization)
• Module 34: L2 results
• Module 35: L1 regularization
• Module 36: Code time (L1 regularization)
• Module 37: L1 results
• Module 38: Why does L1 encourage zeros?
• Module 39: L1 vs L2: Which one is best?
• Module 40: Introduction to validation
• Module 41: Why not evaluate model on training data
• Module 42: The validation set
• Module 43: Code time (validation set)
• Module 44: Error curves
• Module 45: Model selection
• Module 46: The problem with model selection
• Module 47: Tainted validation set
• Module 48: Monkeys with typewriters
• Module 49: My own validation epic fail
• Module 50: The test set
• Module 51: What if the model doesn’t pass the test?
• Module 52: How not to be fooled by randomness
• Module 53: Cross-validation
• Module 54: Code time (cross validation)
• Module 55: Cross-validation results summary
• Module 56: AutoML
• Module 57: Is AutoML a good idea?
• Module 58: Red flags: Don’t do this!
• Module 59: Red flags summary and what to do instead
• Module 60: Your job as a data scientist
• Module 61: Intro and recap
• Module 62: Mistake #1: Data leakage
• Module 63: The golden rule
• Module 64: Helpful trick (feature importance)
• Module 65: Real example of data leakage (part 1)
• Module 66: Real example of data leakage (part 2)
• Module 67: Another (funny) example of data leakage
• Module 68: Mistake #2: Random split of dependent data
• Module 69: Another example (insurance data)
• Module 70: Mistake #3: Look-Ahead Bias
• Module 71: Example solutions to Look-Ahead Bias
• Module 72: Consequences of Look-Ahead Bias
• Module 73: How to split data to avoid Look-Ahead Bias
• Module 74: Cross-validation with temporally related data
• Module 75: Mistake #4: Building model for one thing, using it for something else
• Module 76: Sketchy rationale
• Module 77: Why this matters for your career and job search
• Module 78: Classifying images of handwritten digits
• Module 79: Why the usual regression doesn’t work
• Module 80: Machine learning recipe recap
• Module 81: Logistic model template (binary)
• Module 82: Decision function and boundary (binary)
• Module 83: Logistic model template (multiclass)
• Module 84: Decision function and boundary (multi-class)
• Module 85: Summary: binary vs multiclass
• Module 86: Code time!
• Module 87: Why the logistic model is often called logistic regression
• Module 88: One vs Rest, One vs One
• Module 89: Where we’re at
• Module 90: Brier score and why it doesn’t work
• Module 91: The likelihood function
• Module 92: Optimization task and numerical stability
• Module 93: Let’s improve the loss function
• Module 94: Loss value examples
• Module 95: Adding regularization
• Module 96: Binary cross-entropy loss
• Module 97: Recap
• Module 98: No closed-form solution
• Module 99: Naive algorithm
• Module 100: Fog analogy
• Module 101: Gradient descent overview
• Module 102: The gradient
• Module 103: Numerical calculation
• Module 104: Parameter update
• Module 105: Convergence
• Module 106: Analytical solution
• Module 107: [Optional] Interpreting analytical solution
• Module 108: Gradient descent conditions
• Module 109: Beyond vanilla gradient descent
• Module 110: Reading the documentation
• Module 111: Binary classification and class imbalance
• Module 112: Assessing performance
• Module 113: Accuracy
• Module 114: Accuracy with different class importance
• Module 115: Precision and Recall
• Module 116: Sensitivity and Specificity
• Module 117: F-measure and other combined metrics
• Module 118: ROC curve
• Module 119: Area under the ROC curve
• Module 120: Custom metric (important stuff!)
• Module 121: Other custom metrics
• Module 122: Bad data science process
• Module 123: Data rebalancing (avoid doing this!)
• Module 124: Stratified split
• Module 125: The inverted MNIST dataset
• Module 126: The problem with linear models
• Module 127: Neurons
• Module 128: Multi-layer perceptron (MLP) for binary classification
• Module 129: MLP for regression
• Module 130: MLP for multi-class classification
• Module 131: Hidden layers
• Module 132: Activation functions
• Module 133: Decision boundary
• Module 134: Intro to neural network training
• Module 135: Parameter initialization
• Module 136: Saturation
• Module 137: Non-convexity
• Module 138: Stochastic gradient descent (SGD)
• Module 139: More on SGD
• Module 140: Backpropagation
• Module 141: The problem with MLPs
• Module 142: Deep learning
• Module 143: Decision trees
• Module 144: Building decision trees
• Module 145: Stopping tree growth
• Module 146: Pros and cons of decision trees
• Module 147: Decision trees for classification
• Module 148: Bagging
• Module 149: Random forests
• Module 150: Gradient-boosted trees for regression
• Module 151: Gradient-boosted trees for classification [optional]
• Module 152: How to use gradient-boosted trees
• Module 153: Nearest neighbor classification
• Module 154: K nearest neighbors
• Module 155: Disadvantages of k-NN
• Module 156: Recommendation systems (collaborative filtering)
• Module 157: Introduction to Support Vector Machines (SVMs)
• Module 158: Maximum margin
• Module 159: Soft margin
• Module 160: SVM vs Logistic Model (support vectors)
• Module 161: Alternative SVM formulation
• Module 162: Dot product
• Module 163: Non-linearly separable data
• Module 164: Kernel trick (polynomial)
• Module 165: RBF kernel
• Module 166: SVM remarks
• Module 167: Intro to unsupervised learning
• Module 168: Clustering
• Module 169: K-means clustering
• Module 170: K-means application example
• Module 171: Elbow method
• Module 172: Clustering remarks
• Module 173: Intro to dimensionality reduction
• Module 174: PCA (principal component analysis)
• Module 175: PCA remarks
• Module 176: Code time (PCA)
• Module 177: Missing data
• Module 178: Imputation
• Module 179: Imputer within pipeline
• Module 180: One-Hot encoding
• Module 181: Ordinal encoding
• Module 182: How to combine pipelines
• Module 183: Code sample
• Module 184: Feature Engineering
• Module 185: Features for Natural Language Processing (NLP)
• Module 186: Anatomy of a Data Science Project

What You Will Gain from This Machine Learning for Aspiring Data Scientists: Zero to Hero Training

• Understand core principles and concepts of the Machine Learning for Aspiring Data Scientists: Zero to Hero.
• Apply practical skills in real-world workplace scenarios.
• Chance to earn a recognized certification after completing the Machine Learning for Aspiring Data Scientists: Zero to Hero
• Develop industry-specific skills to advance professional growth.
• Gain confidence to handle tasks and challenges effectively.
• Broaden knowledge in the field of Machine Learning for Aspiring Data Scientists: Zero to Hero for well-rounded expertise.
• Strengthen critical thinking and problem-solving abilities and much more…

Why Choose Centre of Professional for Your Learning Journey?

• Eligible to order a CPD-accredited certificate upon successful completion.
• Career support and guidance available to help you navigate your next steps.
• High-quality, engaging video lessons available anytime, anywhere.
• Lifetime Access to all Machine Learning for Aspiring Data Scientists: Zero to Hero materials.
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Who Can Benefit From This Machine Learning for Aspiring Data Scientists: Zero to Hero?

This Machine Learning for Aspiring Data Scientists: Zero to Hero is perfect for:

• Career starters or job seekers looking to enhance their qualifications.
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Requirements of Machine Learning for Aspiring Data Scientists: Zero to Hero Training

No prior experience needed to enrol in this Machine Learning for Aspiring Data Scientists: Zero to Hero—just a passion for learning and personal growth!

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