Machine Learning For Aspiring Data Scientists: Zero To Hero

Overview

Our comprehensive program is meticulously crafted to equip you with the essential skills and knowledge required to thrive in your chosen field. Developed by seasoned professionals with years of industry experience, this course is ideal for those seeking to kickstart their careers or enhance their existing skill set.

Featuring an engaging audio-visual presentation and easily digestible modules, our program facilitates a self-paced learning experience. Our dedicated online support team is available on weekdays to provide assistance throughout your journey.

Key Learning Outcomes

Grasp the fundamentals and their practical applications.
Cultivate the necessary skills for success in your field.
Apply newfound knowledge to real-world scenarios.
Develop effective solutions for relevant topics.
Elevate your employability and career prospects.

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 #Data leakage
Module 63: The golden rule
Module 64: Helpful trick (feature importance)
Module 65: Real example of data leakage ()
Module 66: Real example of data leakage ()
Module 67: Another (funny) example of data leakage
Module 68: Mistake #Random split of dependent data
Module 69: Another example (insurance data)
Module 70: Mistake #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 #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

Designed to give you a competitive edge in the job market, this course offers lifetime access to materials and the flexibility to learn at your own pace, from the comfort of your home.

Why Choose Us?

Learn at your own pace with 24/7 online access to course materials.
Benefit from full tutor support available Monday through Friday.
Acquire essential skills in the convenience of your home through informative video modules.
Enjoy 24/7 assistance and advice via email and live chat.
Study on your preferred device – computer, tablet, or mobile.
Gain a thorough understanding of the course content.
Improve professional skills and earning potential upon completion.
Access lifetime course materials and expert guidance.
Enjoy the convenience of online learning with flexible schedules.

Why Enroll in This Course?

Our program provides a comprehensive introduction to the subject matter, laying a solid foundation for further study. It empowers students to acquire knowledge and skills applicable to both their professional and personal lives.

Assessment

The course incorporates quizzes to evaluate your understanding and retention of the material. These quizzes pinpoint areas for further practice, allowing you to review course materials as needed. Successfully passing the final quiz qualifies you for a certificate of achievement.

Requirements

There are no formal requirements for this course, it is open to anyone who is interested in learning the material.

Career Path

Our course is meticulously designed to equip you for success in your chosen field. Upon completion, you’ll have the qualifications to pursue diverse career opportunities across various industries.