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• Inlcudes:
• Exam Fee: No
• Labs: Yes
• Test Prep: Yes
• Mentor Support: Yes

Subscription Plan: This plan provides not only access to our extensive course catalog but also dedicated mentorship for content mastery and effective career planning. Please note, course completion is required before starting a new one, ensuring a solid grasp of material. The plan requires an initial R2,500 deposit, reflecting our commitment to quality education. You may cancel anytime with a month's notice. Start your learning journey today!

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Machine Learning With Python

Discover the world of Machine Learning with Python through comprehensive course. This course is designed to equip learners with the fundamental concepts and practical skills needed to excel in the field of machine learning. Whether you're an aspiring data scientist, software engineer, or business analyst, this course caters to a diverse audience eager to delve into the realm of machine learning.

Course Objectives

Lesson 1: 

• Scope, Terminology, Prediction, and Data
• Putting the Machine in Machine Learning
• Examples of Learning Systems
• Evaluating Learning Systems
• A Process for Building Learning Systems
• Assumptions and Reality of Learning
• End-of-Lesson Material

Lesson 2: Some Technical Background

• About Our Setup
• The Need for Mathematical Language
• Our Software for Tackling Machine Learning
• Probability
• Linear Combinations, Weighted Sums, and Dot Products
• A Geometric View: Points in Space
• Notation and the Plus-One Trick
• Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
• NumPy versus “All the Maths”
• Floating-Point Issues
• EOC

Lesson 3: Predicting Categories: Getting Started with Classification

• Classification Tasks
• A Simple Classification Dataset
• Training and Testing: Don’t Teach to the Test
• Evaluation: Grading the Exam
• Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
• Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
• Simplistic Evaluation of Classifiers
• EOC

Lesson 4: Predicting Numerical Values: Getting Started with Regression

• A Simple Regression Dataset
• Nearest-Neighbors Regression and Summary Statistics
• Linear Regression and Errors
• Optimization: Picking the Best Answer
• Simple Evaluation and Comparison of Regressors
• EOC

Lesson 5: Evaluating and Comparing Learners

• Evaluation and Why Less Is More
• Terminology for Learning Phases
• Major Tom, There’s Something Wrong: Overfitting and Underfitting
• From Errors to Costs
• (Re)Sampling: Making More from Less
• Break-It-Down: Deconstructing Error into Bias and Variance
• Graphical Evaluation and Comparison
• Comparing Learners with Cross-Validation
• EOC

Lesson 6: Evaluating Classifiers

• Baseline Classifiers
• Beyond Accuracy: Metrics for Classification
• ROC Curves
• Another Take on Multiclass: One-versus-One
• Precision-Recall Curves
• Cumulative Response and Lift Curves
• More Sophisticated Evaluation of Classifiers: Take Two
• EOC

Lesson 7: Evaluating Regressors

• Baseline Regressors
• Additional Measures for Regression
• Residual Plots
• A First Look at Standardization
• Evaluating Regressors in a More Sophisticated Way: Take Two
• EOC

Lesson 8: More Classification Methods

• Revisiting Classification
• Decision Trees
• Support Vector Classifiers
• Logistic Regression
• Discriminant Analysis
• Assumptions, Biases, and Classifiers
• Comparison of Classifiers: Take Three
• EOC

Lesson 9: More Regression Methods

• Linear Regression in the Penalty Box: Regularization
• Support Vector Regression
• Piecewise Constant Regression
• Regression Trees
• Comparison of Regressors: Take Three
• EOC

Lesson 10: Manual Feature Engineering: Manipulating Data for Fun and Profit

• Feature Engineering Terminology and Motivation
• Feature Selection and Data Reduction: Taking out the Trash
• Feature Scaling
• Discretization
• Categorical Coding
• Relationships and Interactions
• Target Manipulations
• EOC

Lesson 11: Tuning Hyperparameters and Pipelines

• Models, Parameters, Hyperparameters
• Tuning Hyperparameters
• Down the Recursive Rabbit Hole: Nested Cross-Validation
• Pipelines
• Pipelines and Tuning Together
• EOC

Lesson 12: Combining Learners

• Ensembles
• Voting Ensembles
• Bagging and Random Forests
• Boosting
• Comparing the Tree-Ensemble Methods
• EOC

Lesson 13: Models That Engineer Features for Us

• Feature Selection
• Feature Construction with Kernels
• Principal Components Analysis: An Unsupervised Technique
• EOC

Lesson 14: Feature Engineering for Domains: Domain-Specific Learning

• Working with Text
• Clustering
• Working with Images
• EOC

Lesson 15: Connections, Extensions, and Further Directions

• Optimization
• Linear Regression from Raw Materials
• Building Logistic Regression from Raw Materials
• SVM from Raw Materials
• Neural Networks
• Probabilistic Graphical Models
• EOC

Hands-on LAB Activities (Performance Labs)

Some Technical Background

• Plotting a Probability Distribution Graph
• Using the zip Function
• Calculating the Sum of Squares
• Plotting a Line Graph
• Plotting a 3D Graph
• Plotting a Polynomial Graph
• Using the numpy.dot() Method

Predicting Categories: Getting Started with Classification

• Displaying Histograms

Predicting Numerical Values: Getting Started with Regression

• Defining an Outlier
• Calculating the Median Value
• Estimating the Multiple Regression Equation

Evaluating and Comparing Learners

• Constructing a Swarm Plot
• Using the describe() Method
• Viewing Variance

Evaluating Classifiers

• Creating a Confusion Matrix
• Creating an ROC Curve
• Recreating an ROC Curve
• Creating a Trendline Graph

Evaluating Regressors

• Viewing the Standard Deviation
• Constructing a Scatterplot
• Evaluating the Prediction Error Rates

More Classification Methods

• Evaluating a Logistic Model
• Creating a Covariance Matrix
• Using the load_digits() Function

More Regression Methods

• Illustrating a Less Consistent Relationship
• Illustrating a Piecewise Constant Regression

Manual Feature Engineering: Manipulating Data for Fun and Profit

• Manipulating the Target
• Manipulating the Input Space
• Combining Learners
• Calculating the Mean Value

Models That Engineer Features for Us

• Displaying a Correlation Matrix
• Creating a Nonlinear Model
• Performing a Principal Component Analysis
• Using the Manifold Method

Feature Engineering for Domains: Domain-Specific Learning

• Encoding Text

Connections, Extensions, and Further Directions

• Building an Estimated Simple Linear Regression Equation

Benefits of the course:

• Practical Skill Development: Master machine learning techniques through hands-on labs and projects, gaining real-world experience.

• Career Advancement: With machine learning skills in your toolkit, you'll stand out in competitive job markets and open doors to data-centric roles.

• Industry-Relevant Knowledge: Stay up-to-date with the latest advancements in machine learning and apply them to real-world problems.

Prerequisites

A basic understanding of programming concepts and Python is beneficial, but the course is designed to accommodate learners with varying levels of experience.