ML-PYTHON.AP1
Machine Learning with Python
Calling all curious minds! Start your Machine Learning with Python coding journey today, and become an expert engineer.
- Practice in 35 Hands-On Labs — nothing to install
- 16 Interactive Lessons and 105 topics mapped to the official exam objectives
- 129 Practice Test Questions
Intermediate Self-paced · 1 year access 4.2/5 (87 Reviews)
35 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
16Interactive Lessons
105Topics
35LiveLab
129Practice Test Questions
100Flashcards
100Glossary of terms
01 / Skills you'll get
What you will be able to do
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No credit card required
Learn Machine Learning with Python, a comprehensive training manual that teaches the fundamentals of coding with Python. Whether you want to improve your coding skills or you want an upgrade at your workplace, this is the ideal start. In this course, you’ll master the processes, patterns, and strategies of this user-friendly programming language. This Python ML course covers supervised learning paradigms, like classical algorithms, and regression techniques to evaluate performance metrics. Besides this, you’ll also learn feature engineering for converting raw data into meaningful features. Furthermore, you’ll also leverage the Python scikit-learn library along with other powerful tools. Practice on our Labs to solidify your understanding as you explore object-oriented programming, modules, error handling, and even file operations. By the end of this course, you'll be confidently writing Python ML scripts and resolving coding issues.
- Understand the fundamentals of supervised machine learning algorithms and their classification
- Evaluating performance metrics for assessing the efficacy of your models
- Using engineer features to convert raw data into meaningful ML algorithms
- Managing system performance by creating robust pipelines
- Apply ML to various data types
- Leverage Python scikit-learn library and other tools
- Use of advanced techniques like neural networks and graphical models
Course Highlights
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16 Structured Lessons Comprehensive coverage of core course objectives
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35 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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129 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
16 Interactive Lessons · 105 topics01 Let’s Discuss Learning 8 topics +
- Welcome
- 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
02 Some Technical Background 11 topics · 7 LiveLab +
- 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
7 LiveLab in this lesson — see the labs panel →
03 Predicting Categories: Getting Started with Classification 8 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
04 Predicting Numerical Values: Getting Started with Regression 6 topics · 3 LiveLab +
- 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
3 LiveLab in this lesson — see the labs panel →
05 Evaluating and Comparing Learners 9 topics · 3 LiveLab +
- 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
3 LiveLab in this lesson — see the labs panel →
06 Evaluating Classifiers 8 topics · 4 LiveLab +
- 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
4 LiveLab in this lesson — see the labs panel →
07 Evaluating Regressors 6 topics · 3 LiveLab +
- Baseline Regressors
- Additional Measures for Regression
- Residual Plots
- A First Look at Standardization
- Evaluating Regressors in a More Sophisticated Way: Take Two
- EOC
3 LiveLab in this lesson — see the labs panel →
08 More Classification Methods 8 topics · 3 LiveLab +
- Revisiting Classification
- Decision Trees
- Support Vector Classifiers
- Logistic Regression
- Discriminant Analysis
- Assumptions, Biases, and Classifiers
- Comparison of Classifiers: Take Three
- EOC
3 LiveLab in this lesson — see the labs panel →
09 More Regression Methods 6 topics · 2 LiveLab +
- Linear Regression in the Penalty Box: Regularization
- Support Vector Regression
- Piecewise Constant Regression
- Regression Trees
- Comparison of Regressors: Take Three
- EOC
2 LiveLab in this lesson — see the labs panel →
10 Manual Feature Engineering: Manipulating Data for Fun and Profit 8 topics · 2 LiveLab +
- 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
2 LiveLab in this lesson — see the labs panel →
11 Tuning Hyperparameters and Pipelines 6 topics +
- Models, Parameters, Hyperparameters
- Tuning Hyperparameters
- Down the Recursive Rabbit Hole: Nested Cross-Validation
- Pipelines
- Pipelines and Tuning Together
- EOC
12 Combining Learners 6 topics · 1 LiveLab +
- Ensembles
- Voting Ensembles
- Bagging and Random Forests
- Boosting
- Comparing the Tree-Ensemble Methods
- EOC
1 LiveLab in this lesson — see the labs panel →
13 Models That Engineer Features for Us 4 topics · 4 LiveLab +
- Feature Selection
- Feature Construction with Kernels
- Principal Components Analysis: An Unsupervised Technique
- EOC
4 LiveLab in this lesson — see the labs panel →
14 Feature Engineering for Domains: Domain-Specific Learning 4 topics · 1 LiveLab +
- Working with Text
- Clustering
- Working with Images
- EOC
1 LiveLab in this lesson — see the labs panel →
15 Connections, Extensions, and Further Directions 7 topics · 1 LiveLab +
- Optimization
- Linear Regression from Raw Materials
- Building Logistic Regression from Raw Materials
- SVM from Raw Materials
- Neural Networks
- Probabilistic Graphical Models
- EOC
1 LiveLab in this lesson — see the labs panel →
16 Appendix A: mlwpy.py Listing +
Hands-On Labs Our edge
35 LiveLabs- 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
- Displaying Histograms
- Defining an Outlier
- Calculating the Median Value
- Estimating the Multiple Regression Equation
- Constructing a Swarm Plot
- Using the describe() Method
- Viewing Variance
- Creating a Confusion Matrix
- Creating an ROC Curve
- Recreating an ROC Curve
- Creating a Trendline Graph
- Viewing the Standard Deviation
- Constructing a Scatterplot
- Evaluating the Prediction Error Rates
- Evaluating a Logistic Model
- Creating a Covariance Matrix
- Using the load_digits() Function
- Illustrating a Less Consistent Relationship
- Illustrating a Piecewise Constant Regression
- Manipulating the Target
- Manipulating the Input Space
- Calculating the Mean Value
- Displaying a Correlation Matrix
- Creating a Nonlinear Model
- Performing a Principal Component Analysis
- Using the Manifold Method
- Encoding Text
- Building an Estimated Simple Linear Regression Equation
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What prior knowledge is required to take this Python ML course? +
This is a beginner-friendly course and you can literally start with very basic or no prior knowledge of this coding language, and gradually build up your logic building skills as you progress.
However, it will be much easier if you have some basic knowledge of the programming concepts. And, some bit of prior coding experience with Python.
What will I learn from this Machine Learning with Python training course? +
This ML training course will transform you from a curious onlooker to a machine learning expert. There’s a lot you’ll be learning:
- Supervising ML algorithms; classification (spam filters), and regression (predicting prices)
- Build models, assessing their performances, and delivering results
- Master feature engineering
- Exploring data diversity
- Leveraging Scikit-learn and other python tools
What ML Python algorithms will I learn in this course?+
You’ll learn these 2 algorithm categories:
- Classification - Support Vector Machines (SVM) * Random Forests * K-Nearest Neighbors (KNN) * Logistic Regression
- Regression* Linear Regression * Decision Tree Regression
Are deep learning contents covered in this course?+
No, this python course majorly focuses on core fundamentals of ML concepts and algorithms.
Is there a special IDE recommended for this online ML course?+
There isn’t any one particular IDE recommended for this course. Some of the most popular IDE options for python include Jupyter Notebook, PyCharm, and Visual Studio Code (VS code).
Who should enroll for this ML learning course? +
This course is perfect for all those who want to become expert ML engineers, or those wanting to solidify their understanding of Python fundamentals and Machine Learning algorithms.
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- 1 year of full access
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