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The Complete Machine Learning Course with Python [Video]
4.4
(9 reviews total)
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Introduction
-
Getting Started with Anaconda
-
Regression
- Introduction
- Categories of Machine Learning
- Working with Scikit-Learn
- Boston Housing Data - EDA
- Correlation Analysis and Feature Selection
- Simple Linear Regression Modelling with Boston Housing Data
- Robust Regression
- Evaluate Model Performance
- Multiple Regression with statsmodel
- Multiple Regression and Feature Importance
- Ordinary Least Square Regression and Gradient Descent
- Regularised Method for Regression
- Polynomial Regression
- Dealing with Non-linear relationships
- Feature Importance Revisited
- Data Pre-Processing 1
- Data Pre-Processing 2
- Variance Bias Trade Off - Validation Curve
- Variance Bias Trade Off - Learning Curve
- Cross Validation
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Classification
- Introduction
- Logistic Regression 1
- Logistic Regression 2
- MNIST Project 1 - Introduction
- MNIST Project 2 - SGDClassifiers
- MNIST Project 3 - Performance Measures
- MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
- MNIST Project 5 - Precision and Recall Tradeoff
- MNIST Project 6 - The ROC Curve
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Support Vector Machine (SVM)
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Tree
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Ensemble Machine Learning
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k-Nearest Neighbours (kNN)
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Dimensionality Reduction
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Unsupervised Learning: Clustering
About this video
Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
- Set up a Python development environment correctly
- Gain complete machine learning toolsets to tackle most real-world problems
- Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
- Combine multiple models with by bagging, boosting or stacking
- Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
- Develop in Jupyter (IPython) notebook, Spyder and various IDE
- Communicate visually and effectively with Matplotlib and Seaborn
- Engineer new features to improve algorithm predictions
- Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
- Use SVM for handwriting recognition, and classification problems in general
- Use decision trees to predict staff attrition
- Apply the association rule to retail shopping datasets
- And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.
Style and Approach
You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
- Publication date:
- October 2018
- Publisher
- Packt
- Duration
- 18 hours 22 minutes
- ISBN
- 9781789953725
Download code from GitHub
Latest Reviews
(9 reviews total)Great new material and updated concepts
5
Perfect for any IT professional!
5
Muy importante saber sobre machine learning en la actualidad.
5
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