Overview
Overview
Age: 18+
Course Description
This course helps you to practically learn machine learning concepts from basics to advanced and develop the skills needed for an exciting career in Machine Learning.
Course Overview
This career building course involves you to build multiple projects and helps you develop a good understanding of Machine Learning concepts practically.
You will develop the following projects:
- New House Price Prediction
- Credit Card Fraud Detection
- Movie Recommendation System
- Hand Written Digits Recognition
Course Features
- Lectures 94
- Quizzes 0
- Duration 100 hours
- Skill level All levels
- Language English
- Students 1
- Certificate No
- Assessments Yes
Curriculum
Curriculum
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Module 1: What is Machine Learning 8
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Learn: What is Machine LearningLecture1.1
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Learn: Supervised vs Unsupervised LearningLecture1.2
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Learn: Machine Learning Terminology & PipelineLecture1.3
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Learn: Tools for Machine LearningLecture1.4
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Do: Installation of AnacondaLecture1.5
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Do: Setting up a Jupyter NotebookLecture1.6
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Do: Explore JupyterLecture1.7
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Review: What is Machine LearningLecture1.8
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Module 2: Your First Machine Learning Model - Boston Housing 7
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Learn: Boston Housing – Problem DescriptionLecture2.1
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Learn: Types of Data & Its ImplicationsLecture2.2
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Learn: Preprocessing of DataLecture2.3
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Do: Import BH DataLecture2.4
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Do: Data Visualization of BH DataLecture2.5
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Do: Correlation between Target & AttributesLecture2.6
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Review: Your First Machine Learning Model!Lecture2.7
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Module 3: Mean Squared Error 5
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Learn: Errors & TypesLecture3.1
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Learn: Mean Squared ErrorLecture3.2
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Do: Define MSE FunctionLecture3.3
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Do: Calculate MSE for Sample DataLecture3.4
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Review: Mean Squared ErrorLecture3.5
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Module 4: Data Splitting & Accuracy of Model - Boston Housing 3
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Learn: Testing & Training Data – Cross ValidationLecture4.1
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Do: Split the datasetLecture4.2
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Review: Data Splitting & Accuracy of ModelLecture4.3
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Module 5: Linear Regression - Boston Housing 11
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Learn: Linear Regression & its TypesLecture5.1
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Learn: Gradient Descent – Learning RatesLecture5.2
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Learn: Programming logic – Update FunctionLecture5.3
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Do: Update FunctionLecture5.4
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Learn: Programming logic – Descent FunctionLecture5.5
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Do: Descent FunctionLecture5.6
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Learn: Programming logic – Model Training VisualizationLecture5.7
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Do: Model Training VisualizationLecture5.8
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Learn: Programming logic – Error VisualizationLecture5.9
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Do: Error VisualizationLecture5.10
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Review: Linear RegressionLecture5.11
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Module 6: Machine Learning Prediction - Boston Housing 5
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Learn: Prediction & ValidationLecture6.1
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Learn: Programming logic – Prediction of PricesLecture6.2
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Do: Prediction of PricesLecture6.3
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Do: Train Model with Different Attributes & PredictLecture6.4
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Review: Machine Learning PredictionLecture6.5
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Module 7: Credit Card Fraud Detection 4
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Learn: Credit Card Fraud Detection – Problem DescriptionLecture7.1
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Learn: Data Preprocessing – Programming LogicLecture7.2
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Do: Import & Visualize DataLecture7.3
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Do: Preprocess DataLecture7.4
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Module 8: SelectKBest Features 5
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Learn: What is SelectkBestLecture8.1
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Learn: SelectKBest – Programming LogicLecture8.2
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Do: SelectKBest FeaturesLecture8.3
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Do: Discard Bad FeaturesLecture8.4
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Do: Visualize FeaturesLecture8.5
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Module 9: Train Model 4
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Learn: What is Gaussian Naive Bayes AlgorithmLecture9.1
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Learn: What is Cross ValidationLecture9.2
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Learn: Gaussian Naive Bayes – Programming LogicLecture9.3
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Do: Split DatasetLecture9.4
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Module 10: Fraud Prediction 3
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Learn: What is Confusion MatrixLecture10.1
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Learn: Confusion Matrix – Programming LogicLecture10.2
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Do: Evaluation & VisualizationLecture10.3
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Module 11: Movie Recommendation System 3
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Learn: Movie Recommendation – Problem DescriptionLecture11.1
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Learn: MRS – Problem SetupLecture11.2
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Do: Download & Import DataLecture11.3
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Module 12: MRS - Data Preprocessing 7
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Learn: MRS – Data Visualization – Programming LogicLecture12.1
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Learn: MRS – Data Processing – Programming LogicLecture12.2
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Do: MRS – Data Vizualization – MoviesLecture12.3
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Do: MRS – Data Processing – MoviesLecture12.4
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Do: MRS – Data Vizualization & Processing – UsersLecture12.5
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Do: MRS – Create pivot table and sparse matrixLecture12.6
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Do: MRS – Map Data – Movies to Movie IDLecture12.7
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Module 13: Train Model 3
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Learn: K Nearest NeighboursLecture13.1
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Learn: MRS – Train the ModelLecture13.2
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Review: K Nearest NeighboursLecture13.3
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Module 14: MRS - Evaluate Model & Predict 3
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Learn: MRS – Search Movie Title – Programming LogicLecture14.1
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Do: MRS – Search Movie TitleLecture14.2
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Do: MRS – Recommend MoviesLecture14.3
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Module 15: Recognize Handwritten Digits 5
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Learn: What is Deep Learning?Lecture15.1
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Learn: Recognize Handwritten Digits – Problem DescriptionLecture15.2
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Learn: MNIST Dataset – DescriptionLecture15.3
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Learn: Recognize Handwritten Digits – Problem SetupLecture15.4
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Do: Import MNIST DatasetLecture15.5
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Module 16: MNIST Dataset - Preprocessing the Data 4
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Learn: Basic Properties of an ImageLecture16.1
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Do:MNIST Data – Properties of the DatasetLecture16.2
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Do: MNIST Data – Visualize Samples of the DataLecture16.3
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Do: MNIST Data – Scaling the DataLecture16.4
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Module 17: Neural Networks in Deep Learning 8
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Learn: What is a Neural NetworkLecture17.1
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Learn: Representation of Neural NetworksLecture17.2
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Learn: Output of a Neural NetworkLecture17.3
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Learn: Activation Functions in Neural NetworksLecture17.4
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Learn: TensorFlow & Keras – Basic ConceptsLecture17.5
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Learn: Recognize Handwritten Digits – Deep Learning Model – Programming LogicLecture17.6
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Do: Recognize Handwritten Digits – Setup Neural NetworkLecture17.7
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Do: Recognize Handwritten Digits – Train the ModelLecture17.8
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Module 18: Recognize Handwritten Digits - Evaluate the Model & Predict the Digits 6
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Learn: Recognize Handwritten Digits – Model Evaluation – Programming LogicLecture18.1
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Do: Recognize Handwritten Digits – Vizualize model trainingLecture18.2
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Do: Recognize Handwritten Digits – Evaluate Accuracy & Make PredictionsLecture18.3
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Do: Recognize Handwritten Digits – Function to Visualize ResultsLecture18.4
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Do: Recognize Handwritten Digits – Visualize Prediction of Random SamplesLecture18.5
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Do: Recognize Handwritten Digits – Visualize Prediction of Incorrect SamplesLecture18.6
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799.00 د.إ
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