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83
Unlimited Duration
September 9, 2022
Courses
Students
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.
Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.
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- Introduction Unlimited
- Exercise 1.1: Determine which of the systems is more appropriate for the following examples Unlimited
- Applications of Machine Learning Unlimited
- Types of Machine Learning Algorithms Unlimited
- Exercise 1.2: Classify the machine learning problem into Supervised or Unsupervised Unlimited
- Process of Building a Machine Learning System Unlimited
- Summary Unlimited
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- Introduction Unlimited
- Machine Learning Process Unlimited
- Step 3: Data Preparation and Exploratory data analysis Unlimited
- Exercise 2.1: Reading the data and Missing Value Analysis Unlimited
- Step 4: Modeling Unlimited
- Exercise 2.2: Linear Regression from scratch Unlimited
- Exercise 2.3: Linear Regression using sklearn Unlimited
- Step 5: Evaluation Unlimited
- Exercise 2.4: RMSE using numpy and sklearn Unlimited
- Step 6: Deployment Unlimited
- Activity Unlimited
- Project Unlimited
- Summary Unlimited
- Introduction Unlimited
- Machine Learning Process Unlimited
- Step 3: Data Preparation and Exploratory data analysis Unlimited
- Exercise 3.1: Reading the data, Missing value analysis and treatment Unlimited
- Categorical Encoding Unlimited
- Exercise 3.2: Modelling using One Hot and Ordinal Encoding Unlimited
- Feature Importance Unlimited
- Exercise. 3.3 Feature importance using mutual information Unlimited
- Modelling Unlimited
- Exercise. 3.4 Modelling using Logistic Regression Unlimited
- Evaluation Unlimited
- Exercise. 3.5 Evaluation of the model using accuracy (numpy) Unlimited
- Exercise. 3.6 Evaluation of the model using accuracy (scratch) Unlimited
- Deployment Unlimited
- Activity Unlimited
- Project Unlimited
- Summary Unlimited
- Introduction Unlimited
- Wikipedia content classification Project Unlimited
- Exercise 5.1: Creating Wikipedia Content Classification app Unlimited
- Predictions from the wiki model Unlimited
- Exercise 5.2: Making predictions Unlimited
- Saving the Model as a Pickle File Unlimited
- Exercise 5.3 Using Pickle Format Unlimited
- Version management Unlimited
- Exercise 5.4: Creating the requirements file (Package version management) Unlimited
- Virtual Environments Unlimited
- Exercise 5.5: Virtual Environment Unlimited
- Creating Flask Web App Unlimited
- Exercise 5.6: Flask app (running locally) Unlimited
- Exercise 5.7: Docker Container Unlimited
- Deployment to Cloud Unlimited
- Exercise 5.8: Deployment to Cloud. 1 (Heroku) Unlimited
- Activity Unlimited
- Project Unlimited
- Summary Unlimited
