The Future - Data Analytics and Machine Learning
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data, and how to use a set of data to discover potentially predictive relationships.
As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning
Understand the Modules
- Introduction To Data Science.
- Introduction to Inferential Statistics
- Introduction To Python Programming
- Introduction To Hypothesis Testing
- Introduction To Descriptive Statistics
- Introduction to Inferential Statistics
- Introduction To Hypothesis Testing
- Machine Learning
- Exploratory Data Analysis
- Data Visualization
- Introduction To Machine Learning
- Data Preprocessing
- Linear Regression
- Classification Models Matrix
- Logic Regression
- SVM - Support Vector Machines
- NN - K Nearest Neighbor
- Random Forest
- Decision Tree
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction - PCA
- Machine Learning Pipelines & Model
- Deployment
- Portfolio & Project
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