Practical Predictive Analytics: Models and Methods
Know the models and methods of predictive analytics closely by joining Coursera.
Online
3 Weeks
4,117 INR Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Practical Predictive Analytics: Models and Methods is the certification programme on some of the practical and effective concepts and methods of machine learning and their real-world application. The course is a 100% online programme that the learners can pursue at their own pace. Offered by Coursera, Practical Predictive Analytics: Models and Methods Certification Course is the second course in Data Science at Scale Specialization provided by the University of Washington.
Practical Predictive Analytics: Models and Methods Certification Syllabus will shed light on statistical experiment designing, R Programming, Random Forest, Predictive Analytics and Machine Learning. The subtitles for the course videos of Practical Predictive Analytics: Models and Methods Certification by Coursera are available in different languages, such as French, Portuguese (European), Korean, Russian, English, and Spanish, allowing non-English learners to take the online programme.
The Highlights
- Provided by Coursera
- Offered by the University of Washington
- Self-Paced Learning Option
- 100% Online Course
- Around 6 Hours to Complete
- Flexible Deadlines
- Shareable Certificate
- Financial Aid Available
Programme Offerings
- English videos with multiple subtitles
- Shareable Certificate
- Financial aid available
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
The fees for the course Practical Predictive Analytics: Models and Methods is -
| Head | Amount in INR |
| 1 month | Rs. 4,117 |
| 3 month | Rs. 8,234 |
| 6 month | Rs. 12,352 |
What you will learn
At the end of the Practical Predictive Analytics: Models and Methods Training, the students will be able to learn the application of associated optimization methods including the gradient descent and variants, the application of unsupervised learning concepts and methods and the concepts of large-scale graph analytics such as PageRank, recursive queries, and community detection and the like.
Who it is for
Practical Predictive Analytics: Models and Methods Classes is highly recommended for professionals such as Data Scientist, ML Engineer and the like.
Admission Details
Step 1 -Browse the official URL
https://www.coursera.org/learn/predictive-analytics
Step 2- Kickstart the online programme ‘Practical Predictive Analytics: Models and Methods, by opting for the option ‘Enrol’.
The Syllabus
Videos
- Appetite Whetting: Bad Science
- Hypothesis Testing
- Significance Tests and P-Values
- Example: Difference of Means
- Deriving the Sampling Distribution
- Shuffle Test for Significance
- Comparing Classical and Resampling Methods
- Bootstrap
- Resampling Caveats
- Outliers and Rank Transformation
- Example: Chi-Squared Test
- Bad Science Revisited: Publication Bias
- Effect Size
- Meta-analysis
- Fraud and Benford's Law
- Intuition for Benford's Law
- Benford's Law Explained Visually
- Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
- Multiple Hypothesis Testing: False Discovery Rate
- Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
- Big Data and Spurious Correlations
- Spurious Correlations: Stock Price Example
- How is Big Data Different?
- Bayesian vs. Frequentist
- Motivation for Bayesian Approaches
- Bayes' Theorem
- Applying Bayes' Theorem
- Naive Bayes: Spam Filtering
Videos
- Statistics vs. Machine Learning
- Simple Examples
- Structure of a Machine Learning Problem
- Classification with Simple Rules
- Learning Rules
- Rules: Sequential Covering
- Rules Recap
- From Rules to Trees
- Entropy
- Measuring Entropy
- Using Information Gain to Build Trees
- Building Trees: ID3 Algorithm
- Building Trees: C.45 Algorithm
- Rules and Trees Recap
- Overfitting
- Evaluation: Leave One Out Cross Validation
- Evaluation: Accuracy and ROC Curves
- Bootstrap Revisited
- Ensembles, Bagging, Boosting
- Boosting Walkthrough
- Random Forests
- Random Forests: Variable Importance
- Summary: Trees and Forests
- Nearest Neighbor
- Nearest Neighbor: Similarity Functions
- Nearest Neighbor: Curse of Dimensionality
Reading
- R Assignment: Classification of Ocean Microbes
Practice Exercise
- R Assignment: Classification of Ocean Microbes
Videos
- Optimization by Gradient Descent
- Gradient Descent Visually
- Gradient Descent in Detail
- Gradient Descent: Questions to Consider
- Intuition for Logistic Regression
- Intuition for Support Vector Machines
- Support Vector Machine Example
- Intuition for Regularization
- Intuition for LASSO and Ridge Regression
- Stochastic and Batched Gradient Descent
- Parallelizing Gradient Descent
Videos
- Introduction to Unsupervised Learning
- K-means
- DBSCAN
- DBSCAN Variable Density and Parallel Algorithms
Instructors
Articles
Student Community: Where Questions Find Answers