- Learn Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression and Random Forest Regression
- Learn Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification and Random Forest Classification
- Learn Clustering: K-Means and Hierarchical Clustering
- Learn Association Rule Learning: Apriori Algorithm and Eclat
- Learn Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Learn Natural Language Processing: Bag-of-words model and algorithms for NLP
- Learn Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Learn Dimensionality Reduction: PCA, LDA, Kernel PCA
- Learn Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
- Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
- This course also includes both Python and R code templates which you can download and use on your own projects.
- Master the concepts of Supervised and Unsupervised Learning
- Gain practical mastery over principles, algorithms, and applications of machine learning through a hands
- on approach which includes working on 28 case studies and one capstone project.
- Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
- Understand the concepts and operation of Support vector machines, Kernel SVM, Naive bayes, Decision tree classifier, Random forest classifier, Logistic regression, K
- nearest neighbors, K
- means clustering and more.
- Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
- Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems
- Examine and learn data manipulaton with R and Python functions also Learn the fundamentals of R and Python programming.
- Apply data visualization to make fancy plots also predictive Analytics to predict outcomes in R and Python.
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