
Description
Interested in the field of Machine Learning? Then this course is for you!
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
COURSE SYLLABUS:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – 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.

Description
Interested in the field of Machine Learning? Then this course is for you!
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
COURSE SYLLABUS:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – 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.
Course Calendar
Start Date | End Date | Duration | Locaton | Register Now |
---|---|---|---|---|
19th July 2019 | 21th July 2019 | 3 Days | Pune | Register Now |
9th Aug 2019 | 11th Aug 2019 | 3 Days | Pune | Register Now |
13th Sep 2019 | 15th Sep 2019 | 3 Days | Pune | Register Now |
Course Calendar
Stat Date |
End Date | Duration | Location |
---|---|---|---|
19th July 2019 | 21th July 2019 | 3 Days | Pune |
Stat Date |
End Date | Duration | Location |
---|---|---|---|
9th Aug 2019 | 11 Aug 2019 | 3 Days | Pune |
Stat Date |
End Date | Duration | Location |
---|---|---|---|
13th Sep 2019 | 15th Sep 2019 | 3 Days | Pune |