Machine Learning Advanced – TechSoft

Machine Learning Advanced

Machine-Learning--advance

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.

Enquire Now

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

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