Course Features Course Details Module 1 Introduction to python 1 of 5 – Installation and setting up environment – Introduction of python data type and data structure – Exercise Python Data Science Module - Introduction to Numpy 2 of 5 – Review of required mathematics and statistics along side with related functions – Exercise Python Data Science Module - Introduction to Scipy 3 of 5 – Review of required mathematics and statistics along side with related functions – Exercise Python Data Science Module - Introduction to Pandas 4 of 5 – Introduction of Data Manipulation and Preprocessing with pandas – Exercise Introduction to Matplotlib 5 of 5 – Visualizations of Data – Exercise Module 2 Supervised Algorithm with Python Sklearn Module 1 of 1 Model Evaluation Techniques – K-Nearest Neighbour – Support Vector Machines (SVM) – Decision Tree – Linear Regression – Logistic Regression – Naive Bayes Module 3 Ensemble Technique 1 of 2 – Introduction to Bagging and Boosting – Random Forest – Gradient Boosting – AdaBoost Dimension Reduction Techniques 2 of 2 – Principle Component Analysis (PCA) – Linear Discriminant Analysis (LDA) – t-SNE Module 4 Unsupervised Algorithm with Python Sklearn Module 1 of 1 – Model Evaluation Techniques – K-means – Hierarchical Clustering Module 5 Capstone Project 1 of 1 More Courses by this Instructor Blockchain 2 hours Blockchain essentials (0 Votes)This Course is FREE Venkatesan M 922 facebookJoin us on facebooktwitterJoin us on twitterlinkedinJoin us on linkedininstagramJoin us on instagramyoutubeJoin us on youtube
Introduction to python 1 of 5 – Installation and setting up environment – Introduction of python data type and data structure – Exercise Python Data Science Module - Introduction to Numpy 2 of 5 – Review of required mathematics and statistics along side with related functions – Exercise Python Data Science Module - Introduction to Scipy 3 of 5 – Review of required mathematics and statistics along side with related functions – Exercise Python Data Science Module - Introduction to Pandas 4 of 5 – Introduction of Data Manipulation and Preprocessing with pandas – Exercise Introduction to Matplotlib 5 of 5 – Visualizations of Data – Exercise
Supervised Algorithm with Python Sklearn Module 1 of 1 Model Evaluation Techniques – K-Nearest Neighbour – Support Vector Machines (SVM) – Decision Tree – Linear Regression – Logistic Regression – Naive Bayes
Ensemble Technique 1 of 2 – Introduction to Bagging and Boosting – Random Forest – Gradient Boosting – AdaBoost Dimension Reduction Techniques 2 of 2 – Principle Component Analysis (PCA) – Linear Discriminant Analysis (LDA) – t-SNE
Unsupervised Algorithm with Python Sklearn Module 1 of 1 – Model Evaluation Techniques – K-means – Hierarchical Clustering
Venkatesan M