Types of Errors in Machine Learning Population and Sample Test in Inferential Statistics, P-value Tables and Critical value, Confidence Interval , T-test Significance, alpha Critical value, Degree of freedom.
T-test in detail Chi-square in detail Anova in detail , Chi-square in detail with practical implementation . Anova in detail with practical implementation.
What is Database ? •Types of Databases •What is Structured Query Language (SQL) ? •Database Structure •Creating our First DataBase •Simple Query •SQL Datatypes •Categories of SQL Commands •Database related Queries •Table Related Query •Primary Key and Foreign Key •Constraints in SQL
Introduction to Power BI DAX Why DAX is Important in Power BI Scope of Usage with DAX. Usability Options DAX Context: Row Context and Filter Context DAX Entities: Calculated Columns and Measures Fundamental Functions in DAX Parameter in Power BI
New Parameter (Numeric Range and Field) M Language What is M-Mesh Language Security : Manage Roles and View As Connect Data to Mysql Database Schedule Refresh in Power BI Basics of Machine Learning Supervised Ml vs Unsupervised ML
Multiple Linear Regression Mathematical Intuition in Detail. Logistic Regression Mathematical Intuition in Detail. Practical Implementation of Multiple Linear Regression(Python Code) Practical Implementation of Logistic Regression(Python Code) Evaluation Metrics Regression and Classification
Decision Tree Classification Mathematical Intuition In Detail,Decision Tree Regression Mathematical Intuition In Detail,Decision Tree Classification Python Code Decision Tree Regression Python Code
PCA- Dimensionality Reduction Python Code In Detail, Deep Learning Introduction, Artificial Neural Network, Single Neural Network, Back Propagation, Weight Updation Formula
Weight updation Formula Vanishing Gradient Problem Activation Functions Loss Function (MSE,MAE,Huber Loss, Loss Function, Categorical Cross Entropy) Softmax Activation Function Linear Activation Function Optimizers (Gradient Descent, SGD,Minibatch SGD)
NLP Introduction Text Preprocessing Tokenization Stop words Stemming Lemmatization One hot encoding Bag of words TF-IDF Word to Vector Practical Implementation of NLP Task