Syllabus - Statistics and ML

 

Statistics 30 hours ML 40 hours

 

Class 0 - Introduction to Stats, Statistical Thinking, Examples Related to that

 

Class  1 - Variable and Different types of Variables

Quantitative, Categorical, Discrete, Continuous, all with Examples

 

Class 2 - Introduction to Population, Sample, Population vs Sample, Sample Size, Deep Dive into Variables, Data Visualisation Basics, Which chart has to be used where?

 

Class 3 - Data Visualisation Continued(Python Code), Histogram vs Bar Chart, Frequency Distribution Table, Relative Frequency Distribution, 

 

Class 4 - Descriptive Stats

Descriptive Stats Intro and Basics : 

1. Measures of Central Tendency – Mean, Median and Mode
2. Measures of Dispersion – Standard Deviation, Variance, Range, IQR (Inter Quartile Range)
3. Measure of Symmetricity/Shape – Skewness and Kurtosis

 

Class 5 - Descriptive Stats Intro and Basics : 

Continued

 

Class 6 - Five Point Summary and Box Plot, Outliers, Causes of Outliers, How to treat Outliers, IQR Method and Z-Score Method, 

 

Class 7: 

Co-Variance, Direction of Relationship, Formula, Different Scenarios,

 

Class 8: Correlation Coefficient : Pearson’s Correlation, Spearman Rank Correlation, Direction & Strength of relationship

 

Class 9: Sampling Techniques, Need for Sampling, Types of Sampling - Probabilistic & Non Probabilistic Sampling. Simple Random Sampling, Systematic Sampling, Cluster Sampling, Stratified Sampling, Convenience Sampling, Quota Sampling etc.

 

Class 10: Probability & Distribution, Random Experiments, Additive Theorem of Probability, Multiplicative theorem of Probability,

 

Class 11: Conditional Probability, Examples on Conditional Probability, Origin of Bayes Theorem, Bayes Theorem Formula, Derivation, Prior and Posterior Probability

 

Class 12: Generalised form of Bayes Theorem, Central Limit Theorem, Empirical Formula etc., Different Probability Distribution

 

Class 13: Questions and practices on Bayes Theorem

 

Class 14: Introduction to Hypothesis Testing, Definition, Numerical, Point Estimate, Confidence Interval,

 

Class 15: Methods – Acceptance Region/Rejection Region Method, P Value Method, Critical Value Method, Z Test, T-Test, ANOVA.

 

Class 16: Numerical of All the tests and Python Code of the same.

 

Class 17: Linear Algebra, Matrix, Determinant, Addition/Subtraction of Vector/Matrix, Multiplication of vector/matrix,

 

Class 18: Angle Between Vectors/Matrix, Cross Product, Dot Product, Norm of Vector, Eigen Value & Eigen Vectors. Python Code Included.

 

Class 19: Introduction to Data Science/Machine Learning, History, Need of DS, Terminologies of ML – Algorithm, Train Data, Test Data, Model etc. Different steps of ML Lifecycle, Supervised and Unsupervised Learning examples.

 

Class 20: EDA, Pre-processing and Data Cleaning using Python. Techniques Discussion with Coding.

 

Class 21: Linear Regression & Gradient Descent – Intuition, Definition, Relationship, Best Fit Line, Ordinary Least Square, Gradient Descent, R-Squared, Adjusted R Squared, MAE, RMSE, MSE, MAPE, Python Code

 

Class 22: Logistic Regression – Intuition, Sigmoid Function, Logistic Function, Derivation, Decision Boundary, One vs All, Confusion Matrix, TP, FP, TN, FN, Precision, Recall, ROC, AUC of ROC Curve, Specificity, Sensitivity, F1-Score, PR Curve etc., Python Code

 

Class 23: Decision Tree – Intuition, Decision Based Models, Terminologies, Splitting Criteria – Gini, Entropy, Chi-Square, Python Code

 

Class 24: Random Forest– Intuition, Bagging Method, How different from Decision Tree, Cross Validation, Hyperparameter Tuning, Variable Importance, Grid Search CV. Python Code

 

Class 25: Tree Based Algorithm Continued – Differences, Bagging/Boosting. Bagging and Boosting Working. Adaboost Example.

 

Class 26: KNN: Intuition, Nearest Neighbours?, Distances – Euclidean, Manhattan, What is K, How to Choose optimum value of K, Steps of KNN, Reasons of KNN being Expensive, Python Code.

 

Class 27: Naïve Bayes – Intuition, Why Naïve?, Bayes Theorem Refresher, Laplace Correction, Different Classifiers of Naïve Bayes – Gaussian, Multinomial, Bernoulli etc., Multi Class Classification Confusion Matrix, Python Code.

 

Class 28: K-Means/Hierarchical Clustering – Unsupervised Algorithm, Intuition ,Hierarchical and Non Hierarchical, Use Cases, Clustering Steps, How to choose Value of K, Elbow Method(WSS Curve), Dendogram, Agglomerative & Divisive Clustering Differences, Python Code – K Means & Hierarchical. Silhouette Coefficient,

 

Class 29: SVM: Intuition, Hyperplane, Decision Boundary, Support Vectors, Margin, Kernel Trick, Some Examples and Equation of Kernel Trick, Hyperparameters – Regularization Parameter (C) and Gamma. Python Code.

 

Class 30: PCA: Intuition ,Dimensionality Reduction, Variability, Co-Variance Matrix, Principal Components. Python Code.

 

Class 31: Interview Questions of Stats & Machine Learning