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Module

1

- Basics of Python Language

- Python objects with details of shell/numbers/variables etc.

- Comparison operators

-Range, List Comprehension,

Functions, Lambda expressions etc.

-Introduction to NumPy

-Random functions, Reshape, Arithmetic Operations

-Hands-on project

Module

2

Introduction to Pandas

- Selecting a single column, important series methods

- Indexing & Sorting; loc & iloc with series

- Inspecting dataFrames, filtering with conditional operators

-Adding & removing columns; updating values, working with date & time

-Hands-on Project

Module

3

-Working with Matplotlib Library; Working with different plots

-Working with text

- Concatenating Series & DataFrames

- Working with Seaborn Library

-Seaborn categorical Plots

-Hands-on project

Module

4

- Machine Learning Basics, introduction to supervised & unsupervised learning

-  Linear Regression for One and Multiple Variables, Cost Function & Gradient Function

- Ordinary Least Square, Dummy Variables, One Hot Encoding, Polynomial Regression

- Anscombe’s quartet, Performance Metrics like Mean Absolute Error, Root Mean Squared Error, - Regularization (Ridge & Lasso)

Module

5

- Logistic Regression, Sigmoid Function, Anscombe’s quartet

-Confusion Matrix, interpreting parameters like F-1 score, Accuracy, Precision, Recall etc.

- Bias-variance trade off, Overfitting, Underfitting of Models

-K- nearest neighbors (KNN), Elbow Method; Distance Metric in KNN

-Understanding Support Vector machines using Hyperplanes; Maximum Margin Classifier

Module

6

- Higher Dimension Transformation and Projection, Kernels :: Polynomial, RBF etc.

- Decision Trees, Nodes: Root, Leaf, Parent, Children. Tree Pruning, Gini Impurity

-Random Forests, Ensemble Learners, Information Gain

-Boosted Trees, Weak and Strong Learners, AdaBoost, Gradient Boosting, Stump Classification

-Naive Bayes classifier, Conditional Probability, Bayes Theorem 

Module

7

- Natural Language Processing (NLP), Count Vectorization, Extracting Features From Text Data, Term Frequency - Inverse Document Frequency (TF-IDF), Document Term Matrix (DTM)

-Unsupervised Learning Basics

-K-Means Clustering, Clustering of unlabelled data, Assigning new point to the cluster

-Hierarchical Clustering: Agglomerative and Divisive Approach, Dendrogram, Linkage Matrix, Similarity Metrics, Ward

Module

8

- DBSCAN, epsilon distance, Core, Border and Outlier

-Principal Component Analysis (PCA), Dimension Reduction

-Introduction to Deep Learning

- Artificial Neural Networks

-Perceptron Model, Activation Functions; Cost Functions and Gradient Descent

-Forward and Backward Propagation; Keras vs TensorFlow

-Hands-on Project on ML