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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