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Course Curriculum

Why use Python for data science
Installation of Python using Anaconda in local system along with Jupiter notebook
Flowchart for representing logic
Data Structures in python
Conditional statements

Introduction to Numpy
Creating Arrays
Initial Placeholders
Saving

Introduction to Pandas
Pandas Data structures (Series

Preparing the data
Creating the plot
Plotting Routines
Customizing the Plot
Saving the Plot
Displaying the Plot

Areas of Math essential to machine learning (Probability, Statistical Inference,
Linear Algebra, Calculus)
Importance of Math in Machine Learning
The concept of probability
Probability spaces
Axioms of Probability
Types of probability spaces (Discrete and Continuous distribution)
Random Variables
Multivariate probability distributions
Example of Multivariate distribution
Marginal and Conditional Probability
Example of Marginal Probability
Example of Conditional Probability
Continuous Multivariate Distribution
Expected value of a function
Example of Expected value of a function
Expected Value in Continuous Space
Mean
Variance
Covariance
Pearson Correlation Coefficient
Complement rule for occurrence of an event
Product rule for co-occurrence of events
Rule of total probability
Independence of event occurrence
Bayes Rule with example
Probabilities: When to add, When to multiply

Motivation- Linear Algebra
Representation of problems in Linear Algebra
Matrix representations and its operations
Eigen values and Eigen vectors
Singular Value Decompositions of a Matrix

Feature Scaling
Feature Standardization
Label Encoding
One Hot Encoding

Steps of Data Exploration and Preparation
Missing Value Treatment
Techniques of Outlier Detection and Treatment
Art of Feature Engineering

What is Machine Learning
Examples of Machine Learning
Supervised, Unsupervised and Reinforcement learning
Difference between Unsupervised and Supervised learning
Nomenclature
Classification
Supervised learning pipeline
Linear Regression
Logistic Regression
Decision Tree
Support Vector Machine
Naive Bayes
K Nearest Neighbors
K- Means
Random Forest
Dimensionality Reduction methods
Gradient Boosting Algorithms- GBM, XGBoost, Light GBM, CatBoost
Case Study with live implementation of the above algorithms in Python

Introduction
Derivatives
Geometric definition
Taking the derivative
Step-by-Step
Machine learning use cases
Chain Rule:
How it works
Step-by-Step
Multiple functions
Gradients:
Partial derivatives
Step-by-Step
Directional Derivatives
Useful Properties
Integrals
Computing integrals
Applications of Integrations

Biological Neuron
Artificial Neural Network
Layered Networks
Neural Network Applications
The simplest model-Perceptron
Second Generation Neural Networks
Back Propagation Algorithm
Why Deep layered Neural Network
2006 Breakthrough
Unsupervised greedy layer wise training procedure
Layer wise unsupervised Pre-training
Layer wise local unsupervised learning
Experiments
Classification errors on MNIST training
Experiments
Conclusions
Applications
Recent Deep learning Highlights Biological Neuron
Artificial Neural Network
Layered Networks
Neural Network Applications
The simplest model-Perceptron
Second Generation Neural Networks
Back Propagation Algorithm
Why Deep layered Neural Network
2006 Breakthrough
Unsupervised greedy layer wise training procedure
Layer wise unsupervised Pre-training
Layer wise local unsupervised learning
Experiments
Classification errors on MNIST training
Experiments
Conclusions
Applications
Recent Deep learning Highlights

When to apply neural networks
General Way to solve problems using Neural Networks
Understanding Image data and popular libraries to solve it
What is TensorFlow
A typical flow of Tensorflow
Implementing Multi-Layer perceptron in Tensorflow
Limitations of Tensorflow
Tensorflow vs other Libraries

Introduction to Keras
Advantages of using Keras
Limitations of using Keras
General way to solve problems with Neural Networks
Starting with a simple Keras implementation on “Identify the Digits”
Hyper parameters to look for in Neural Networks
Parameter fine tuning
Transfer Learning using keras

An overview of PyTorch
Diving into technicalities
Building a neural network in Numpy vs PyTorch
Comparison of PyTorch with other deep learning libraries
Solving an image recognition problem with PyTorch
Transfer learning using PyTorch

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