### Batches

### Training Dates

### 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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