Machine Learning, Deep Learning & AI using Python

Machine Learning, Deep Learning & AI using Python

Introduction

• ML Fundamentals
• ML Common Use Cases
• Understanding Supervised and Unsupervised Learning Techniques

 Clustering

• Similarity Metrics
• Distance Measure Types: Euclidean, Cosine Measures
• Creating predictive models
• Understanding K-Means Clustering
• Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
• Case study

Implementing Association rule mining

• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Recommendation Use-case
• Case study

Understanding Process flow of Supervised Learning Techniques

Decision Tree Classifier

• How to build Decision trees
• What is Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Decision Tree
• Confusion Matrix
• Case study

Random Forest Classifier

• What is Random Forests
• Features of Random Forest
• Out of Box Error Estimate and Variable Importance
• Case study

Naive Bayes Classifier.

• Case study

Project Discussion

Problem Statement and Analysis

• Various approaches to solve a Data Science Problem
• Pros and Cons of different approaches and algorithms.

Linear Regression

• Case study
• Introduction to Predictive Modeling
• Linear Regression Overview
• Simple Linear Regression
• Multiple Linear Regression

Logistic Regression

• Case study
• Logistic Regression Overview
• Data Partitioning
• Univariate Analysis
• Bivariate Analysis
• Multicollinearity Analysis
• Model Building
• Model Validation
• Model Performance Assessment AUC & ROC curves
• Scorecard

Support Vector Machines

• Case Study
• Introduction to SVMs
• SVM History
• Vectors Overview
• Decision Surfaces
• Linear SVMs
• The Kernel Trick
• Non-Linear SVMs
• The Kernel SVM

Time Series Analysis

• Describe Time Series data
• Format your Time Series data
• List the different components of Time Series data
• Discuss different kind of Time Series scenarios
• Choose the model according to the Time series scenario
• Implement the model for forecasting
• Explain working and implementation of ARIMA model
• Illustrate the working and implementation of different ETS models
• Forecast the data using the respective model
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied
• Implement respective model for forecasting
• Visualizing and formatting Time Series data
• Plotting decomposed Time Series data plot
• Applying ARIMA and ETS model for Time Series forecasting
• Forecasting for given Time period
• Case Study

Machine Learning Project

Machine learning algorithms Python

• Various machine learning algorithms in Python
• Apply machine learning algorithms in Python

Feature Selection and Pre-processing

• How to select the right data
• Which are the best features to use
• Additional feature selection techniques
• A feature selection case study
• Preprocessing
• Preprocessing Scaling Techniques
• How to preprocess your data
• How to scale your data
• Feature Scaling Final Project

Which Algorithms perform best

• Highly efficient machine learning algorithms
• Bagging Decision Trees
• The power of ensembles
• Random Forest Ensemble technique
• Boosting – Adaboost
• Boosting ensemble stochastic gradient boosting
• A final ensemble technique

Model selection cross validation score

• Introduction Model Tuning
• Parameter Tuning GridSearchCV
• A second method to tune your algorithm
• How to automate machine learning
• Which ML algo should you choose
• How to compare machine learning algorithms in practice

Text Mining& NLP

• Sentimental Analysis
• Case study

PySpark and MLLib

• Introduction to Spark Core
• Spark Architecture
• Working with RDDs
• Introduction to PySpark
• Machine learning with PySpark – Mllib

Deep Learning & AI using Python

Deep Learning & AI

• Case Study
• Deep Learning Overview
• The Brain vs Neuron
• Introduction to Deep Learning

Introduction to Artificial Neural Networks

• The Detailed ANN
• The Activation Functions
• How do ANNs work & learn
• Gradient Descent
• Stochastic Gradient Descent
• Backpropogation
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• Backpropagation – Learning Algorithm
• Understand Backpropagation – Using Neural Network Example
• MLP Digit-Classifier using TensorFlow
• Building a multi-layered perceptron for classification
• Why Deep Networks
• Why Deep Networks give better accuracy?
• Use-Case Implementation
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent

Convolutional Neural Networks

• Convolutional Operation
• Relu Layers
• What is Pooling vs Flattening
• Full Connection
• Softmax vs Cross Entropy
• ” Building a real world convolutional neural network
• for image classification”

What are RNNs – Introduction to RNNs

• Recurrent neural networks rnn
• LSTMs understanding LSTMs
• long short term memory neural networks lstm in python

Restricted Boltzmann Machine (RBM) and Autoencoders

• Restricted Boltzmann Machine
• Applications of RBM
• Introduction to Autoencoders
• Autoencoders applications
• Understanding Autoencoders
• Building a Autoencoder model

Tensorflow with Python

• Introducing Tensorflow
• Introducing Tensorflow
• Why Tensorflow?
• What is tensorflow?
• Tensorflow as an Interface
• Tensorflow as an environment
• Tensors
• Computation Graph
• Installing Tensorflow
• Tensorflow training
• Prepare Data
• Tensor types
• Loss and Optimization
• Running tensorflow programs

Building Neural Networks using

Tensorflow

• Tensors
• Tensorflow data types
• CPU vs GPU vs TPU
• Tensorflow methods
• Introduction to Neural Networks
• Neural Network Architecture
• Linear Regression example revisited
• The Neuron
• Neural Network Layers
• The MNIST Dataset
• Coding MNIST NN

Deep Learning using

Tensorflow

• Deepening the network
• Images and Pixels
• How humans recognise images
• Convolutional Neural Networks
• ConvNet Architecture
• Overfitting and Regularization
• Max Pooling and ReLU activations
• Dropout
• Strides and Zero Padding
• Coding Deep ConvNets demo
• Debugging Neural Networks
• Visualising NN using Tensorflow
• Tensorboard

Transfer Learning using

Keras and TFLearn

• Transfer Learning Introduction
• Google Inception Model
• Retraining Google Inception with our own data demo
• Predicting new images
• Transfer Learning Summary
• Extending Tensorflow
• Keras
• TFLearn
• Keras vs TFLearn Comparison

 

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