✔ Limitations of Machine Learning

✔ Need for Data Scientists
✔ Foundation of Data Science
✔ What is Business Intelligence
✔ What is Data Analysis
✔ What is Data Mining

✔ Value Chain
✔ Types of Analytics
✔ Lifecycle Probability
✔ Analytics Project Lifecycle
✔ Advantages of Deep Learning over Machine learning
✔ Reasons for Deep Learning
✔ Real-Life use cases of Deep Learning
✔ Review of Machine Learning

✔ Basis of Data Categorization
✔ Types of Data
✔ Data Collection Types
✔ Forms of Data & Sources
✔ Data Quality & Changes
✔ Data Quality Issues
✔ Data Quality Story
✔ What is Data Architecture
✔ Components of Data Architecture
OLTP vs OLAP
How is Data Stored?

✔ What is Big Data?
✔ 5 Vs of Big Data
✔ Big Data Architecture
✔ Big Data Technologies
✔ Big Data Challenge
✔ Big Data Requirements
✔ Big Data Distributed Computing & Complexity
✔ Hadoop
✔ Map-Reduce Framework
✔ Hadoop Ecosystem

✔ What Data Science is
✔ Why Data Scientists are in demand
✔ What is a Data Product
✔ The growing need for Data Science
✔ Large Scale Analysis Cost vs Storage
✔ Data Science Skills
✔ Data Science Use Cases
✔ Data Science Project Life Cycle & Stages
✔ Data Acquisition
✔ Where to source data
✔ Techniques
✔ Evaluating input data
✔ Data formats
✔ Data Quantity
✔ Data Quality
✔ Resolution Techniques
✔ Data Transformation
✔ File format Conversions
✔ Anonymization

✔ Python Overview
✔ About Interpreted Languages
✔ Advantages/Disadvantages of Python pydoc.
✔ Starting Python
✔ Interpreter PATH
✔ Using the Interpreter
✔ Running a Python Script
✔ Using Variables
✔ Keywords
✔ Built-in Functions
✔ StringsDifferent Literals
✔ Math Operators and Expressions
✔ Writing to the Screen
✔ String Formatting
✔ Command Line Parameters and Flow Control.
✔ Lists
✔ Tuples
✔ Indexing and Slicing
✔ Iterating through a Sequence
✔ Functions for all Sequences

✔ Range() function
✔ List Comprehensions
✔ Generator Expressions
✔ Dictionaries and Sets.

✔ Learning NumPy
✔ Introduction to Pandas
✔ Creating Data Frames
✔ GroupingSorting
✔ Plotting Data
✔ Creating Functions
✔ Slicing/Dicing Operations.

✔ Functions
✔ Function Parameters
✔ Global Variables
✔ Variable Scope and Returning Values. Sorting
✔ Alternate Keys
✔ Lambda Functions
✔ Sorting Collections of Collections
✔ Classes & OOPs

✔ What is Statistics
✔ Descriptive Statistics
✔ Central Tendency Measures
✔ The Story of Average
✔ Dispersion Measures
✔ Data Distributions
✔ Central Limit Theorem
✔ What is Sampling
✔ Why Sampling
✔ Sampling Methods
✔ Inferential Statistics
✔ What is Hypothesis testing
✔ Confidence Level
✔ Degrees of freedom
✔ What is pValue
✔ Chi-Square test
✔ What is ANOVA
✔ Correlation vs Regression
✔ Uses of Correlation & Regression

✔ Introduction
✔ ML Fundamentals
✔ ML Common Use Cases
✔ Understanding Supervised and Unsupervised Learning Techniques

✔ 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

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

✔ 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

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

✔ Case Study

✔ Various approaches to solving a Data Science Problem
✔ Pros and Cons of different approaches and algorithms.

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

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

✔ 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 scenarios based on which different Exponential
✔ The 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

✔ Various machine learning algorithms in Python
✔ Apply machine learning algorithms in Python

✔ 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

✔ 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

✔ 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

✔ Sentimental Analysis
✔ Case study

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

✔ Deep Learning & AI
✔ Case Study
✔ Deep Learning Overview
✔ The Brain vs Neuron
✔ Introduction to Deep Learning

✔ The Detailed ANN
✔ The Activation Functions
✔ How do ANNs work & learn
✔ Gradient Descent
✔ Stochastic Gradient Descent
✔ Backpropagation
✔ Understand the 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 Operation
✔ Relu Layers
✔ What is Pooling vs Flattening
✔ Full Connection
✔ Softmax vs Cross Entropy
✔ ” Building a real world convolutional neural network
for image classification”

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

✔ Restricted Boltzmann Machine
✔ Applications of RBM
✔ Introduction to Autoencoders
✔ Autoencoders applications
✔ Understanding Autoencoders
✔ Building an Autoencoder model

✔ 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

✔ 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

✔ 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
✔ Visualizing NN using Tensorflow
✔ Tensorboard

✔ 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