Saturday, February 23, 2019



Best Artificial Intelligence training institute in noida

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Deep Learning: A revolution in Artificial Intelligence
  1. Limitations of Machine Learning
What is Deep Learning?
  1. Need for Data Scientists
  2. Foundation of Data Science
  3. What is Business Intelligence
  4. What is Data Analysis
  5. What is Data Mining
What is Machine Learning?
Analytics vs Data Science
  1. Value Chain
  2. Types of Analytics
  3. Lifecycle Probability
  4. Analytics Project Lifecycle
  5. Advantage of Deep Learning over Machine learning
  6. Reasons for Deep Learning
  7. Real-Life use cases of Deep Learning
  8. Review of Machine Learning
Data
  1. Basis of Data Categorization
  2. Types of Data
  3. Data Collection Types
  4. Forms of Data & Sources
  5. Data Quality & Changes
  6. Data Quality Issues
  7. Data Quality Story
  8. What is Data Architecture
  9. Components of Data Architecture
  10. OLTP vs OLAP
  11. How is Data Stored?
Big Data
  1. What is Big Data?
  2. 5 Vs of Big Data
  3. Big Data Architecture
  4. Big Data Technologies
  5. Big Data Challenge
  6. Big Data Requirements
  7. Big Data Distributed Computing & Complexity
  8. Hadoop
  9. Mapreduce Framework
  10. Hadoop Ecosystem
Data Science Deep Dive
  1. What Data Science is
  2. Why Data Scientists are in demand
  3. What is a Data Product
  4. The growing need for Data Science
  5. Large Scale Analysis Cost vs Storage
  6. Data Science Skills
  7. Data Science Use Cases
  8. Data Science Project Life Cycle & Stages
  9. Data Acquisition
  10. Where to source data
  11. Techniques
  12. Evaluating input data
  13. Data formats
  14. Data Quantity
  15. Data Quality
  16. Resolution Techniques
  17. Data Transformation
  18. File format Conversions
  19. Anonymization

Python

  1. Python Overview
  2. About Interpreted Languages
  3. Advantages/Disadvantages of Python pydoc.
  4. Starting Python
  5. Interpreter PATH
  6. Using the Interpreter
  7. Running a Python Script
  8. Using Variables
  9. Keywords
  10. Built-in Functions
  11. Strings Different Literals
  12. Math Operators and Expressions
  13. Writing to the Screen
  14. String Formatting
  15. Command Line Parameters and Flow Control.
Lists
  1. Tuples
  2. Indexing and Slicing
  3. Iterating through a Sequence
  4. Functions for all Sequences
Operators and Keywords for Sequences
  1. The xrange() function
  2. List Comprehensions
  3. Generator Expressions
  4. Dictionaries and Sets.
Numpy & Pandas
  1. Learning NumPy
  2. Introduction to Pandas
  3. Creating Data Frames
  4. Grouping Sorting
  5. Plotting Data
  6. Creating Functions
  7. Slicing/Dicing Operations.
Deep Dive – Functions & Classes & Oops
  1. Functions
  2. Function Parameters
  3. Global Variables
  4. Variable Scope and Returning Values. Sorting
  5. Alternate Keys
  6. Lambda Functions
  7. Sorting Collections of Collections
  8. Classes & OOPs

Statistics

  1. What is Statistics
  2. Descriptive Statistics
  3. Central Tendency Measures
  4. The Story of Average
  5. Dispersion Measures
  6. Data Distributions
  7. Central Limit Theorem
  8. What is Sampling
  9. Why Sampling
  10. Sampling Methods
  11. Inferential Statistics
  12. What is Hypothesis testing
  13. Confidence Level
  14. Degrees of freedom
  15. what is p Value
  16. Chi-Square test
  17. What is ANOVA
  18. Correlation vs Regression
  19. Uses of Correlation & Regression

Machine Learning, Deep Learning & AI using Python

Introduction
  1. ML Fundamentals
  2. ML Common Use Cases
  3. Understanding Supervised and Unsupervised Learning Techniques
Clustering
  1. Similarity Metrics
  2. Distance Measure Types: Euclidean, Cosine Measures
  3. Creating predictive models
  4. Understanding K-Means Clustering
  5. Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  6. Case study
Implementing Association rule mining
  1. What is Association Rules & its use cases?
  2. What is Recommendation Engine & it’s working?
  3. Recommendation Use-case
  4. Case study
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
  1. How to build Decision trees
  2. What is Classification and its use cases?
  3. What is Decision Tree?
  4. Algorithm for Decision Tree Induction
  5. Creating a Decision Tree
  6. Confusion Matrix
  7. Case study
Random Forest Classifier
  1. What is Random Forests
  2. Features of Random Forest
  3. Out of Box Error Estimate and Variable Importance
  4. Case study
Naive Bayes Classifier.
  1. Case study
Project Discussion
Problem Statement and Analysis
  1. Various approaches to solve a Data Science Problem
  2. Pros and Cons of different approaches and algorithms.
Linear Regression
  1. Case study
  2. Introduction to Predictive Modeling
  3. Linear Regression Overview
  4. Simple Linear Regression
  5. Multiple Linear Regression
Logistic Regression
  1. Case study
  2. Logistic Regression Overview
  3. Data Partitioning
  4. Univariate Analysis
  5. Bivariate Analysis
  6. Multicollinearity Analysis
  7. Model Building
  8. Model Validation
  9. Model Performance Assessment AUC & ROC curves
  10. Scorecard
Support Vector Machines
  1. Case Study
  2. Introduction to SVMs
  3. SVM History
  4. Vectors Overview
  5. Decision Surfaces
  6. Linear SVMS
  7. The Kernel Trick
  8. Non-Linear SVMs
  9. The Kernel SVM
Time Series Analysis
  1. Describe Time Series data
  2. Format your Time Series data
  3. List the different components of Time Series data
  4. Discuss different kind of Time Series scenarios
  5. Choose the model according to the Time series scenario
  6. Implement the model for forecasting
  7. Explain working and implementation of ARIMA model
  8. Illustrate the working and implementation of different ETS models
  9. Forecast the data using the respective model
  10. What is Time Series data?
  11. Time Series variables
  12. Different components of Time Series data
  13. Visualize the data to identify Time Series Components
  14. Implement ARIMA model for forecasting
  15. Exponential smoothing models
  16. Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  17. Implement respective model for forecasting
  18. Visualizing and formatting Time Series data
  19. Plotting decomposed Time Series data plot
  20. Applying ARIMA and ETS model for Time Series forecasting
  21. Forecasting for given Time period
  22. Case Study
Machine Learning Project
Machine learning algorithms Python
  1. Various machine learning algorithms in Python
  2. Apply machine learning algorithms in Python
Feature Selection and Pre-processing
  1. How to select the right data
  2. Which are the best features to use
  3. Additional feature selection techniques
  4. A feature selection case study
  5. Preprocessing
  6. Preprocessing Scaling Techniques
  7. How to preprocess your data
  8. How to scale your data
  9. Feature Scaling Final Project
Which Algorithms perform best
  1. Highly efficient machine learning algorithms
  2. Bagging Decision Trees
  3. The power of ensembles
  4. Random Forest Ensemble technique
  5. Boosting – Adaboost
  6. Boosting ensemble stochastic gradient boosting
  7. A final ensemble technique
Model selection cross validation score
  1. Introduction Model Tuning
  2. Parameter Tuning GridSearchCV
  3. A second method to tune your algorithm
  4. How to automate machine learning
  5. Which ML algo should you choose
  6. How to compare machine learning algorithms in practice
Text Mining & NLP
  1. Sentimental Analysis
  2. Case study
PySpark and MLLib
  1. Introduction to Spark Core
  2. Spark Architecture
  3. Working with RDDs
  4. Introduction to PySpark
  5. Machine learning with PySpark – Mllib

Deep Learning & AI using Python

Deep Learning & AI
  1. Case Study
  2. Deep Learning Overview
  3. The Brain vs Neuron
  4. Introduction to Deep Learning
Introduction to Artificial Neural Networks
  1. The Detailed ANN
  2. The Activation Functions
  3. How do ANNs work & learn
  4. Gradient Descent
  5. Stochastic Gradient Descent
  6. Backpropagation
  7. Understand limitations of a Single Perceptron
  8. Understand Neural Networks in Detail
  9. Illustrate Multi-Layer Perceptron
  10. Backpropagation – Learning Algorithm
  11. Understand Backpropagation – Using Neural Network Example
  12. MLP Digit-Classifier using TensorFlow
  13. Building a multi-layered perceptron for classification
  14. Why Deep Networks
  15. Why Deep Networks give better accuracy?
  16. Use-Case Implementation
  17. Understand How Deep Network Works?
  18. How Backpropagation Works?
  19. Illustrate Forward pass, Backward pass
  20. Different variants of Gradient Descent
Convolutional Neural Networks
  1. Convolutional Operation
  2. Relu Layers
  3. What is Pooling vs Flattening
  4. Full Connection
  5. Softmax vs Cross Entropy
  6. ” Building a real world convolutional neural network
  7. for image classification”
What are RNNs – Introduction to RNNs
  1. Recurrent neural networks rnn
  2. LSTMs understanding LSTMs
  3. long short term memory neural networks lstm in python
Restricted Boltzmann Machine (RBM) and Autoencoders
  1. Restricted Boltzmann Machine
  2. Applications of RBM
  3. Introduction to Autoencoders
  4. Autoencoders applications
  5. Understanding Autoencoders
  6. Building a Autoencoder model
Tensorflow with Python
  1. Introducing Tensorflow
  2. Introducing Tensorflow
  3. Why Tensorflow?
  4. What is tensorflow?
  5. Tensorflow as an Interface
  6. Tensorflow as an environment
  7. Tensors
  8. Computation Graph
  9. Installing Tensorflow
  10. Tensorflow training
  11. Prepare Data
  12. Tensor types
  13. Loss and Optimization
  14. Running tensorflow programs
Building Neural Networks using
Tensorflow
  1. Tensors
  2. Tensorflow data types
  3. CPU vs GPU vs TPU
  4. Tensorflow methods
  5. Introduction to Neural Networks
  6. Neural Network Architecture
  7. Linear Regression example revisited
  8. The Neuron
  9. Neural Network Layers
  10. The MNIST Dataset
  11. Coding MNIST NN
Deep Learning using
Tensorflow
  1. Deepening the network
  2. Images and Pixels
  3. How humans recognise images
  4. Convolutional Neural Networks
  5. ConvNet Architecture
  6. Overfitting and Regularization
  7. Max Pooling and ReLU activations
  8. Dropout
  9. Strides and Zero Padding
  10. Coding Deep ConvNets demo
  11. Debugging Neural Networks
  12. Visualising NN using Tensorflow
  13. 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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Phone.No- +91-120-421-3880
EMail-  info@inovitechnologies.com

Address:-  F7 Sector-3 Noida UP 201301

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