Data Science (ML-DL-Ai)

Data science (ML-DL-ai)

Statistics  Multiple Regression  Model Building and Evaluation

Introduction to Statistics Model post fitting for Inference  Types of Statistics  Examining Residuals  Analytics Methodology and Problem-  Regression Assumptions  Solving Framework  Identifying Influential Observations  Populations and samples  Detecting Collinearity  Parameter and Statistics  Uses of variable: Dependent and Categorical Data Analysis  Independent variable  Describing categorical Data  Types of Variable: Continuous and  One-way frequency tables categorical variable  Association  Cross Tabulation Tables Descriptive Statistics  Test of Association Probability Theory and Distributions  Logistic Regression  Model Building Picturing your Data  Multiple Logistic Regression and  Histogram Interpretation  Normal Distribution  Skewness, Kurtosis Model Building and scoring for  Outlier detection Prediction  Introduction to predictive modelling Inferential Statistics  Building predictive model  Scoring Predictive Model Hypothesis Testing  Introduction to Machine Learning and Analytics Analysis of variance (ANOVA)  Two sample t-Test Introduction to Machine Learning  F-test  What is Machine Learning?  One-way ANOVA  Fundamental of Machine Learning  ANOVA hypothesis  Key Concepts and an example of ML  ANOVA Model  Supervised Learning  Two-way ANOVA  Unsupervised Learning

Regression Linear Regression with one variable  Exploratory data analysis  Model Representation  Hypothesis testing for correlation  Cost Function  Outliers, Types of Relationship,  Parameter Learning  Scatter plot  Gradient Descent  Missing Value Imputation  Simple Linear Regression Model

NextGen Soft Solutions Regd. Office # 202-B, II floor, Prashanthi Towers, Saradhi Studios Lane, Metro Pillar No 1463 Maithrivanam, Ameerpet, Hyderabad 500 038. Ph # +91 40 42700222, 8074358639. E-Mail Id: [email protected] URL: www.nextgensoftsolutions.com

Linear Regression with Multiple Python Variables  Basics of Python  Computing parameter analytically  Machine Learning Libraries  Ridge, Lasso, Polynomial Regression  Data Pre-processing/Exploration in Python Logistic Regression  Handling Missing Values  Classification  Handling Outliers  Hypothesis Testing  One Hot Encoder & Feature Scaling  Decision Boundary  Cost Function and Optimization Regression  Assumptions of Linear Regression Multiclass Classification  Simple Linear Regression Model Regularization  Cost Function & Gradient Descent  Overfitting, Under fitting  Multiple Regression  Model Building and Evaluation Model Evaluation and Selection  Ridge, Lasso and Polynomial  Confusion Matrix Regression  Precision-recall and ROC curve  Identifying Influential Features  Regression Evaluation  Regularization: Overfitting and underfitting Support Vector Machine  Cross-Validation

Decision Tree, Random Forest Categorical Data Analysis Unsupervised Learning  Describing categorical Data  Clustering  Association  K-mean Algorithm  Cross Tabulation Tables  Test of Association Dimensionality Reduction  Logistic Regression  Principal Component Analysis and  Decision Boundary applications  Cost Function and Optimization  Model Building Introduction to text analytics  Multiple Logistic Regression and

Interpretation Introduction to Neural Network Model Building and scoring for Machine Learning with Prediction  Introduction to predictive modelling Python  Building predictive model  Scoring Predictive Model Introduction to Machine Learning  What is Machine Learning? Multiclass/Multi-Label Classification  Fundamental of Machine Learning  Key Concepts and an example of ML Imbalanced Dataset

 Supervised Learning  Unsupervised Learning

Model Evaluation and Selection  Markov Decision Processes  Accuracy  Markov Decision Processes II  Confusion Matrix  Reinforcement Learning  Precision-recall and ROC curve  Reinforcement Learning II  Regression Evaluation  Probability  Bayes' Nets: Representation Support Vector Machine  Bayes' Nets: Independence

 Bayes' Nets: Inference K-Nearest Neighbours(K-NN)  Bayes' Nets: Sampling Decision Tree, Random Forest  Decision Diagrams / VPI  Clustering  HMMs: Filtering  K-means Algorithm  HMMs: Wrap-up / Speech  ML: Naive Bayes Unsupervised Learning  ML: Perceptron  ML: Kernels and Clustering Dimensionality Reduction  ML: Neural Networks and Decision  Principal Component Analysis and Trees applications  Robotics / Language / Vision

Introduction to text analytics/  Miscellaneous Topics

Natural Language Processing  Bag of Words Deep Learning and Image  TF-IDF Recognition  LDA (Latent Discriminant Analysis)

Model Selection, Ensemble models Training Neural Networks, part I  Activation functions, initialization, XG-Boost dropout,  batch normalization Introduction to Neural Network Course Introduction Recommender Systems  Computer vision overview  Collaborative Filtering  Historical context  Content-Based Filtering  Course logistics  SVD (Singular value Decomposition) Image Classification Artificial Intelligence  The data-driven approach  K-nearest neighbour  Introduction to AI  Linear classification I

 Agents and Search Loss Functions and Optimization  A* Search and Heuristics  Linear classification II  Constraint Satisfaction Problems  CSPs II Introduction to Neural Networks  Game Trees: Minimax  Backpropagation  Game Trees: Expectimax; Utilities  Multi-layer Perceptrons

 The neural viewpoint  Q-Learning, Actor-Critic

Convolutional Neural Networks  History Tableau 10 Visualization  Convolution and pooling  ConvNets outside vision Introduction to Tableau Desktop  Overview of Business Intelligence Training Neural Networks, part II  Introduction to Tableau Desktop  Update rules, ensembles, data  Use and benefits of Tableau Desktop augmentation, transfer learning  Tableau's Offerings  Guide to Install Tableau Desktop Deep Learning Software 10.5  Caffe, Torch, Theano, TensorFlow,  Keras, PyTorch, etc Tableau Desktop Interface  Start Page CNN Architectures  Data Source Page  AlexNet, VGG, GoogLeNet, ResNet,  Worksheet Interface etc  Creating a Basic View

 Recurrent Neural Networks Show Me  Shelves, cards , Marks and pills  RNN, LSTM, GRU  Language modelling Connecting Data Sources  Image captioning, visual question  Data Types  answering Soft attention  Data Roles  Higher-level representations, image  Visual Cues for Fields features  Data Preparation  Optimization, stochastic gradient  Data Source optimization descent  Joins

Detection and Segmentation  Cross Database Joins  Semantic segmentation  Data Blending  Object detection  Joining vs. Blending  Instance segmentation  Union  Creating Data Extracts Visualizing and Understanding  Writing Custom SQL  Feature visualization and inversion  Adversarial examples Organizing Data  DeepDream and style transfer  Filtering Data  Sorting Data Generative Models  Creating Combined Fields  PixelRNN/CNN  Creating Groups and Defining  Variational Autoencoders Aliases  Generative Adversarial Networks  Working with Sets and Combined Sets Deep Reinforcement Learning  Drilling and Hierarchy  Policy gradients, hard attention  Adding Grand Totals and Subtotals

 Changing Aggregation Functions  Measure Distance  Creating Bin  WMS server  Cross Data Source Filter  Use a background image  Custom Territories Formatting Data  Effectively use Titles, Captions, and Fields in Tableau Tooltips  Tableau generated fields  Format Results with the Edit Axes  Measure values and names  Formatting your View  When to use measure values and  Formatting results with Labels and names  Annotations  Number of records  Enabling Legends per Measure  Generated latitude and longitude  Special fields Calculations  Date hierarchies  Use Strings, Date, Logical, and  Discrete and continuous date parts Arithmetic Calculations  Custom dates  Create Table Calculations  Define a fiscal year  Discover Ad-hoc Analytics Parameters  Perform LOD Calculations  Create a parameter

Visualizations  Explore Parameter Controls  Creating Basic Charts such as Heat  Using Parameters in Calculations Map, Tree Map, Bullet Chart, and so  Using Parameters and Reference on Lines  Creating Advanced Chart as  Using Parameters with Filters

Waterfall, Pareto, Gantt, Market Create Dashboards and Stories Basket analysis, and Mekko Chart  Dashboard Interface Embed Views  Build Interactive Dashboards Analysis using Desktop  Explore Dashboard Actions  Reference lines  Best Practices for Creating Effective  Reference bands Dashboards  Reference distributions  Story Interface  Trend lines  Creating Stories  Statistical summary card  Share Your Work

 Instant Analytics Tableau Online  Forecasting  Creating Tableau online account  Clustering  Administering Tableau Online Mapping  Publish data source  Modify locations within Tableau  Publish Reports

 Import and manage custom Tableau Project geocoding  Industry based Project  Explore Geographic Search  Perform Pan/Zoom, Lasso, and Radial Selection