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 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 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 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 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 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 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 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 .