Data Science Training Course in Washington

SQL Basics – 

• Fundamentals of Structured Query Language
• SQL Tables, Joins, Variables 

Advanced SQL –  

• SQL Functions, Subqueries, Rules, Views
• Nested Queries, string functions, pattern matching
• Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

• Types of UDFs, Inline table value, multi-statement table. 
• Stored procedures, rank function, triggers, etc. 

SQL Optimization and Performance

• Record grouping, searching, sorting, etc. 
• Clustered indexes, common table expressions.

Version Control 

• What is version control, types, SVN.

GIT 

• Git Lifecycle, Common Git commands, Working with branches in Git

• Github collaboration (pull request), Github Authentication (ssh and Http)

• Merging branches, Resolving merge conflicts, Git workflow

Descriptive Statistics – 

• Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

• Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Extract Transform Load

• Web Scraping, Interacting with APIs 

Data Handling with NumPy

• NumPy Arrays, CRUD Operations,etc. 
• Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices. 

Data Manipulation Using Pandas

• Loading the data, dataframes, series, CRUD operations, splitting the data, etc. 

Data Preprocessing

• Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc. 
• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc. 

Data Visualization

• Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
• Regression plots, categorical plots, area plots, etc, with Python seaborn. 

Introduction to Machine learning 

• Supervised, Unsupervised learning.
• Introduction to scikit-learn, Keras, etc. 

Regression 

• Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
• How to train the model in a regression problem. 
• How to evaluate the model for a regression problem. 
• How to optimize the efficiency of the regression model. 

Classification 

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables. 
• How to train the model in a classification problem. 
• How to evaluate the model for a classification problem. 
• How to optimize the efficiency of the classification model. 

Clustering 

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. 
• How to train the model in a clustering problem. 
• How to evaluate the model for a clustering problem. 
• How to optimize the efficiency of the clustering model.

Supervised Learning 

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc. 
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc. 
• Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.   
• Random Forest – Creating random forest models for classification problems in a supervised learning approach. 
• Support Vector Machine – SVM or support vector machines for regression and classification problems. 
• Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function. 
• K-Nearest Neighbors – A simple algorithm that can be used for classification problems. 
• Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting. 

Unsupervised Learning 

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach. 
• Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation. 
• Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data. 
• Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

• Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. 
• Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. 
• r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics 

• Introduction to keras API and tensorflow

Neural Networks

• Neural networks
• Multi-layered Neural Networks
• Artificial Neural Networks 

Deep Learning 

• Deep neural networks
• Convolutional Neural Networks 
• Recurrent Neural Networks
• GPU in deep learning
• Autoencoders, restricted boltzmann machine 

Text Mining, Cleaning, and Pre-processing

• Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text classification, NLTK, sentiment analysis, etc  

• Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

• Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine 

• Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI. 

RBM and DBNs & Variational AutoEncoder

• Introduction rbm and autoencoders
• Deploying rbm for deep neural networks, using rbm for collaborative filtering
• Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

• Constructing a convolutional neural network using TensorFlow
• Convolutional, dense, and pooling layers of CNNs
• Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

• Automated conversation bots leveraging
• Generative model, and the sequence to sequence model (lstm).

Distributed & Parallel Computing for Deep Learning Models

• Parallel Training, Distributed vs Parallel Computing
• Distributed computing in Tensorflow, Introduction to tf.distribute
• Distributed training across multiple CPUs, Distributed Training
• Distributed training across multiple GPUs, Federated Learning
• Parallel computing in Tensorflow

Reinforcement Learning

• Mapping the human mind with deep neural networks (dnns)
• Several building blocks of artificial neural networks (anns)
• The architecture of dnn and its building blocks
• Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

• Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
• Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
• Deploying deep learning models in Serverless Environments
• Deploying Model to Sage Maker
• Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Introduction to MLOps 

• MLOps lifecycle
• MLOps pipeline 
• MLOps Components, Processes, etc

Deploying Machine Learning Models 

• Introduction to Azure Machine Learning 
• Deploying Machine Learning Models using Azure