Machine Learning Training Course in Irving

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.

Hands-on exercise: 

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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.

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.

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 

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

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

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

• Extracting, loading and transforming data into usable format to gather insights. 
• Data manipulation and handling to pre-process the data.
• Feature engineering and scaling the data for various problem statements. 
• Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
• Assessment and monitoring of the model created using the machine learning models.

• Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
• Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
• Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
• Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
• Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
• Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
• Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
• AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.