Data Science – Machine Learning & Artificial Intelligence

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Data Science – Machine Learning & Artificial Intelligence

Get project and career ready with a speciality certification on Machine Learning, Artificial Intelligence with Python. With assisted lab practice supported by Real Life Labs, get hands-on training on core topics of Data Science, ML, DL and AI from the experts at Skill Sigma and open doors to new career opportunities. Get access to real-life use cases from multiple domains and understand how to deploy your solutions to the web.

Key Highlights

• Students from across 14 countries.
• Delivered by mentors with international experience of 15+ years.
• Globally recognized certification.
• Experience of training more than 3000 Data Science professionals.
• Write your own ML & DL algorithms.
• Access to class videos and additional learning 24X7.
• 100% lab practice supported by real life labs.
• Career support services for all learners.

Program: Available in in-class & live sessions to attend from anywhere.
Duration: 5 months with more than 80 hours of classroom connect.
Entry: No entry criteria, requires high school math knowledge.

Python Programming for Analytics & Data Science

» Introduction, The Fundamentals of Python, Storing Items in Variables, String
» Formatting Program Flow Control in Python, Conditions With If, ElIf & Else, For Loops.
» Understanding Continue, Break and Else, While Loop, Lists, Dictionaries
» Python Matrix, DataFrames, Modules and Functions in Python, The standard Python library.
» Object-Oriented Python, Object Orientated Programming and Classes, Instances, Constructors, Self.
» Class Attributes, Getters and Properties, Getters and Setters, Data Attributes and Properties.
» Inheritance, Subclasses and Overloading, Calling Super Methods.

Machine Learning

» Introduction of Data Science and Machine Learning,
» Statistics for Machine Learning
» Sampling, Choosing the right Sampling Strategy.
» Understanding Numpy, Pandas, Dataframes, Scikit Learn,
» Data Pre-processing,
» Regression, Simple Linear Regression,
» Multiple Linear Regression, Polynomial Regression
» Decision Tree Regression, Random Forest Regression
» Classification, Logistic Regression, K-Nearest Neighbors (K-NN)
» Support Vector Machine (SVM), Kernel SVM, Naive Bayes
» Decision Tree Classification, Random Forest Classification
» Clustering, K-Means Clustering
» Reinforcement Learning
» Upper Confidence Bound (UCB), The Multi-Armed Bandit Problem.
» Thompson Sampling

Deep Learning & Artificial Intelligence

Pre-requisite:
» Basic Python Syntax.
» Comfortable using Jupyter notebooks.
» Loops and Conditional Statements.
» Writing Functions and using lambda expressions in Python.
» Basics around Numpy, matplotlib, Pandas.
» Good to have prior knowledge of any OOPS based language.

» Data Preprocessing
» Machine Learning Overview
» Neural Networks
» ANN-Artificial Neural Networks,
» CNN- Convolutional Neural Networks,
» RNN- Recurrent Neural Networks
» Self Organizing Maps,
» Boltzmann Machines,
» AutoEncoders
» Natural Language Processing With Deep Learning

Real Life Assisted Labs

Learn the science behind multiple industry use cases
Financial Analytics: Banking Fraud, Credit Score, Foreign Exchange Rate, Signature and Currency verification, Predicting Stocks, Loan Approvals
HR Analytics: Predicting Employee Performance, Salary Prediction
Customer & Sales Analytics: Sales Forecasts, New Product Analysis, Call Center Analysis
Ecommerce & Digital: Image Tagging, Reverse Image Search, Image Captioning, Object Detection, Image Segmentation, Semantic Segmentation, Image Denoising, Image Generation,
Medical & Health Analytics: Medical Images,
Climate & Geographic, Weather
Other:Text Summarization, AutoFill, Auto Suggestion, Patterns in text, speech and images, , Language Translation, Recognize and respond to Smart Devicers

Python Programming for Analytics & Data Science

» Introduction, The Fundamentals of Python, Storing Items in Variables, String
» Formatting Program Flow Control in Python, Conditions With If, ElIf & Else, For Loops.
» Understanding Continue, Break and Else, While Loop, Lists, Dictionaries
» Python Matrix, DataFrames, Modules and Functions in Python, The standard Python library.
» Object-Oriented Python, Object Orientated Programming and Classes, Instances, Constructors, Self.
» Class Attributes, Getters and Properties, Getters and Setters, Data Attributes and Properties.
» Inheritance, Subclasses and Overloading, Calling Super Methods.

Machine Learning

» Introduction of Data Science and Machine Learning,
» Statistics for Machine Learning
» Sampling, Choosing the right Sampling Strategy.
» Understanding Numpy, Pandas, Dataframes, Scikit Learn,
» Data Pre-processing,
» Regression, Simple Linear Regression,
» Multiple Linear Regression, Polynomial Regression
» Decision Tree Regression, Random Forest Regression
» Classification, Logistic Regression, K-Nearest Neighbors (K-NN)
» Support Vector Machine (SVM), Kernel SVM, Naive Bayes
» Decision Tree Classification, Random Forest Classification
» Clustering, K-Means Clustering
» Reinforcement Learning
» Upper Confidence Bound (UCB), The Multi-Armed Bandit Problem.
» Thompson Sampling

Deep Learning & Artificial Intelligence

Pre-requisite:
» Basic Python Syntax.
» Comfortable using Jupyter notebooks.
» Loops and Conditional Statements.
» Writing Functions and using lambda expressions in Python.
» Basics around Numpy, matplotlib, Pandas.
» Good to have prior knowledge of any OOPS based language.

» Data Preprocessing
» Machine Learning Overview
» Neural Networks
» ANN-Artificial Neural Networks,
» CNN- Convolutional Neural Networks,
» RNN- Recurrent Neural Networks
» Self Organizing Maps,
» Boltzmann Machines,
» AutoEncoders
» Natural Language Processing With Deep Learning

Real Life Assisted Labs

Learn the science behind multiple industry use cases
Financial Analytics: Banking Fraud, Credit Score, Foreign Exchange Rate, Signature and Currency verification, Predicting Stocks, Loan Approvals
HR Analytics: Predicting Employee Performance, Salary Prediction
Customer & Sales Analytics: Sales Forecasts, New Product Analysis, Call Center Analysis
Ecommerce & Digital: Image Tagging, Reverse Image Search, Image Captioning, Object Detection, Image Segmentation, Semantic Segmentation, Image Denoising, Image Generation,
Medical & Health Analytics: Medical Images,
Climate & Geographic, Weather
Other:Text Summarization, AutoFill, Auto Suggestion, Patterns in text, speech and images, , Language Translation, Recognize and respond to Smart Devicers

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