Data Science, Machine Learning & Artificial Intelligence

Skill Sigma's Data Science- ML & AI Certification training course equips you with conceptual and technical skills to launch or advance your data career. The course is designed to build expertise in predictive analytics, machine learning, visualization, data manipulation and data science with hands-on assisted lab practice supported by Real Life Labs, get trained by industry experts and instructors who will continually guide and support you.

Build a solid project portfolio to demonstrate your abilities with our student projects and internships. Get globally recognized certification. Be a part of the Data Science community with over 5000 data scientists and business analysts.

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

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.
• 
  

Course Includes

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.
• 
  

Deployment, Analytics & Hybrid Services

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.
• 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.