Data Science & Machine Learning (AI)

Introduction to Artificial Intelligence

• Decoding Artificial Intelligence
• Fundamentals of Machine Learning and Deep Learning
• Machine Learning Workflow
• Performance Metrics

Statistics and Linear Algebra Basics

• Sample and population data?
• The fundamentals of descriptive statistics
• Measures of central tendency, asymmetry, and variability
• Practical example: descriptive statistics
• Distributions
• Estimators and estimates
• Confidence intervals: advanced topics
• Practical example: inferential statistics
• Hypothesis testing: Introduction
• introduction to Probability
• Conditional Probability.
• Bay’s theorem.
• Vectors vs Matrices.
• Vectors Magnitude and direction.
• Cos similarity between vectors.
• Matrix operations (dot product, multiplication, addition, subtraction, inverse).
• Covariance Matrix.
• Eigen Vectors and Eigen Values.

Python for Data Science

• Python Basics
  • Conditions and Loops.
  • Python Functions.
  • Python Recursion.
  • Python Classes.
• Python Data Structures.
  • Variables
  • Lists
  • Tuple
  • Set
  • dictionaries.
• Working with Data in Python
  • Read and Write data to files.
  • Data pickling.

Data Science with Python

• Data Science Overview
• Data Analytics Overview
• Statistical Analysis and Business Applications
• Python Environment Setup and Essentials
• Mathematical Computing with Python (NumPy)
  • Vectors and arrays.
  • Matrix Multiplication and dot product.
  • Matrix transpose and inverse matrix.
  • Statistical operations on arrays.
• Scientific computing with Python
• Data Manipulation with Pandas
  • Pandas Series and Data Frames.
  • Statistical Analysis using Pandas.
  • Data Cleaning using pandas.
  • Dummy and Categorical Data.
  • Read and write to csv and excel files.
• Data Visualization in Python using matplotlib.
  • Line, Bar, Scatter plot.
  • Histogram graph.
• Web Scraping with Beautiful Soup.

Machine Learning

• Introduction to Artificial Intelligence and Machine Learning
• Data Prepossessing:
  • Feature Scaling.
  • Missing Data.
  • Dummy Variable.
  • Imbalanced Data.
• Feature Engineering.
  • Backward Elimination.
  • Forward Elimination.
  • Model Validation.
• Supervised Learning:
  • Linear Regression.
  • Logistic Regression.
  • Naive Bays.
  • Regression and Classification Metrics, Confusion Matrix, ROC and AUC.

• Ensemble Learning
  • Decision Tree.
  • Random Forest.
  • Ensemble models
• Unsupervised learning
  • K-means Clustering.
  • Principle Component Analysis PCA.
• Recommender Systems
  • Content Based.
  • Collaborative Filter Based.
• Association Rule
  • Market basket analysis
  • Support
  • Confidence
  • Lift
• Text Processing
  • Convert text to structured shape.
  • Methods of text encoding
  • Text Vectorization
  • Text classification
• Artificial Neural Network
• Model Deployment

Introduction to Artificial Intelligence

• Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
• Fundamental concepts of Machine Learning and Deep Learning
• Machine Learning workflow and how to implement the steps effectively
• The role of performance metrics and how to identify their essential methods

Statistics and Linear Algebra Basics

• Understand the fundamentals of statistics
• Work with different types of data
• How to plot different types of data
• Calculate the measures of central tendency, asymmetry, and variability
• Calculate correlation and covariance
• Distinguish and work with different types of distribution
• Perform hypothesis testing
• Make data-driven decisions
• Understand the mechanics of regression analysis
• Carry out regression analysis
• Use and understand dummy variables
• Understand the concepts needed for data science even with Python.

Data Science with Python

• Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics
• Install the required Python environment and other auxiliary tools and libraries
• Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
• Perform high-level mathematical computing using the NumPy, Pandas package and its vast library of mathematical functions.
• Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
• Perform data analysis and manipulation using data structures and tools provided in the Pandas package
• Gain expertise in Machine Learning using the Scikit-Learn package
• Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
• Use the Scikit-Learn package for natural language processing
• Use the matplotlib library of Python for data visualization
• Extract useful data from websites by performing web scraping using Python.

Machine Learning

• Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling
• Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
• Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning
• Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
• Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithm, which include Boosting & Bagging techniques
• Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning

Artificial Intelligence course is well suited for a variety of profiles like:

• Developers aspiring to be an ‘Artificial Intelligence Engineer’ or Machine Learning engineers
•  Analytics managers who are leading a team of analysts
•  Information architects who want to gain expertise in Artificial Intelligence algorithms
•  Graduates looking to build a career in Artificial Intelligence and Machine Learning