Data Science & Machine Learning (AI) Training Course

Home
Courses
CLS
Data Science & Machine Learning (AI)

Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions.
You will learn how to design intelligent models and advanced artificial neural networks and leverage predictive analytics to solve real-time problems in this course.
Learn about the major applications of Artificial Intelligence across various use cases across various fields like customer service, financial services, healthcare, etc.
Master the Machine Learning skills and tools used by the most innovative Artificial Intelligence teams across the globe as you delve into specializations, and gain experience solving real-world challenges.
Implement classical Artificial Intelligence and Machine Learning techniques such as search algorithms, neural networks, and tracking.
Design and build your own intelligent agents and apply them to create practical Artificial Intelligence projects including games, Machine Learning models, logic constraint satisfaction problems, knowledge- based systems, probabilistic models, agent decision-making functions and more.
Gain the ability to apply Artificial Intelligence and Machine Learning techniques for problem- solving and explain the limitations of current Artificial Intelligence techniques.

Course Outline

Data Science and Machine Learning with Python Training Course Outline

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.
- Big O notation.
- Stacks, queues and deques.
- 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 (Scipy)
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.
- Gradient Boost, XGBoost and LGBM
Unsupervised learning
- K-means Clustering.
- Hieratical Clustering.
- Gaussian Mixture Model GMM.
- Principle Component Analysis PCA.
Recommender Systems
- Content Based.
- Knowledge Based.
- Collaborative Filter Based.

What you will learn

Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
Fundamental concepts of Machine Learning and Deep Learning
Difference between supervised, semi-supervised and unsupervised 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
Estimate confidence intervals
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 Integrate Python with Hadoop, Spark, and MapReduce

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

Audience profile

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