Machine Learning


Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

  • Online Classes
  • Expert Instructions
  • 24*7 Expert
  • Flexible Schedule


We provide our certificate to all our students who completed the course. Some employers require a certification in order to apply for a job. Many people earn specialty certifications to help them advance in their careers.Earning a certification can give you a big leg up in the job market. It’s a key item that hiring employers look for on resumes. Some employers may also require workers to have certain certifications.

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  • Overview
  • Flow of course
  • What is Data?
  • How Data is generated?
  • Data Storage Overview
  • Types of Data
  • Software Life Cycle
  • What is data science
  • Importance of data science
  • Demand for data science
  • Prerequisite to learn data science
  • Data science life cycle
  • Why Organizations hiring Data Scientist
  • Real Time Projects overview
  • Data science vs Business Intelligence
  • Terms to Remember in Data Science
  • Artificial Intelligence Overview
  • Overview of Real time Project on Artificial Intelligence
  1. Installation of required softwares
  2. How to install Packages/ Libraries
  • Numpy
  • Pandas
  • Scipy
  • Scikit-Learn
  • Keras
  • Matplotlib
  • Seaborn
  • Cufflinks
  • Introduction to Python
  • Python 2 vs Python 3
  • Installation of Anaconda
  • Understanding Jupyter Notebook
  • Basic commands in Jupyter Notebook
  • Understanding Python Syntax
  • Comments (Single and Multi lines)
  • Python Identifiers
  • Operators
  • Data Types
  • Advanced Data Structure
  • Different type of Loops
  • Conditions
  • Functions
  • File Handling
  • Collecting data from different sources
  • Analyzing data
  • Data Preprocessing
  • Data Munging
  • Data Mining
  • Data Manipulation
  • Data Visualization
  • Feature Selection
  • Feature Scaling
  • Dimensionality Reduction Techniques
  • Basics of Statistics
  • Descriptive Statistics
  • Inferential Statistics
  • Qualitative Analysis
  • Quantitative Analysis
  • Hypothesis Testing
  • Data Distribution
  • Outlier Detection
  • Other Statistical fundamentals
  • Probability
  • Calculus
  • Linear algebra
  • Introduction to Linear Regression
  • Multiple Linear Regression
  • Ordinary Least Squares Method
  • Non Linear Models
  • Polynomial Regression
  • Support Vector Regressor
  • K-NN Regressor
  • Decision Tree Regressor
  • Random Forest Regressor

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