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About this course

This Course teaches you the basics of Machine Learning using an approachable, and well-known programming language, Python. Machine learning provides the ability to learn without being explicitly programmed. It focuses on the development of computer programs that can change when exposed to new data. You’ll learn about the Supervised Vs Unsupervised learning, and do a comparison of each. We’ll explore many popular algorithms including classification, Regression, clustering, dimensional reduction and popular models such as Train/Test split, Root Mean Square Error, Random Forests and apply those algorithms & Models in practical areas like Medical, Marketing, Trades etc. This course helps you to crack the interview of a Machine Learning Engineer and start your career as ML Engineer.

What You’ll Learn: 

  • You learn the basics of Machine Learning and its importance 
  • Understand the importance and need of Python for ML 
  • Able the understand the supervised, Unsupervised and Reinforcement Learning 
  • Compare supervised Vs Unsupervised Vs Reinforcement Learning 
  • Real life examples of different ways the Machine Learning Models affect society 
  • Use the various algorithms of ML in your Problem 
  • Knows the application of each ML Models 
  • Apply ML in your Problem by building a project 

SKILLS YOU WILL GAIN
  • Python
  • Machine Learning
  • Tensorflow
  • Keras
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
avatar  Learner Career Outcomes
  • 52%

    started a new career after completing these courses
  • 46%

    started a new career after completing these courses
  • 14%

    got a pay increase or promotion
  •   Flexible deadlines
    Reset deadlines in accordance to your schedule.
  •   Shareable Certificate
    Earn a Certificate upon completion
  •   100% online
    Start instantly and learn at your own schedule.
  •   English

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Syllabus - What you will learn from this course

Module

    1    

4 Hours to complete

Module 1: Python For Machine Learning

  • What is Machine Learning ?
  • Why do we need to know ML ?
    • Importance of ML in AI and DS
    • How ML is helpful for solving complex problems
    • Is ML Engineer a good career ?
  • Basics of Machine Learning 
    • Introduction to different models and algorithms in ML
  • Basics of Python for Machine Learning
    • Python Data Structures 
    • Python Functions, Modules and Packages 
    • Python Object Oriented Programming
  • How Python is helpful in learning ML
    • Benefits of Learning Python for ML
  • Using Python instead of R for ML
    • Compare Python and R Programming for use as ML language
  • Python Tools for ML tasks

Module

    2    

2 Hours to complete

Module 2: Introduction to Machine Learning Algorithms

  • Introduction to Machine Learning algorithms
  • Classification of ML algorithms 
  • Supervised Learning 
    • What is Supervised Learning ?
    • Various algorithms used in Supervised Learning
  • Unsupervised Learning
    • What is Unsupervised learning ?
    • Various algorithms used in Unsupervised learning
  • Reinforcement Learning
    • What is Reinforcement Learning ?
    • Various algorithms used in Reinforcement learning
  • Comparison of Supervised, Unsupervised and Reinforcement Learning

Module

    3    

3 Hours to complete

Module 3: Supervised Learning in ML

  • Fundamentals of Supervised Learning
    • Classification and Regression in Supervised Learning
  • Classification Model
    • K-Nearest Neighbour
    • Decision Tree
    • Logistic Regression
    • Support Vector Machine
    • Model Evaluation
  • Application of Classification
    • Diagnostics and Identity Fraud detection
    • Image Classification and Customer Retention

Module

    4    

3 Hours to complete

Module 4: Regression in ML

  • Regression Model
    • Linear Regression
    • Non-Linear Regression
    • Model Evaluation
    • Evaluation Metrics
    • Multiple-Linear Regression
  • Logistic Regression Vs Linear Regression
  • Application of Regression Model
    • Weather and Market Forecasting
    • Estimating Life Expectancy
    • Advertising Popularity Prediction

Module

    5    

2 Hours to complete

Module 5: Unsupervised Learning in ML

  • Fundamentals of Unsupervised learning 
    • Clustering Dimensionality Reduction in Unsupervised Learning
  • Clustering Model
    • K-Means Clustering
    • Heirarchical Clustering 
    • Density-Based Clustering
  • Application of Clustering 
    • Targeted Marketing 
    • Recommender Systems 
    • Customer Segmentation

Module

    6    

2 Hours to complete

Module 6: Dimensionality Reduction in ML

  • Introduction to Dimensionality Reduction
  • Techniques used in Dimensionality Reduction
    • Feature selection methods
    • Matrix Factorization
    • Manifold Learning 
    • Autoencoder Methods
  • Application of Dimensionality Reduction
    • Big Data Visualization
    • Feature Elicitation
    • Meaninful Compression
    • Structure Discovery

Module

    7    

2 Hours to complete

Module 7: Reinforcement Learning in ML

  • Introduction to Reinforcement Learning
  • Types of Reinforcement Learning
    • Positive Reinforcement 
    • Negative Reinforcement
  • Reinforcement Learning in Artificial Intelligence 
  • Application of Reinforcement Learning 
    • Game AI
    • Real Time Decisions
    • Robot Navigation
    • Skill Acquisition and learning tasks

Module

    8    

3 Hours to complete

Module 8: Built a Simple Project in Machine Learning

  • How to start ML Projects ?
    • Understand and define the problem
    • Analyse and prepare data
    • Apply the algorithms
    • Reduce the errors
    • Predict the result
  • Built a Music Recommendation System Project 


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