Machine Learning for Beginners

About the Course

In this era of Alexa and Siri, Artificial Intelligence and Machine Learning have automated almost every task around us. Every professional, whether with technical or non technical requirements, is utilizing these technologies. Hence, for the people who are complete beginners and have no knowledge about the details and mathematics behind AI and ML, I am putting a weekend initiative where we would meet online and discuss some concept every weekend. Every concept would be discussed in detail with its implementation in Python. After attending every session, the attendees would have detailed knowledge and they can implement it in their respective fields. The course would also include different Machine Learning Projects on which the attendees would work on.  

Curriculum for the Course

  • INTRODUCTION TO PYTHON/R

  • INTRODUCTION TO MACHINE LEARNING PROJECTS

    • Framing the Problem

    • Getting and Importing Datasets, Libraries

    • Visualizing Data

    • Preparing Data for Machine Learning Algorithms (DATA PRE-PROCESSING)

      • Data Cleaning

      • Handling Text and Categorical Attributes

      • Feature Scaling

      • Splitting Datasets into Training and Test Data

  • Regression

    • Simple Linear Regression

    • Multiple Linear Regression

    • Polynomial Regression

    • Support Vector Regression

    • Decision Tree Regression

    • Random Forest Regression

    • Evaluating Regression Models Performance

  • Classification

    • Training Binary Classifier

    • Performance Measures

    • Logistic Regression

    • K-Nearest Neighbors

    • Support Vector Machines

    • Kernel SVM

    • Naive Bayes

    • Decision Tree Classification

    • Random Forest Classification

    • Evaluating Classification Models Performance

  • Dimensionality Reduction

    • PCA (Principal Component Analysis)

    • LDA (Linear Discriminant Analysis)

    • Other Dimensionality Reduction Techniques

  • Unsupervised Learning Techniques

    • Clustering

      • K-Means

      • DBSCAN

      • Other Clustering Algorithms

    • Gaussian Mixtures

  • Association Rule Learning

  • Reinforcement Learning

  • Deep Learning

    • ANN

    • Training Deep Neural Networks

    • Custom Models and Training with TensorFlow

    • Loading and Pre-Processing Data with TensorFlow

    • CNN

  • Introduction to NLP (Natural Language Processing)

  • Introduction to Computer Vision

  • 3 PROJECTS

    • (Tentative List)- The list can be modified depending on everyone’s requirements

  1. One Supervised Learning Project

  2. One Unsupervised Learning Project

  3. One Computer Vision Project

Course Structure and Schedule

Sessions would be conducted in the form of weekend webinars.

Every webinar would be of 1.5 -  2 hours.

Assignments would be shared after every session. The attendees can work on those assignments and get in touch with the Instructor for help and discussions. 

Upcoming Episodes

Python Fundamentals

Data PreProcessing

Regression

Classification Part 1

Further episodes would be updated soon!

© 2020 by tenbyten.io

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