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Machine Learning & Data Science Bootcamp with R & Python
Rating: 4.0 out of 5(49 ratings)
1,362 students

Machine Learning & Data Science Bootcamp with R & Python

Learn R, Python, Machine Learning, Deep Learning, Google Colab, Real world projects with Code and step by step guidance
Created byAcademy of Computing & Artificial Intelligence
Last updated 7/2021
English
English [Auto],

What you'll learn

  • Why we need Data Mining & Machine Learning
  • What is Data Mining
  • What is Machine Learning
  • Traditional Programming Vs Machine Learning
  • Steps to Solve a Data Mining & Machine Learning Problem
  • Types of Learning in Machine learning (Supervised, Unsupervised, Reinforcement )
  • Classification & Clustering
  • Setting up the Environment for Machine Learning - R language and R studio , Python, Anaconda
  • Introduction to Deep Learning - Guest Lecture
  • Machine learning project : Car Price Prediction Project
  • Kaggle - Covid 19- Classification (Chest X-ray.) - Covid-19 & Pneumonia
  • Supervised Learning
  • Unsupervised Learning

Course content

11 sections • 34 lectures • 5h 7m total length

  • Why we need Data Mining & Machine Learning4:34

    Explore why data mining and machine learning matter as data volumes explode, traditional techniques fail, and patterns enable smarter decision making, personalized customer relationship management, and competitive advantage.

  • What is Data Mining3:19

    Explore data mining as the non-trivial extraction of implicit, previously unknown, potentially useful information. Combine statistics, machine learning, recognition, and database techniques to analyze large datasets and reveal meaningful patterns.

  • What is Machine Learning8:47

    Define machine learning as a subfield of computer science that uses algorithms to let computers learn from data and extract information automatically, without explicit programming.

  • Traditional Programming Vs Machine Learning3:44

    Explore the difference between traditional programming and machine learning, where data guides the creation of a model instead of explicit programming, including neural networks.

  • Steps to Solve a Data Mining & Machine Learning Problem6:42

    Discover the data mining and machine learning workflow from data gathering and cleaning to feature engineering, algorithm selection, model evaluation, and data quality considerations.

  • Types of Learning in Machine learning (Supervised, Unsupervised, Reinforcement )4:01

    Explore the three main types of machine learning, supervised, unsupervised, and reinforcement learning, through definitions and examples like house price prediction and disease diagnosis, highlighting data labels and rewards.

  • Classification : Definition10:22

    Explain classification within data mining, detailing training sets, records, and attributes, and illustrate building a model to assign unseen records to a class such as luxury.

  • Clustering : Definition4:58

    Define clustering as grouping data points by similarity into clusters using a similarity measure, such as Euclidean distance, and view it as a preprocessing step for other machine learning tasks.

Requirements

  • Computer with Internet connection
  • Python or any programming language

Description

Academy of Computing & Artificial Intelligence proudly present you the course "Data Engineering with Python". It all started when the expert team of Academy of Computing & Artificial Intelligence (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2021.

At the end of the Course you will be able to start your career in Data Mining & Machine Learning.

1) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance

2) Setting up the Environment for Machine Learning - Step by step guidance  [R Programming & Python]

3) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines (SVM), Random Forest)

4) Unsupervised Learning

5) Convolutional Neural Networks - CNN

6) Artificial Neural Networks

7) Real World Projects with Source



Course Learning Outcomes

To provide awareness of  (Supervised & Unsupervised learning) coming under Machine Learning (Why we need Data Mining & Machine Learning, What is Data Mining, What is Machine Learning, Traditional Programming Vs Machine Learning, Steps to Solve a Data Mining & Machine Learning Problem, Classification , Clustering)

Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.

To build appropriate neural models from using state-of-the-art python framework.

To setup the Environment for Machine Learning - Step by step guidance  [R Programming & Python]

Convolutional Neural Networks - CNN

Resources from MIT and many famous Universities

Projects with Source


Who this course is for:

  • Anyone who wish to start the career in Data Science & Machine Learning

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