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Course Information
Course Overview
Learn R, Python, Machine Learning, Deep Learning, Google Colab, Real world projects with Code and step by step guidance
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
Course Content
- 10 section(s)
- 34 lecture(s)
- Section 1 Introduction
- Section 2 Setting up the Environment for Machine Learning - R language and R studio
- Section 3 Setting up the Environment for Machine Learning - Python & Anaconda
- Section 4 Machine Learning - Supervised Learning
- Section 5 Machine Learning - Unsupervised Learning - Clustering
- Section 6 Implementing a ANN with R programming / R Studio
- Section 7 Convolutional Neural Networks - CNN
- Section 8 Data Science & Machine Learning Resources
- Section 9 MIT Introduction to Deep Learning - Guest Lecture - Online
- Section 10 Machine learning project : Car Price Prediction Project
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
Skills covered in this course
Reviews
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OOshan Perera
I have a much better understanding about Machine Learning now. And got to practice more with R and Python. Thank You
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GGayani Peshala Karunaratne
Excellent
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AAyeshmantha Wijegunathileke
This is a great course to grasp the fundamentals of Machine Learning and Data Science.