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Machine Learning Career Track

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 course, you will learn about the fundamentals of machine learning. We will see examples of supervised, unsupervised and reinforcement learning. We will use a few machine learning API's to build a movie recommendation engine.

Course Objectives

  • Introduce you to fundamentals of Machine Learning
  • Serve as a launch pad for your career in Machine Learning and Data science

Requirements

  • A computer and broadband internet connection
  • No software required, we'll install everything as we go.

Who is the target audience?

  • This course is for beginners with a none to a small amount of Machine Learning experience.

Course Syllabus

Module 0: Getting Started
  • Python and Libraries Setup
Module 1: Introduction to Python and Statistics
  • Introduction to Python Programming
  • Intermediate Python Concepts
  • Python Packages For Data Science Numpy & Matplotlib
  • Stats for Machine Learning
  • Exploratory Data Analysis - 1
  • Exploratory Data Analysis - 2
  • Inferential Statistics
Module 2: Data Extraction and Cleaning
  • Introduction to SQL
  • SQL For Data Science - Part 2
  • Web Scraping with Python
  • Web Scraping with Python - Part 2
  • Working with APIs
  • Text Data Cleaning
  • Module 2 Revision and Test
Module 3: Supervised Machine Learning
  • Introduction to Machine Learning
  • Naive Bayes Classifier
  • Content Based Recommendation Systems
  • Recommendation Systems - Collaborative Filtering
  • Gradient Descent For Machine Learning
  • Logistic Regression
Module 4: Intermediate ML Concepts
  • SVM - Introduction
  • Digit Recognition with SVM
  • Ensemble Methods, Bagging & Boosting
Neural Networks
  • Introduction to Neural Networks
  • Neural Networks - Part 2
  • Introduction to TensorFlow
  • Convolution Neural Networks (CNN)
  • Object Recognintion with Neural Networks
Unsupervised Learning
  • Introduction to Unsupervised Learning using K-means
  • Principal Component Analysis (PCA)
  • Face Recognition using PCA
  • Model Selection and Boosting
Reinforcement Learning
  • Inroduction to Reinforcement Learning
  • Reinforcement Learning with OpenAI Gym
  • Building a Crawling Robot with Q-Learning
  • Solving a Mouse Cat maze with RL
Capstone Project
  • Part 1 - Research
Mihir Thakkar
About the Instructor

Mihir has earned his Masters degree in Computer Engineering from University of Maryland, College Park. Prior to starting Code Heroku, he has worked in the US as a software engineer for a healthcare IT company and taught summer courses at Columbia University, New York.

Our Reviews

Attended the Machine Learning class yesterday and I must say that it is on of the best platforms which really thinks about its students and is dedicated to provide a better learning environment for the advancement of Indian education system. The class was interactive and I enjoyed taking it ! I would love to join future sessions on machine learning.

- Suman Adhikari

I've been to a lot of online sessions but this one stands out from the rest. The online class I've attended is Supervised Machine Learning, It is super easy to understand and follow up with the video

- Akhil Leventis

Session on web2py was good. More hands-on session was useful for understanding. Waiting for still more sessions like this one. Thank u!

- Ritu Koneri

I had my first session with Code Heroku and I found it to be quite insightful. I am a beginner at coding, so i was looking for some ways to learn new skills and i found this Building apps with Python workshop ad. The session was delivered in a manner such that a beginner could comprehend easily and could get hands-on learning experience. Thanks for the great session! Looking forward to other sessions as well.

- Preeti Manchanda

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Machine Learning Career Track