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Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,..

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Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,.., available at $44.99, has an average rating of 4.25, with 48 lectures, 9 quizzes, based on 35 reviews, and has 6913 subscribers.

You will learn about The three building blocks of Machine Learning: Maths, Methods and Machine. Maths: Calculus, Linear Algebra, Statistics, Naive Bayes Methods: Neural Networks, Deep Learning, PCA, Scikit-learn, Tensorflow, Keras Machine: Python, Cloud Computing, Colab Insights into real life projects and how to apply the concepts This course is ideal for individuals who are Everyone who is interested in machine learning and artificial intelligence. or Pupils, students, employees in all kind of roles, self-employed workers or You! It is particularly useful for Everyone who is interested in machine learning and artificial intelligence. or Pupils, students, employees in all kind of roles, self-employed workers or You!.

Enroll now: Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,..

Summary

Title: Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,..

Price: $44.99

Average Rating: 4.25

Number of Lectures: 48

Number of Quizzes: 9

Number of Published Lectures: 48

Number of Published Quizzes: 9

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 73

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

  • The three building blocks of Machine Learning: Maths, Methods and Machine.
  • Maths: Calculus, Linear Algebra, Statistics, Naive Bayes
  • Methods: Neural Networks, Deep Learning, PCA, Scikit-learn, Tensorflow, Keras
  • Machine: Python, Cloud Computing, Colab
  • Insights into real life projects and how to apply the concepts

Who Should Attend

  • Everyone who is interested in machine learning and artificial intelligence.
  • Pupils, students, employees in all kind of roles, self-employed workers
  • You!

Target Audiences

  • Everyone who is interested in machine learning and artificial intelligence.
  • Pupils, students, employees in all kind of roles, self-employed workers
  • You!

Do you want to master Machine Learning (ML) – the key field of the future?

ML is the core of artificial intelligence and will transform all industries and all areas of life.

This comprehensive course covers the three M’s Maths, Methods and Machine, and is easy to understand.

Maths

  • Calculus

  • Linear Algebra

  • Probability theory

  • Statistics

Methods

  • Machine learning libraries

    • Scikit-learn

    • Tensorflow

    • Keras

  • Estimators & Predictors

    • Neural Network (Deep Learning)

    • Support Vector Machine

    • K-Nearest Neighbor

    • Decision Tree

    • and many more

  • Concepts & techniques

    • Principal Component Analysis (PCA)

    • Neural Machine Translation (NMT)

    • Long Short-Term Memory (LSTM)

    • Monte-Carlo Tree Search (MCTS)

    • Deep Convolutional Generative Adversarial Network (DCGAN)

    • and many more

Machine

  • Python

  • Cloud Computing

  • Colab Cloud Notebook

These three building blocks will give you the deep understanding of the subject.

Machine Learning

  • Supervised learning

    • Regression

    • Classification

  • Unsupervised learning

  • Reinforcement learning

Furthermore projectswill provide insights into real life solutions.

Projects

  1. Titanic dataset (binary classification)

  2. Boston Housing dataset (regression)

  3. Student performance (binary classification)

  4. Hand-written digits (image recognition & generation)

  5. Stock market predictions

  6. Text recognition and language translation

  7. Autonomous driving (reinforcement learning)

  8. Mastering the game of GO (deep reinforcement learning)

  9. Segmentation of customer data (PCA)

  10. Spam detection (Bayes)

Do not hesitate and join the course. ML will transform your life!

This course is extraordinary, as it is easy to understand, and combines education with entertainment.

Learning should be exciting!

Enjoy the course and all the best for your future!

Machine Learning is the key component of artificial intelligence and will transform our lives and all industries.

Stay ahead of the game!

Course Curriculum

Chapter 1: Introduction & Overview

Lecture 1: Introduction

Lecture 2: ML in a Nutshell

Lecture 3: Linear Regression

Chapter 2: Methods 0: Scikit-learn

Lecture 1: Overview

Lecture 2: Data transformation and splitting

Lecture 3: Estimators

Lecture 4: Metrics

Lecture 5: Example: IRIS data set

Chapter 3: Project: Titanic (Binary Classification)

Lecture 1: Titanic Dataset

Lecture 2: Data exploration

Lecture 3: Data transformation and ML model

Chapter 4: Project: Boston Housing (Regression)

Lecture 1: Boston Housing solution

Chapter 5: Project: Student Performance (Binary Classification)

Lecture 1: Student Performance solution

Chapter 6: Methods 1: Neural Networks

Lecture 1: Concepts

Lecture 2: Forwardpropagation

Lecture 3: Backpropagation

Lecture 4: Activation Functions

Lecture 5: Loss Functions

Chapter 7: Methods 2: Tensorflow

Lecture 1: Overview

Lecture 2: Implementing a neural network

Lecture 3: Example: MNIST Fashion

Chapter 8: Project: MNIST hand-written digits (DCGAN)

Lecture 1: Introduction to GAN, CNN and DCGAN

Lecture 2: DCGAN MINST solution

Chapter 9: Project: Stock market prediction (LSTM)

Lecture 1: Introduction to time series data, RNN and LSTM

Lecture 2: LSTM solution

Chapter 10: Project: Language Translation (NMT)

Lecture 1: Introduction to Sequence-to-sequence (seq2seq) models

Lecture 2: NMT solution

Chapter 11: Reinforcement Learning

Lecture 1: Overview

Lecture 2: Autonomous driving

Lecture 3: Master the game of Go

Lecture 4: Whitepaper AlphaGo Zero

Chapter 12: Maths 1: Calculus

Lecture 1: Introduction Calculus

Lecture 2: Limits

Lecture 3: Derivatives

Lecture 4: Extrema

Lecture 5: Integrals

Chapter 13: Maths 2: Linear Algebra

Lecture 1: Introduction Linear Algebra

Lecture 2: Vector and matrix operations

Lecture 3: Matrix Multiplication

Lecture 4: Matrix inverse

Lecture 5: Eigenvalues and eigenvector

Chapter 14: Project: Customer segmentation (PCA)

Lecture 1: Introduction to PCA (Principal Component Analysis)

Lecture 2: Customer segmentation solution

Chapter 15: Project: Spam detection (Bayes)

Lecture 1: Introduction to Bayes

Lecture 2: Spam detection solution

Chapter 16: Sources and further reading

Lecture 1: Abbreviations

Lecture 2: Sources, links and further reading

Lecture 3: Crossword Puzzle Solver – Implementation Concept

Instructors

Rating Distribution

  • 1 stars: 5 votes
  • 2 stars: 3 votes
  • 3 stars: 8 votes
  • 4 stars: 11 votes
  • 5 stars: 8 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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