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

Master the 3 M's of ML: Maths, Methods and Machine

4.25 (35 reviews)
Udemy
platform
English
language
Data Science
category
instructor
6,913
students
5 hours
content
Sep 2019
last update
$44.99
regular price

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

Description

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 projects will 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!

Content

Introduction & Overview

Introduction
ML in a Nutshell
Linear Regression

Methods 0: Scikit-learn

Overview
Data transformation and splitting
Estimators
Metrics
Example: IRIS data set
Quiz

Project: Titanic (Binary Classification)

Titanic Dataset
Data exploration
Data transformation and ML model

Project: Boston Housing (Regression)

Boston Housing solution

Project: Student Performance (Binary Classification)

Student Performance solution

Methods 1: Neural Networks

Concepts
Forwardpropagation
Backpropagation
Activation Functions
Loss Functions
Neural Networks

Methods 2: Tensorflow

Overview
Implementing a neural network
Example: MNIST Fashion
Tensorflow & KERAS

Project: MNIST hand-written digits (DCGAN)

Introduction to GAN, CNN and DCGAN
DCGAN MINST solution
DCGAN

Project: Stock market prediction (LSTM)

Introduction to time series data, RNN and LSTM
LSTM solution
Time series data, RNN and LSTM

Project: Language Translation (NMT)

Introduction to Sequence-to-sequence (seq2seq) models
NMT solution
Language translation

Reinforcement Learning

Overview
Autonomous driving
Master the game of Go
Whitepaper AlphaGo Zero

Maths 1: Calculus

Introduction Calculus
Limits
Limits
Derivatives
Extrema
Integrals

Maths 2: Linear Algebra

Introduction Linear Algebra
Vector and matrix operations
Matrix Multiplication
Matrix inverse
Eigenvalues and eigenvector

Project: Customer segmentation (PCA)

Introduction to PCA (Principal Component Analysis)
Customer segmentation solution
PCA & Clustering

Project: Spam detection (Bayes)

Introduction to Bayes
Spam detection solution
Bayes

Sources and further reading

Abbreviations
Sources, links and further reading
Crossword Puzzle Solver - Implementation Concept

Screenshots

Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,.. - Screenshot_01Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,.. - Screenshot_02Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,.. - Screenshot_03Machine Learning (ML) Bootcamp: Python, TensorFlow, Colab,.. - Screenshot_04

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2229568
udemy ID
2/19/2019
course created date
11/23/2019
course indexed date
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course submited by