## Machine Learning ML Bootcamp Python TensorFlow Colab

### What you’ll 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

### Requirements

No prerequisites

### 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.MathsCalculusLinear AlgebraProbability theoryStatisticsMethodsMachine learning librariesScikit-learnTensorflowKeras Estimators & PredictorsNeural Network (Deep Learning)Support Vector MachineK-Nearest NeighborDecision Treeand many moreConcepts & techniquesPrincipal 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 moreMachinePythonCloud ComputingColab Cloud NotebookThese three building blocks will give you the deep understanding of the subject. Machine LearningSupervised learningRegressionClassificationUnsupervised learningReinforcement learningFurthermore projects will provide insights into real life solutions.ProjectsTitanic dataset (binary classification)Boston Housing dataset (regression)Student performance (binary classification)Hand-written digits (image recognition & generation)Stock market predictionsText recognition and language translationAutonomous driving (reinforcement learning)Mastering the game of GO (deep reinforcement learning)Segmentation of customer data (PCA)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!

### Overview

Section 1: Introduction & Overview

Lecture 1 Introduction

Lecture 2 ML in a Nutshell

Lecture 3 Linear Regression

Section 2: Methods 0: Scikit-learn

Lecture 4 Overview

Lecture 5 Data transformation and splitting

Lecture 6 Estimators

Lecture 7 Metrics

Lecture 8 Example: IRIS data set

Section 3: Project: Titanic (Binary Classification)

Lecture 9 Titanic Dataset

Lecture 10 Data exploration

Lecture 11 Data transformation and ML model

Section 4: Project: Boston Housing (Regression)

Lecture 12 Boston Housing solution

Section 5: Project: Student Performance (Binary Classification)

Lecture 13 Student Performance solution

Section 6: Methods 1: Neural Networks

Lecture 14 Concepts

Lecture 15 Forwardpropagation

Lecture 16 Backpropagation

Lecture 17 Activation Functions

Lecture 18 Loss Functions

Section 7: Methods 2: Tensorflow

Lecture 19 Overview

Lecture 20 Implementing a neural network

Lecture 21 Example: MNIST Fashion

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

Lecture 22 Introduction to GAN, CNN and DCGAN

Lecture 23 DCGAN MINST solution

Section 9: Project: Stock market prediction (LSTM)

Lecture 24 Introduction to time series data, RNN and LSTM

Lecture 25 LSTM solution

Section 10: Project: Language Translation (NMT)

Lecture 26 Introduction to Sequence-to-sequence (seq2seq) models

Lecture 27 NMT solution

Section 11: Reinforcement Learning

Lecture 28 Overview

Lecture 29 Autonomous driving

Lecture 30 Master the game of Go

Lecture 31 Whitepaper AlphaGo Zero

Section 12: Maths 1: Calculus

Lecture 32 Introduction Calculus

Lecture 33 Limits

Lecture 34 Derivatives

Lecture 35 Extrema

Lecture 36 Integrals

Section 13: Maths 2: Linear Algebra

Lecture 37 Introduction Linear Algebra

Lecture 38 Vector and matrix operations

Lecture 39 Matrix Multiplication

Lecture 40 Matrix inverse

Lecture 41 Eigenvalues and eigenvector

Section 14: Project: Customer segmentation (PCA)

Lecture 42 Introduction to PCA (Principal Component Analysis)

Lecture 43 Customer segmentation solution

Section 15: Project: Spam detection (Bayes)

Lecture 44 Introduction to Bayes

Lecture 45 Spam detection solution

Section 16: Sources and further reading

Lecture 46 Abbreviations

Lecture 47 Sources, links and further reading

Lecture 48 Crossword Puzzle Solver – Implementation Concept

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

#### Course Information:

Udemy | English | 4h 53m | 4.18 GB

Created by: Samuel Reischl

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