What You Need to Know About Machine Learning in 2019

Let’s talk about the 6 steps to becoming a machine learning expert in 2019. This was inspired by our team’s machine learning masterclass, which launched this week.

1. Python Programming

Python Code

Any learner who wants to learn machine learning the practical way, rather than just the theory, should first learn how to code in Python. Being a more concise and clean language than others like Java and C#, Python is easy to learn for even someone who has never coded before.

2. Machine Learning Concepts

Hand Reaching from Computer
Once you’ve learned Python, you’ll want to learn the fundamentals and theory on machine learning.

It is important to have a solid footing on what machine learning is, before you start making a specific project. This ensures you know all the terms and concepts mentioned when you’re studying a course or looking up documentation for your first actual project.

3. TensorFlow 2.0

TensorFlow Logo
TensorFlow is a library for machine learning research and production.

As it was developed by Google, it’s no surprise that TensorFlow is the most popular library. And it’s open source as well! TensorFlow 2.0 was just released, so make sure you’re not choosing an outdated tutorial using an earlier version. If you want to learn on the most current library, look for TensorFlow 2.0.

First you’ll want to learn TensorFlow fundamentals and get yourself familiar with the library and its syntax. The best way to learn is to make a few simple projects that are targeted for beginners and don’t overwhelm you.

4. Data Models and Algorithms

Data on Computer
Once you have a foundation of TensorFlow and a few easy projects under your belt, you can start making more advanced projects using data models and algorithms.

At this point, you should be able to create your own data pipeline for production. A lot of tutorials out there either target beginners or experts, so it can be hard to find a full tutorial series when you’re in that intermediate stage. Where you can find the projects, building examples is the best way to learn more in-depth how to work with data in various ways.

5. Neural Networks

Artificial Intelligence Head
After you’ve built several projects with TensorFlow, you can start implementing neural networks into projects.

This is where you get to dive into deep learning and use Python and TensorFlow together to create even more advanced projects. Make sure you try implementing various types of neural networks, including different types of unsupervised and supervised networks.

6. Real-World Projects

3D Model of Car
Once you have these 5 core skills, you can start building the real-world projects that we often see making headlines.

That means computer vision for image recognition. That means stock prediction. Fraud detection.

Machine learning has so many applications that you can dive into right away on a small scale. You can build your own version of Amazon and Netflix’s recommender systems. You can implement lane detection, car detection, and curve navigation for self-driving cars.

In the real world, these projects are handled by teams of people. While you’re learning, you can build a subset of such a project, or the project on a smaller scale, to show what you can do, and set yourself up to become one of the people on a machine learning team.

If you’re as serious about machine learning as we are, check out our team’s machine learning masterclass.

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The genre blends the exploration and scavenging elements of a survival game with last man standing gameplay. Complete with all art assets and source code as a resource, you’ll make a project to put front-page on your portfolio. Zero experience required.

Learn from Industry Leading Instructors

Straightforward teaching style, clean coding techniques, and thoughtful developer advice.

Straightforward teaching style, clean coding techniques, and thoughtful developer advice.

Glauco makes games in Unity, Unreal, and HTML and works with languages like C#, C++, and JavaScript. He has a decade’s experience in game development.

Step by step explanations, and eye for design with many tips

Kevin is our local 3D artist. He has made over 20 courses and mastered tools including Blender, Photoshop, Maya, MakeHuman, and Affinity.

Since 2009 the online instructor team Mammoth Interactive has created over 150 courses to 300’000+ students in 170 countries. We publish new courses every month in our mission to make education, professional development, and hobbies that involve learning accessible to anyone with a computer.

Learn with video tutorials, game development resources, and a curated curriculum

Learn with video tutorials, game development resources, and a curated curriculum

Mammoth Interactive is back with another huge masterclass library. Pledge now to meet your 5-star instructors.

Surprise yourself with how much you can achieve by taking this course. These days you should learn how to code. Building a game is a fun way to grow your personal skillset.

Master the fundamentals of the Unity game development interface

Master the fundamentals of the Unity game development interface

If you have no experience with ever coding, making a game, or trying the Unity game engine, start here. You’ll also learn to code in the widely used C# programming language. We’ll show you everything you need to know about the most popular game software.

Run, jump and prepare yourself for the bulletstorm to come! Have fun while learning to code and build your own Unity game that you can change and expand.

Create a character you can control and customize behavior

Create a character you can control and customize behavior

You’ve never taken an online course as engaging as this. If you want to learn to code without getting bored, this is the Kickstarter for you 🙂

Build a game scene

Build a game scene

Players need to feel like they are inside a true battle arena, intended only for the best of the best. Create a map with graphical effects and make it a strategy funzone in which players will fight! You will also learn how to make a  fort mechanic.

Learn to make weapons in Unity

Learn to make weapons in Unity

Begin this section by learning how to build simple pistols. We’ll make our weapons work at varying strength in all kinds of scenarios. We’ll design and code pistols, shotguns, snipers, rocket launchers and more! You will learn the tools to make any weapon imaginable.

Build your defenses and shoot at enemies who are trying to kill you.

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This is where we’ll wrap things up for the shooting arena. Players must have a sharp eye for the hazards to come, so there’s nothing better than having enemies to destroy!

Proceed into your game development journey and learn great new skills.

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The game you make in this course might be the next multimillion dollar hit like Fortnite.

Click To Pledge

Updates to Unreal Course – Unreal Engine 4

Learn Unreal and C++ as part of our epic bundle Build 177 Games: The Complete 2D, 3D and VR Bundle

1. Receiving errors about Box Component?

Add the line below to the top of the cpp file that is producing errors. #include "Components/BoxComponent.h"

This line creates a reference to access components in the BoxComponent.h script. (Unreal Engine 4.15 requires separate includes.)

2. To get UGameplayStatics to compile you need to add:

#include "Kismet/GameplayStatics.h"

3. I’m getting errors in Visual Studio. What do they mean?

Many times you can ignore Visual Studio’s error messages.

If your project builds successfully in Unreal, you are good to go.

Visual Studio can mark things in the source code as an error even though it’s not.

This occurs because when generating new source files, Unreal can get out of sync with Visual Studio.

To sync Visual Studio with Unreal, go File -> Refresh Visual Studio Project.

Thanks to Mammoth Interactive students James and Chris for helping with this question!

Learn Unreal and C++ as part of our epic bundle Build 177 Games: The Complete 2D, 3D and VR Bundle

Curriculum of our Android Studio, Java and TensorFlow course

Enroll now in our mobile machine learning masterclass to get all of these topics and learn from the comfort of your couch. Now dirt cheap at https://training.mammothinteractive.com/p/mobilemachinelearning/?product_id=509994&coupon_code=SPECIALBLOGCOUPON

Introduction to Machine Learning + Software

  • An interview with your instructor
  • Intro to the Course (9:56)
  • Update! Resources Folder

Intro to Android Studio

  • Intro and Topics List (2:25)
  • Downloading and Installing Android Studio (6:44)
  • Exploring Interface (12:12)
  • Setting up an Emulator and Running Project (6:43)

Intro to Java

  • Intro to Language Basics (2:46)
  • Variable Types (14:00)
  • Operations on Variables (10:49)
  • Array and Lists (9:26)
  • Array and List Operations (7:59)
  • If and Switch Statements (11:34)
  • While Loops (10:09)
  • For Loops (8:51)
  • Functions Intro (8:39)
  • Parameters and Return Values (7:05)
  • Classes and Objects Intro (12:13)
  • Superclass and Subclasses (11:42)
  • Static Variables and Axis Modifiers (7:27)

Intro to App Development

  • Intro to Android App Development (1:57)
  • Building Basic User Interface (12:15)
  • Connecting UI to Backend (6:12)
  • Implementing Backend and Tidying UI (9:09)

Intro to Machine Learning Concepts

  • Introduction to ML Concepts
  • Downloading and Installing PyCharm and Python (6:55)
  • Exploring PyCharm (7:48)

Python Language Basics

  • Intro and Topics List (2:40)
  • Intro to Variables (13:17)
  • Variables Operations and Conversions (12:35)
  • Collection Types (12:47)
  • Collections Operations (8:42)
  • Control Flow If Statements (12:50)
  • While and For Loops (10:44)
  • Functions (11:23)
  • Classes and Objects (15:40)
  • Source Code

Intro to TensorFlow

  • TensorFlow Intro (2:53)
  • Topics List (6:09)
  • Importing TensorFlow to PyCharm (4:25)
  • Constant Nodes and Sessions (9:01)
  • Variable Nodes (10:45)
  • Placeholder Nodes (7:35)
  • Operation Nodes (12:47)
  • Loss, Optimizers, and Training (11:56)
  • Building a Linear Regression Model (20:30)
  • Source Code

Machine Learning in Android Studio Projects

  • Introduction to Level 2 (5:15)

Introduction to Tensorflow Estimator

  • Introduction (3:08)
  • Topics List (4:12)
  • Setting up Prebuilt Estimator Model (15:15)
  • Evaluating and Predicting with Prebuilt Model (7:42)
  • Building Custom Estimator Function (10:12)
  • Testing Custom Estimator Function (7:00)
  • Summary and Model Comparison (9:46)
  • Source Code

Intro to Android ML Model Import

  • Intro and Demo (4:09)
  • Topics List (4:22)
  • Formatting and Saving Model (8:25)
  • Saving Optimized Graph File (14:48)
  • Starting Android Project (9:01)
  • Building UI (14:56)
  • Implementing Inference Functionality (9:14)
  • Testing and Error Fixing (11:01)
  • Source Files

Simple MNIST

  • Intro and Demo (3:50)
  • Topics List and Intro to MNIST Data (10:24)
  • Building Computational Graph (14:20)
  • Training and Testing Model (14:24)
  • Saving and Freezing Graph for Android Import (12:33)
  • Setting up Android Studio Project (13:07)
  • Building User Interface (15:58)
  • Loading Digit Images (10:02)
  • Formatting Image Data (10:59)
  • Making Prediction Using Model (7:32)
  • Displaying Results and Summary (13:13)
  • Source Files

MNIST With Estimator

  • Introduction (3:08)
  • Topics List (2:38)
  • Building Custom Estimator Function (15:34)
  • Building Input Functions, Training, and Testing (13:38)
  • Predicting Using Model and Model Comparisons (9:37)
  • Source Files

Build Image Recognition Apps

  • Introduction to Building Image Recognition Apps (6:34)

Weather Prediction

  • Intro and Demo (3:49)
  • Tasks List (4:36)
  • Retrieving the Data (14:00)
  • Formatting Data Sets (14:02)
  • Building Computational Graph (11:47)
  • Writing, Training, Testing, and Evaluating Functions (12:24)
  • Training, Testing, and Freezing the Model (9:48)
  • Setting up Android Project (8:05)
  • Building the UI (15:29)
  • Build App Backend and Project Summary (13:46)
  • Source Code

Text Prediction

  • Intro and Demo (4:13)
  • Tasks List (3:17)
  • Processing Text Data (13:18)
  • Building Data Sets and Model Builder Function (16:16)
  • Building Computational Graph (8:37)
  • Writing, Training, and Testing Code (15:11)
  • Training, Testing, and Freezing Graph (12:27)
  • Setting up Android Project (7:41)
  • Setting up UI (5:19)
  • Setting up Vocab Dictionary (8:34)
  • Formatting Input and Running Through Model (7:55)
  • Source Code

Stock Market Prediction

  • Intro and Demo (3:47)
  • Task List (5:17)
  • Retrieving Data via RESTful API Call (16:30)
  • Parsing JSON Data Pycharm Style (6:37)
  • Formatting Data (15:45)
  • Building the Model (13:26)
  • Training and Testing the Model (9:54)
  • Freezing Graph (10:06)
  • Setting up Android Project (6:07)
  • Building UI (8:25)
  • Requesting Data Via AsyncTask (8:25)
  • Parsing JSON Data Android Style (12:04)
  • Running Inference and Displaying Results (17:42)
  • Source Code

Image Analysis with Keras

  • Introduction to Level 4 (9:44)

Simple CIFAR-10

  • Intro and Demo (4:47)
  • Topics List (3:47)
  • Exploring CIFAR-10 Dataset (10:48)
  • Update! CIFAR_10 Android Fix
  • Formatting Input Data (13:13)
  • Building the Model (16:24)
  • Freezing Graph and Training Model (16:56)
  • Setting up the Android Project (16:43)
  • Setting up UI (9:02)
  • Loading and Displaying Image (6:33)
  • Formatting Image Data for Model Input (13:55)
  • Predicting and Displaying Results (13:26)
  • Summary and Outro (6:39)
  • Source Code

Face Detection

  • Intro and Demo (3:20)
  • Tasks List (3:09)
  • Loading Face and Non Face Images (15:56)
  • Reformatting Input Data (11:12)
  • Build Model + Write, Train & Test Scripts (19:12)
  • Freeze Graph + Train & Test Model (14:38)
  • Setting up Android Project (11:49)
  • Setting up UI (7:52)
  • Loading and Display Images (10:11)
  • Formatting Data and Running Inference (12:47)
  • Displaying Results and Summary (8:51)
  • Source Code

Emotions Detection

  • Intro and Demo (3:39)
  • Tasks List (2:34)
  • Loading and Formatting Data (11:13)
  • Build Training and Testing Datasets (6:52)
  • Building the Model (9:57)
  • Build Functions to Train, Test, & Predict (11:58)
  • Training and Testing the Model (11:06)
  • Setting up Android Project (6:33)
  • Importing and Displaying Images (6:25)
  • Convert Images and Run Inference (8:17)
  • Displaying Results and Summary (7:48)
  • Source Code

Increase Efficiency of Machine Learning Models

  • Intro to Increasing ML Efficiency (5:47)
  • Source Code

Introduction to Tensorflow Lite

  • Tensorflow Lite (10:19)

Text Summarizer

  • Introduction (6:22)
  • How a Model Is Built (13:08)
  • Training and Summarizing Mechanisms (9:31)
  • Training and Summarizing Code (7:44)
  • Testing the Model (5:27)
  • Source Code

Object Localization

  • Introduction (4:13)
  • Examining Project Code (15:05)
  • Testing with a Mobile Device (7:30)

Object Recognition

  • Introduction (7:30)
  • Examining Code (22:29)
  • Testing on Mobile Device (5:36)

Intro to Tensorboard

  • Introduction (2:55)
  • Examining Computational Graph In Tensorboard (13:46)
  • Analyzing Scalars and Histograms (13:01)
  • Modifying Model Parameters Across Multiple Runs (10:32)
  • Source Code

Advanced ML Concepts

  • Introduction (18:49)
  • Source Code

Advanced MNIST

  • Intro and Demo (3:42)
  • Topics List (3:41)
  • Building Neuron Functions (11:18)
  • Building the Convolutional Layers (11:51)
  • Building Dense, Dropout, and Readout Layers (14:38)
  • Loss & Optimizer Functions, Training, & Testing (19:51)
  • Optimizing Saved Graph (10:57)
  • Setting up Android Project (12:30)
  • Setting Up UI (10:58)
  • Load and Display Digit Images (6:14)
  • Formatting Model Input (13:52)
  • Displaying Results and Summary (11:11)
  • Source Files

Advanced CIFAR-100

  • Intro and Demo (3:18)
  • Tasks List (3:11)
  • Inputting and Formatting Data (10:50)
  • Building the Model (10:19)
  • Training, Testing, and Freezing Model (10:57)
  • Setting up Android Project (10:24)
  • Building UI (8:09)
  • Loading and Displaying Images (7:03)
  • Converting Image Data & Running Inference (7:37)
  • Summary and Outro (9:29)
  • Source Code

Image Recognition in iOS

  • Introduction and Demo (3:20)
  • Project Setup (6:59)
  • Displaying and Resizing Images (9:42)
  • Converting Image to Pixel Buffer (14:06)
  • Summary and Outro (8:07)
  • Source Code

Intro to iOS

  • Introduction (2:15)
  • Source Code

Xcode Intro

  • Downloading and Installing (6:22)
  • Exploring XCode’s Interface (15:40)

Swift Language Basics

  • Variables Intro (7:57)
  • Variable Operations (10:43)
  • Collections (8:57)
  • Control Flow (10:18)
  • Functions (5:28)
  • Classes and Objects (9:55)

iOS App Development Intro

  • Building App From Start to Finish (12:46)

Intro to CoreML

  • Introduction to CoreML (9:08)

iOS Tensorflow Model Import

  • Introduction (4:35)
  • Converting pb to mlmodel
  • File & Setting up Project (7:34)
  • Running Inference Through Model (9:58)
  • Testing and Summary (3:55)

Learn now in our master course with app developer Nimish Narang! 90% off today with this coupon: https://training.mammothinteractive.com/p/mobilemachinelearning/?product_id=509994&coupon_code=SPECIALBLOGCOUPON

Where to Learn Online

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