Merge branch 'main_upstream'

.gitignore

@@ -3,6 +3,8 @@
 ##
 ## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore
 
+dist 
+
 # User-specific files
 *.rsuser
 *.suo

1-Introduction/1-intro-to-ML/README.md

@@ -1,12 +1,16 @@
 # Introduction to machine learning
 
+<!--
+Watch the video, then take the pre-lesson quiz
+-->
+
 [![ML, AI, deep learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
 
 > 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
 
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
-***
+---
 
 Welcome to this course on classical machine learning for beginners! Whether you're completely new to this topic, or an experienced ML practitioner looking to brush up on an area, we're happy to have you join us! We want to create a friendly launching spot for your ML study and would be happy to evaluate, respond to, and incorporate your [feedback](https://github.com/microsoft/ML-For-Beginners/discussions).
 
@@ -14,7 +18,8 @@ Welcome to this course on classical machine learning for beginners! Whether you'
 
 > 🎥 Click the image above for a video: MIT's John Guttag introduces machine learning
 
-# Getting started with machine learning
+---
+## Getting started with machine learning
 
 Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally.
 
@@ -24,42 +29,52 @@ Before starting with this curriculum, you need to have your computer set up and
 - **Create a GitHub account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too 😊)
 - **Explore Scikit-learn**. Familiarize yourself with [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons.
 
-# What is machine learning?
+---
+## What is machine learning?
 
 The term 'machine learning' is one of the most popular and frequently used terms of today. There is a nontrivial possibility that you have heard this term at least once if you have some sort of familiarity with technology, no matter what domain you work in. The mechanics of machine learning, however, are a mystery to most people. For a machine learning beginner, the subject can sometimes feel overwhelming. Therefore, it is important to understand what machine learning actually is, and to learn about it step by step, through practical examples.
 
-# The hype curve
+---
+## The hype curve
 
 ![ml hype curve](images/hype.png)
 
 > Google Trends shows the recent 'hype curve' of the term 'machine learning'
 
-# A mysterious universe
+---
+## A mysterious universe
 
 We live in a universe full of fascinating mysteries. Great scientists such as Stephen Hawking, Albert Einstein, and many more have devoted their lives to searching for meaningful information that uncovers the mysteries of the world around us. This is the human condition of learning: a human child learns new things and uncovers the structure of their world year by year as they grow to adulthood.
 
-# The child's brain
+---
+## The child's brain
 
 A child's brain and senses perceive the facts of their surroundings and gradually learn the hidden patterns of life which help the child to craft logical rules to identify learned patterns. The learning process of the human brain makes humans the most sophisticated living creature of this world. Learning continuously by discovering hidden patterns and then innovating on those patterns enables us to make ourselves better and better throughout our lifetime. This learning capacity and evolving capability is related to a concept called [brain plasticity](https://www.simplypsychology.org/brain-plasticity.html). Superficially, we can draw some motivational similarities between the learning process of the human brain and the concepts of machine learning.
 
-# The human brain
+---
+## The human brain
 
 The [human brain](https://www.livescience.com/29365-human-brain.html) perceives things from the real world, processes the perceived information, makes rational decisions, and performs certain actions based on circumstances. This is what we called behaving intelligently. When we program a facsimile of the intelligent behavioral process to a machine, it is called artificial intelligence (AI).
 
-# Some terminology
+---
+## Some terminology
 
 Although the terms can be confused, machine learning (ML) is an important subset of artificial intelligence. **ML is concerned with using specialized algorithms to uncover meaningful information and find hidden patterns from perceived data to corroborate the rational decision-making process**.
 
-# AI, ML, Deep Learning
+---
+## AI, ML, Deep Learning
 
 ![AI, ML, deep learning, data science](images/ai-ml-ds.png)
 
 > A diagram showing the relationships between AI, ML, deep learning, and data science. Infographic by [Jen Looper](https://twitter.com/jenlooper) inspired by [this graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
-# Concepts to cover
+---
+## Concepts to cover
+
 In this curriculum, we are going to cover only the core concepts of machine learning that a beginner must know. We cover what we call 'classical machine learning' primarily using Scikit-learn, an excellent library many students use to learn the basics.  To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable, and so we would like to offer it here.
 
-# In this course you will learn:
+---
+## In this course you will learn:
 
 - core concepts of machine learning
 - the history of ML
@@ -72,7 +87,8 @@ In this curriculum, we are going to cover only the core concepts of machine lear
 - reinforcement learning
 - real-world applications for ML
 
-# What we will not cover
+---
+## What we will not cover
 
 - deep learning
 - neural networks
@@ -80,7 +96,8 @@ In this curriculum, we are going to cover only the core concepts of machine lear
 
 To make for a better learning experience, we will avoid the complexities of neural networks, 'deep learning' - many-layered model-building using neural networks - and AI, which we will discuss in a different curriculum. We also will offer a forthcoming data science curriculum to focus on that aspect of this larger field.
 
-# Why study machine learning?
+---
+## Why study machine learning?
 
 Machine learning, from a systems perspective, is defined as the creation of automated systems that can learn hidden patterns from data to aid in making intelligent decisions.
 
@@ -88,11 +105,13 @@ This motivation is loosely inspired by how the human brain learns certain things
 
 ✅ Think for a minute why a business would want to try to use machine learning strategies vs. creating a hard-coded rules-based engine.
 
-# Applications of machine learning
+---
+## Applications of machine learning
 
 Applications of machine learning are now almost everywhere, and are as ubiquitous as the data that is flowing around our societies, generated by our smart phones, connected devices, and other systems. Considering the immense potential of state-of-the-art machine learning algorithms, researchers have been exploring their capability to solve multi-dimensional and multi-disciplinary real-life problems with great positive outcomes.
 
-# Examples of applied ML
+---
+## Examples of applied ML
 
 **You can use machine learning in many ways**:
 
@@ -103,7 +122,8 @@ Applications of machine learning are now almost everywhere, and are as ubiquitou
 
 Finance, economics, earth science, space exploration, biomedical engineering, cognitive science, and even fields in the humanities have adapted machine learning to solve the arduous, data-processing heavy problems of their domain.
 
-# Conclusion
+---
+## Conclusion
 
 Machine learning automates the process of pattern-discovery by finding meaningful insights from real-world or generated data. It has proven itself to be highly valuable in business, health, and financial applications, among others.
 
@@ -116,12 +136,14 @@ Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com
 
 # [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
+---
 # Review & Self Study
 
 To learn more about how you can work with ML algorithms in the cloud, follow this [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
 
 Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) about the basics of ML.
 
+---
 # Assignment
 
 [Get up and running](assignment.md)

1-Introduction/2-history-of-ML/README.md

@@ -5,41 +5,55 @@
 
 ## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
+---
+
 In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
 
 The history of artificial intelligence (AI) as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmic, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'
 
+---
 ## Notable discoveries
 
 - 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occurring based on prior knowledge.
 - 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about  in our Regression unit, helps in data fitting.
 - 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain), named after Russian mathematician Andrey Markov, is used to describe a sequence of possible events based on a previous state.
 - 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
+
+---
+
 - 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to  detect patterns.
 - 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network).
 - 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs.
 
 ✅ Do a little research. What other dates stand out as pivotal in the history of ML and AI?
+
+---
 ## 1950: Machines that think
 
 Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think.' He grappled with naysayers and his own need for empirical evidence of this concept in part by creating the [Turing Test](https://www.bbc.com/news/technology-18475646), which you will explore in our NLP lessons.
 
+---
 ## 1956: Dartmouth Summer Research Project
 
 "The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," and it was here that the term 'artificial intelligence' was coined ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
 
 > Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
 
+---
+
 The lead researcher, mathematics professor John McCarthy, hoped "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." The participants included another luminary in the field, Marvin Minsky.
 
 The workshop is credited with having initiated and encouraged several discussions including "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems." ([source](https://wikipedia.org/wiki/Dartmouth_workshop)).
 
+---
 ## 1956 - 1974: "The golden years"
 
 From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
 
 natural language processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
 
+---
+
 Research was well funded by government agencies, advances were made in computation and algorithms, and prototypes of intelligent machines were built. Some of these machines include:
 
 * [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), who could maneuver and decide how to perform tasks 'intelligently'.
@@ -47,21 +61,26 @@ Research was well funded by government agencies, advances were made in computati
     ![Shakey, an intelligent robot](images/shakey.jpg)
     > Shakey in 1972
 
+---
+
 * Eliza, an early 'chatterbot', could converse with people and act as a primitive 'therapist'. You'll learn more about Eliza in the NLP lessons.
 
     ![Eliza, a bot](images/eliza.png)
     > A version of Eliza, a chatbot
 
+---
+
 * "Blocks world" was an example of a micro-world where blocks could be stacked and sorted, and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward.
 
     [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
     
     > 🎥 Click the image above for a video: Blocks world with SHRDLU
 
+---
 ## 1974 - 1980: "AI Winter"
 
 By the mid 1970s, it had become apparent that the complexity of making 'intelligent machines' had been understated and that its promise, given the available compute power, had been overblown. Funding dried up and confidence in the field slowed. Some issues that impacted confidence included:
-
+---
 - **Limitations**. Compute power was too limited.
 - **Combinatorial explosion**. The amount of parameters needed to be trained grew exponentially as more was asked of computers, without a parallel evolution of compute power and capability.
 - **Paucity of data**. There was a paucity of data that hindered the process of testing, developing, and refining algorithms.
@@ -69,8 +88,11 @@ By the mid 1970s, it had become apparent that the complexity of making 'intellig
   - Turing tests came into question by means, among other ideas, of the 'chinese room theory' which posited that, "programming a digital computer may make it appear to understand language but could not produce real understanding." ([source](https://plato.stanford.edu/entries/chinese-room/))
   - The ethics of introducing artificial intelligences such as the "therapist" ELIZA into society was challenged.
 
+---
+
 At the same time, various AI schools of thought began to form. A dichotomy was established between ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) practices. _Scruffy_ labs tweaked programs for hours until they had the desired results. _Neat_ labs "focused on logic and formal problem solving". ELIZA and SHRDLU were well-known _scruffy_ systems. In the 1980s, as demand emerged to make ML systems reproducible, the _neat_ approach gradually took the forefront as its results are more explainable.
 
+---
 ## 1980s Expert systems
 
 As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)).
@@ -79,18 +101,23 @@ This type of system is actually _hybrid_, consisting partially of a rules engine
 
 This era also saw increasing attention paid to neural networks.
 
+---
 ## 1987 - 1993: AI 'Chill'
 
 The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data.
 
+---
 ## 1993 - 2011
 
 This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline.
 
+---
 ## Now
 
 Today machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
 
+---
+
 It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
 
 [![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
@@ -103,6 +130,7 @@ Dig into one of these historical moments and learn more about the people behind
 
 ## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
+---
 ## Review & Self Study
 
 Here are items to watch and listen to:
@@ -111,6 +139,8 @@ Here are items to watch and listen to:
 
 [![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
 
+---
+
 ## Assignment
 
 [Create a timeline](assignment.md)

quiz-app/package.json

@@ -10,7 +10,7 @@
   "dependencies": {
     "core-js": "^3.6.5",
     "vue": "^2.6.11",
-    "vue-i18n": "^8.22.2",
+    "vue-i18n": "^8.26.5",
     "vue-router": "^3.4.9"
   },
   "devDependencies": {

quiz-app/src/App.vue

@@ -4,10 +4,12 @@
       <router-link class="navlink" to="/">Home</router-link>
       <label for="locale">locale</label>
       <select v-model="locale">
+        <option>br</option>
         <option>en</option>
-        <option>tr</option>
         <option>fr</option>
+        <option>it</option>
         <option>ja</option>
+        <option>tr</option>
       </select>
     </nav>
     <div id="app">

quiz-app/src/assets/translations/en.json

@@ -2927,3 +2927,4 @@
     ]
   }
 ]
+

translations/README.ms.md

@@ -0,0 +1,129 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# Pembelajaran Mesin untuk Pemula - Kurikulum
+
+> 🌍 Mengembara ke seluruh dunia semasa kita meneroka Pembelajaran Mesin melalui budaya dunia 🌍
+
+Azure Cloud Advocates di Microsoft dengan senang hati menawarkan kurikulum 12-minggu, 24-pelajaran (ditambah satu!) Mengenai**Pembelajaran Mesin**. Dalam kurikulum ini, anda akan belajar tentang apa yang kadang-kadang disebut**pembelajaran mesin klasik**, menggunakan terutamanya Scikit-learning sebagai perpustakaan dan mengelakkan pembelajaran mendalam, yang dicakup dalam kurikulum 'AI for Beginners' yang akan datang. Pasangkan pelajaran ini dengan ['Data Science for Beginners' kurikulum](https://aka.ms/datascience-beginners) kami juga!
+
+Perjalanan bersama kami di seluruh dunia kerana kami menerapkan teknik klasik ini ke data dari banyak kawasan di dunia. Setiap pelajaran merangkumi kuiz sebelum dan sesudah pelajaran, arahan bertulis untuk menyelesaikan pelajaran, penyelesaian, tugasan dan banyak lagi. Pedagogi berasaskan projek kami membolehkan anda belajar sambil membina, cara yang terbukti untuk kemahiran baru 'melekat'.
+
+**✍️ Terima kasih kepada penulis kami**Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, dan Amy Boyd
+
+**🎨 Terima kasih juga kepada ilustrator kami**Tomomi Imura, Dasani Madipalli, dan Jen Looper
+
+**🙏 Terima kasih khas 🙏 kepada pengarang, pengulas dan penyumbang kandungan Duta Pelajar Microsoft kami**, terutamanya Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, dan Snigdha Agarwal
+
+**🤩 Terima kasih yang tidak terhingga kepada Duta Pelajar Microsoft Eric Wanjau atas pelajaran R kami!**
+
+---
+
+# Bermula
+
+**Pelajar**, untuk menggunakan kurikulum ini, garpu seluruh repo ke akaun GitHub anda sendiri dan selesaikan latihan anda sendiri atau bersama kumpulan:
+
+- Mulakan dengan kuiz pra-kuliah.
+- Baca kuliah dan selesaikan aktiviti, berhenti sebentar dan renungkan pada setiap pemeriksaan pengetahuan.
+- Cuba buat projek dengan memahami pelajaran daripada menjalankan kod penyelesaian; namun kod itu terdapat di folder `/solution` dalam setiap pelajaran berorientasikan projek.
+- Ikuti kuiz pasca kuliah.
+- Selesaikan cabaran.
+- Selesaikan tugasan.
+- Setelah menyelesaikan kumpulan pelajaran, lawati [Discussion board](https://github.com/microsoft/ML-For-Beginners/discussions) dan "belajar dengan kuat" dengan mengisi rubrik PAT yang sesuai. 'PAT' adalah Alat Penilaian Kemajuan yang merupakan rubrik yang anda isi untuk melanjutkan pembelajaran anda. Anda juga boleh bertindak balas terhadap PAT lain sehingga kami dapat belajar bersama.
+
+> Untuk kajian lebih lanjut, kami mengesyorkan mengikuti [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) berikut dan jalan belajar.
+
+**Guru**, kami telah [memasukkan beberapa cadangan](for-teachers.md) mengenai cara menggunakan kurikulum ini.
+
+
+## Jumpa pasukan
+
+[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+
+> 🎥 Klik gambar di atas untuk video mengenai projek dan orang yang membuatnya!
+
+---
+
+## Pedagogi
+
+Kami telah memilih dua prinsip pedagogi semasa membina kurikulum ini: memastikan bahawa ia adalah **berasaskan projek** dan merangkumi **kuiz yang kerap**. Di samping itu, kurikulum ini mempunyai **tema umum** untuk memberikannya kesatuan.
+
+Dengan memastikan bahawa kandungan sesuai dengan projek, proses dibuat lebih menarik bagi pelajar dan pengekalan konsep akan ditambah. Di samping itu, kuiz bertaraf rendah sebelum kelas menetapkan niat pelajar untuk mempelajari sesuatu topik, sementara kuiz kedua selepas kelas memastikan pengekalan selanjutnya. Kurikulum ini dirancang agar fleksibel dan menyenangkan dan dapat diambil secara keseluruhan atau sebahagian. Projek bermula kecil dan menjadi semakin rumit pada akhir kitaran 12 minggu. Kurikulum ini juga termasuk skrip tulisan mengenai aplikasi ML dunia nyata, yang dapat digunakan sebagai kredit tambahan atau sebagai dasar perbincangan.
+
+> Cari garis panduan [Kod Tingkah Laku](CODE_OF_CONDUCT.md) kami, [Menyumbang](CONTRIBUTING.md), dan [Terjemahan](TRANSLATIONS.md). Kami mengalu-alukan maklum balas yang membina!
+
+
+## Setiap pelajaran merangkumi:
+
+- nota lakaran pilihan
+- video tambahan pilihan
+- kuiz pemanasan sebelum kuliah
+- pelajaran bertulis
+- untuk pelajaran berasaskan projek, panduan langkah demi langkah bagaimana membina projek
+- pemeriksaan pengetahuan
+- satu cabaran
+- bacaan tambahan
+- tugasan
+- kuiz pasca kuliah
+
+
+> **Catatan mengenai bahasa**: Pelajaran ini terutama ditulis dalam Python, tetapi banyak juga tersedia dalam R. Untuk menyelesaikan pelajaran R, pergi ke folder `/solution` dan cari pelajaran R. Mereka termasuk pelanjutan .rmd yang mewakili fail **R Markdown** yang hanya dapat didefinisikan sebagai penyisipan `potongan kode '(dari R atau bahasa lain) dan` header YAML` (yang membimbing cara memformat output seperti PDF) dalam `Markdown document`. Oleh itu, ia berfungsi sebagai kerangka penulisan teladan bagi sains data kerana ia membolehkan anda menggabungkan kod, output dan pemikiran anda dengan membolehkan anda menuliskannya dalam Markdown. Lebih-lebih lagi, dokumen R Markdown dapat diberikan ke format output seperti PDF, HTML, atau Word.
+
+> **Catatan mengenai kuiz**: Semua kuiz terkandung [dalam aplikasi ini](https://white-water-09ec41f0f.azurestaticapps.net/), untuk 50 keseluruhan kuiz masing-masing dari tiga soalan. Mereka dihubungkan dari dalam pelajaran tetapi aplikasi kuiz dapat dijalankan secara tempatan; ikuti arahan dalam folder `quiz-app`.
+
+
+| Nombor Pelajaran |                            Topik                               |                   Pengumpulan Pelajaran                        | Objektif Pembelajaran                                                                                                           |                   Pautan Pembelajaran                                                                                                           |     Pengarang                                      |
+|:-------------:|:--------------------------------------------------------------:|:--------------------------------------------------------:|------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------:|
+|       01      |                  Pengenalan pembelajaran mesin                 |       [Pengenalan](../1-Introduction/README.md)             | Ketahui konsep asas di sebalik pembelajaran mesin                                                                                     |    [Pelajaran](../1-Introduction/1-intro-to-ML/README.md)                                                                                       |    Muhammad                                         |
+|       02      |               Sejarah pembelajaran mesin                       |       [Pengenalan](../1-Introduction/README.md)             | Ketahui sejarah yang mendasari bidang ini                                                                                             |   [Pelajaran](../1-Introduction/2-history-of-ML/README.md)                                                                                      |   Jen and Amy                                       |
+|       03      |                Keadilan dan pembelajaran mesin                 |       [Pengenalan](../1-Introduction/README.md)             | Apakah masalah falsafah penting mengenai keadilan yang harus dipertimbangkan oleh pelajar semasa membina dan menggunakan model ML?    |     [Pelajaran](../1-Introduction/3-fairness/README.md)                                                                                         |     Tomomi                                          |
+|       04      |               Teknik untuk pembelajaran mesin                  |       [Pengenalan](../1-Introduction/README.md)             | Teknik apa yang digunakan oleh penyelidik ML untuk membina model ML?                                                                  | [Pelajaran](../1-Introduction/4-techniques-of-ML/README.md)                                                                                     |  Chris and Jen                                      |
+|       05      |                 Pengenalan regresi                             |         [Regresi](../2-Regression/README.md)                | Mulakan dengan Python dan Scikit-belajar untuk model regresi                                                                          |       <ul><li>[Python](../2-Regression/1-Tools/README.md)</li><li>[R](../2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)</li></ul>            |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
+|       06      |               Harga labu Amerika Utara 🎃                      |         [Regresi](../2-Regression/README.md)                | Visualisasikan dan bersihkan data sebagai persediaan untuk ML                                                                         |     <ul><li>[Python](../2-Regression/2-Data/README.md)</li><li>[R](../2-Regression/2-Data/solution/R/lesson_2-R.ipynb)</li></ul>                |        <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
+|       07      |               Harga labu Amerika Utara 🎃                      |         [Regresi](../2-Regression/README.md)                | Membina model regresi linear dan polinomial                                                                                           |     <ul><li>[Python](../2-Regression/3-Linear/README.md)</li><li>[R](../2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul>            |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
+|       08      |               Harga labu Amerika Utara 🎃                      |         [Regresi](../2-Regression/README.md)                | Bina model regresi logistik                                                                                                            |     <ul><li>[Python](../2-Regression/4-Logistic/README.md)  </li><li>[R](../2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul>     |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>     |
+|       09      |                         Aplikasi Web 🔌                        |            [Aplikasi Web](../3-Web-App/README.md)           | Bina aplikasi web untuk menggunakan model terlatih anda                                                                               |        [Python](../3-Web-App/1-Web-App/README.md)                                                                                               |       Jen                                           |
+|       10      |               Pengenalan klasifikasi                           |    [Pengelasan](../4-Classification/README.md)              | Bersihkan, persiapkan, dan gambarkan data anda; pengenalan klasifikasi                                                                |<ul><li>  [Python](../4-Classification/1-Introduction/README.md) </li><li>[R](../4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb)   | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|       11      |            Masakan Asia dan India yang lazat 🍜                |     [Pengelasan](../4-Classification/README.md)             | Pengenalan kepada pengelasan                                                                                                          |<ul><li> [Python](../4-Classification/2-Classifiers-1/README.md)</li><li>[R](../4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb)   | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|       12      |            Masakan Asia dan India yang lazat 🍜                |     [Pengelasan](../4-Classification/README.md)             | Lebih banyak pengelasan                                                                                                                |<ul><li>  [Python](../4-Classification/3-Classifiers-2/README.md)</li><li>[R](../4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
+|       13      |            Masakan Asia dan India yang lazat 🍜                |     [Pengelasan](../4-Classification/README.md)             | Bina aplikasi web cadangan menggunakan model anda                                                                                      |     [Python](../4-Classification/4-Applied/README.md)                                                                                          |       Jen                                           |
+|       14      |                 Pengenalan pengelompokan                       |         [Penggabungan](../5-Clustering/README.md)           | Bersihkan, persiapkan, dan gambarkan data anda; Pengenalan pengelompokan                                                              |     <ul><li> [Python](5-Clustering/1-Visualize/README.md)</li><li>[R](../5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb)                 |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
+|       15      |             Meneroka Selera Muzik Nigeria 🎧                   |         [Penggabungan](../5-Clustering/README.md)           | Terokai kaedah pengelompokan K-Means                                                                                                  |      <ul><li> [Python](../5-Clustering/2-K-Means/README.md)</li><li>[R](../5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb)                 |       <ul><li>Jen</li><li>Eric Wanjau</li></ul>    |
+|       16      |        Pengenalan pemprosesan bahasa semula jadi ☕️           |    [Pemprosesan bahasa semula jadi](../6-NLP/README.md)     | Ketahui asas mengenai NLP dengan membina bot sederhana                                                                                |    [Python](../6-NLP/1-Introduction-to-NLP/README.md)                                                                                           |     Stephen                                        |
+|       17      |                     Tugas NLP biasa ☕️                        |    [Pemprosesan bahasa semula jadi](../6-NLP/README.md)     | Memperdalam pengetahuan NLP anda dengan memahami tugas-tugas umum yang diperlukan ketika berurusan dengan struktur bahasa              |           [Python](../6-NLP/2-Tasks/README.md)                                                                                                 |     Stephen                                        |
+|       18      |            Analisis terjemahan dan sentimen ♥                  ️|    [Pemprosesan bahasa semula jadi](../6-NLP/README.md)     | Terjemahan dan analisis sentimen dengan Jane Austen                                                                                   |   [Python](../6-NLP/3-Translation-Sentiment/README.md)                                                                                          |     Stephen                                        |
+|       19      |                 Hotel romantis di Eropah ♥                     ️|    [Pemprosesan bahasa semula jadi](../6-NLP/README.md)     | Analisis sentimen dengan ulasan hotel                                                                                                 |      [Python](../6-NLP/4-Hotel-Reviews-1/README.md)                                                                                             |     Stephen                                        |
+|       20      |                 Hotel romantis di Eropah ♥                     ️|    [Pemprosesan bahasa semula jadi](../6-NLP/README.md)     | Analisis sentimen dengan ulasan hotel 2                                                                                               |      [Python](../6-NLP/5-Hotel-Reviews-2/README.md)                                                                                             |     Stephen                                        |
+|       21      |           Pengenalan ramalan siri masa                         |        [Siri masa](../7-TimeSeries/README.md)               | Pengenalan ramalan siri masa                                                                                                          |    [Python](../7-TimeSeries/1-Introduction/README.md)                                                                                           |    Francesca                                       |
+|       22      | ⚡️ Penggunaan Kuasa Dunia ⚡️ - ramalan siri masa dengan ARIMA  |        [Siri masa](../7-TimeSeries/README.md)              | Ramalan siri masa dengan ARIMA                                                                                                         |        [Python](../7-TimeSeries/2-ARIMA/README.md)                                                                                             |    Francesca                                       |
+|       23      | World️ Penggunaan Kuasa Dunia ⚡️ - ramalan siri masa dengan SVR |        [Siri masa](../7-TimeSeries/README.md)              | Ramalan siri masa dengan Regressor Vektor Sokongan                                                                                      |        [Python](../7-TimeSeries/3-SVR/README.md)                                                                                              |    Anirban                                         |
+|       24      |           Pengenalan pembelajaran pengukuhan                   | [Pembelajaran pengukuhan](../8-Reinforcement/README.md)    | Pengenalan pembelajaran pengukuhan dengan Q-Learning                                                                                     |    [Python](../8-Reinforcement/1-QLearning/README.md)                                                                                        |     Dmitry                                         |
+|       25      |                Tolong Peter mengelakkan serigala! 🐺           | [Pembelajaran pengukuhan](../8-Reinforcement/README.md)     | Gym pembelajaran pengukuhan                                                                                                             |       [Python](../8-Reinforcement/2-Gym/README.md)                                                                                            |     Dmitry                                        |
+|   Poskrip     |          Senario dan aplikasi ML Dunia Sebenar                 |       [ML di Alam Nyata](../9-Real-World/README.md)        | Aplikasi ML klasik yang menarik dan mendedahkan                                                                                         |    [Pelajaran](../9-Real-World/1-Applications/README.md)                                                                                       |      Pasukan                                      |
+
+## Akses luar talian
+
+Anda boleh menjalankan dokumentasi ini di luar talian dengan menggunakan [Docsify](https://docsify.js.org/#/). 'Fork' repo ini, [pasang Docsify](https://docsify.js.org/#/quickstart) pada mesin tempatan anda, dan kemudian di folder root repo ini, ketik `docsify serve`. Laman web akan dilayan di port 3000 di localhost anda: `localhost: 3000`.
+
+## PDF
+
+Cari pdf kurikulum dengan pautan [di sini](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf).
+
+## Pertolongan diperlukan!
+
+Adakah anda ingin menyumbang terjemahan? Sila baca [pedoman terjemahan](TRANSLATIONS.md)  kami dan tambahkan input [di sini](https://github.com/microsoft/ML-For-Beginners/issues/71).
+
+## Kurikulum Lain
+
+Pasukan kami menghasilkan kurikulum lain! Lihat:
+
+- [Peranti Web untuk Pemula](https://aka.ms/webdev-beginners)
+- [IoT untuk Pemula](https://aka.ms/iot-beginners)
+- [Sains Data untuk Pemula](https://aka.ms/datascience-beginners)