- ankitrathi

# The Machine Learning Curve

Source: __http://stamfordresearch.com/__

In the current era of open internet & MOOCs, its pretty easy to be perplexed by plethora of options available & its a challenge to decide from where to get information and grasp it in the most optimized way. Machine Learning beginners can easily relate to it, so many books, courses, lectures, presentations available on the internet but very little information on where to start. In 2012, when I started with my ML journey, not many options were available but things have changed a lot in last 4 years.

So, here is an attempt to consolidate the information available on the internet in a structured way (with links to useful sources) for a ML starter to kick-off his journey based on my experience as a learner.

*Note: As ML is a vast area of application & research these days, not each and every aspect is covered here, its just an approach of learning for ML starter and enable him to explore further in a structured & optimized way.*

**1. What & Why of Machine Learning**

First & foremost, you should be starting with exploring what is ML & why it is so useful these days? What are the types of ML and which type serves what kind of problem?

*-Machine Learning*

*-ML Types*

*— Supervised*

*— Unsupervised*

*— Reinforced*

*Links*

__Machine Learning with R — Brett Lantz__

__Machine Learning on Coursera -Andrew Ng__

__Machine Learning Mastery -Jason Brownlee__

**2. Basics of R & Python**

After getting the introduction to ML, you should have a look at the tools (R/Python etc) for the basics of programming & data manipulation so that you can have a look at sample solutions or tutorials to get a view how ML problems are solved using these tools.

*-R*

*— Basic Syntax*

*— Condition & Loops*

*— Data Manipulation*

*Links*

__Machine Learning with R — Brett Lantz__

__Learn Data Science in R — Analytics Vidhya__

*-Python*

*— BasicSyntax*

*— Condition & Loops*

*— Classes & Methods*

*— Data Manipulation*

*Links*

__Python Machine Learning — Sebastian Raschka__

__Learn Data Science in Python — Analytics Vidhya__

**3. Kaggle Tutorials**

Once you are done with the tools part, you can have a look at some basic tutorials at Kaggle, the problem mentioned below covers different domains of ML (mainly classification & regression). Its important to get your hands dirty quickly with practical problems so that you can stay motivated & get the context when you dive deep into the theory.

**4. Maths/Stats Basics**

Before starting with what, why & how of different ML algorithms, I would advice you to get started with the basics of Statistics, Probability & Linear Algebra, which will prepare you to grasp the working on algorithms without any discomfort.

__Linear Algebra for Machine Learning__

**5. Machine Learning Algorithms**

Now, in my opinion, is the right time to understand & dive deep into the ML algorithms with a view on what algorithm solves what kind of practical problem?

*-Supervised*

*— Classification*

*— Regression*

*— Common*

*-Unsupervised*

*— Association*

*— Clustering*

*— Anomaly Detection*

*-Reinforced*

*Links*

__Essentials of Machine Learning Algorithms -Analytics Vidhya__

__List of machine learning concepts -Wikipedia__

**6. Machine Learning Process/Framework**

Once you are familiar with all the topics above, I believe you can now have a look at the complete ML process/framework. Here you will learn, how to collect data, how to understand, explore & prepare data for ML algorithms to be consumed, how to evaluate the performance & how to improve the performance of the model.

__-Exploratory Data Analysis -Analytics Vidhya__

*-Applying Models/Algorithms*

*— Linear Regression*

*— Logistic Regression*

*— Decision Tree*

*— Support Vector Machines (SVM)*

*— Naive Bayes*

*— KNN*

*— K-Means*

*— Random Forest*

*— Dimensionality Reduction Algorithms*

*— Gradient Boost & Adaboost*

*-Evaluation with Cross-Validation*

__Improve Your Model Performance using Cross Validation -Analytics Vidhya__

*-Improving Model Performance*

*— Feature Engineering*

*— Ensembling*

*— -Bagging*

*— -Boosting*

*— -Stacking*

*Links*

__How to build Ensemble Models in machine learning? -Analytics Vidhya__

__Kaggle Ensembling Guide -MLWave__

**7. Machine Learning Practice**

So now, you know the theory, you know the tools, you know the process/framework, its the right time to get your hands dirty with practical problems and now its the matter of practice, practice & practice to excel in ML space.

*-Kaggle Competitions*

*-Hackathons*

*-On Job*

I hope you liked the article & it proves useful to your journey, it may give some insights to you so that you can develop your own learning path, happy learning.

*Thank you for reading my post. I regularly write about Data & Technology on *__LinkedIn__* & *__Medium__*. If you would like to read my future posts then simply ‘Connect’ or ‘Follow’. Also feel free to connect on *__Slideshare__*.*

*Originally published at *__https://www.linkedin.com__* on March 21, 2017.*