What Should You Know Before Trying to Learn Machine Learning?

Now a days Learning Machine Learning is the trending technology, 

  

It really depends on what YOU want to know about machine learning. 

What Do You Really Want In Machine Learning!

1. If you want to know the theory behind the algorithms and how they work, definitely being well versed in probability (and statistics), linear algebra and calculus are really important.

 Some background in algorithms and data structures will help you with classifiers like https://en.m.wikipedia.org/wiki/... 

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It Will Makes Easy For You!

Knowing a programming language like Python will make it easy for you to implement the algorithms you learn without needing to deal with classes and code structure etc

. This is really important because it really gets you thinking about the internal mechanics of the so called "Machine"

2. If you are interested in the applications of machine learning to data-sets, there are fantastic libraries in C++ And Java (https://en.m.wikipedia.org/wiki/...), not to mention the fantastic Python libraries like scikit and lasagne

Ofcourse to know how to use which algorithm when requires some knowledge of the domain and what classifiers exist out there. 

Best way to Learn Machine Learning (Online Course)

3. (RECOMMENDED) A hybrid approach. Understand the math and the application at the same time.

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 I'd highly recommend Andrew Ngs course on coursera: https://www.coursera.org/learn/m...
Which ever technique you pick, there is only one way to get better at Machine Learning and that is to practice, practice, practice. 

Kaggle (company)Competitions are great way to get started. Tons of code available and tutorials. 

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Starting And Ending Point

 There is a sequence on learning Machine language which make you better to understand:

Follow these method:

Learn on by one topic in the sequence given below

• Part 1 - Data Preprocessing
• Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Part 4 - Clustering: K-Means, Hierarchical Clustering
• Part 5 - Association Rule Learning: Apriori, Eclat
• Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
• Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

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