100DaysOfMLCode
By Harshit Ahluwalia
100 Days of Machine Learning Coding as proposed by Siraj Raval
My Journey towards Machine Learning upto 2018
At the end of this readme I've attached some of the Amazing infographics that are from Analytics Vidhya(taken the permission from the author) and I have started 26 weeks of ML Code where you can learn Machine Learning in just 26 weeks . I will post the Schedule sooon that on which week you have to study which topic
How to Learn Machine Learning
While i don't want to overstate the complexity of the field, 30 days is awfully short.
 Spend most of my time on the basics of statistics
 Then have a look at 1 or 2 very common techniques. (e.g.,
linear regression
and logistic regression
)
 Take a dataset that interests you and do some descriptive statistics on it (counts, max, min, median, plots etc) and discover as many weird things in the data as possible. Weird meaning stuff that does not seem right.
 Now try to answer a question for yourself on the above dataset. Do this by (A) solving the weird stuff, (B) getting the data into a format that works for (C) one of the common techniques you studied. It is okay if you hack the code together with lots of googling. (do sanity checks on your results though ;)
Basic Statistics
One of the most easy pitfalls is to just take offtheshelf implementations of algorithms and throw them against your problem. But most algorithms are based on assumptions and all of them have some limitations. A good grasp of basic statistics will help you:
 Determine whether the assumptions hold.
 What they mean for your choice of algorithm
 Reason about the limitations they imply
 The impact if they are not present (which is not always dramatic)
 Any time spent here will pay dividends every time you have a look at a new algorithm. So no worries if this takes up nearly all your time.
Common Techniques
 Early on, you actually better go deep than broad, because many concepts/elements return any way in other algorithms.
 I mention two types of regressions because in many cases, you'll get a decent answer with these techniques. Also, it is in some sense amazing how something that is basically 'draw trendline' in Excel actually goes so deep. Not that all of it is taken that heavily into account in practice, but it still is good to have it in the back of your head. Especially for those times where you get weird results.
Weird Data Stuff
 This is the largest timesink, always. And it is very important, hence the mantra 'garbage in, garbage out'. Take any realworld dataset which has not been precleaned and you'll find weird things:
 A hugely overrepresented value (companies who like to code missing as 999...)
Duplicate ID's
 A variable which is actually an ID (amazing how many student dreams are shattered by pointing this one out if they have a nearly perfect model ;))
 Missing values
 Mislabeled cases, misspellings...
 Everything is on state level, except for this one state for which they are reporting counties instead.
 You need to experience it to acknowledge it. And almost any realworld dataset + a critical eye will make you do just that. ;)
Try It

Well, you didn't learn all this not to use it, right? Also, making sense of your results is important. And being critical for them as well. It is so easy to make a logical mistake which is not programming mistake. I.e., the software will run, but the result will be very wrong.

If you want to go all the way, take your results to a friend/family and try to explain highlevel what you did, what the results are and what they mean. Again speaking from teaching experience, there are people who are really good at the technical stuff, but cannot transfer the relevant implications of it to a nontechnical person.
So You want to Learn Machine Learning in 30 Days . you need to Devote About ML & work hard ,in Machine Learning There are Various Concepts are there .
Better to take any Online Course. then note down all course content and Prepare Schedule for 30 Days . i will Suggest you Best Online Machine Learning Course.
Machine Learning by Standford University, Mentor  Andrew Ng
Topics include:
(i) Supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, antispam), computer vision, medical informatics, audio, database mining, and other areas.
Machine Learning AZ™: HandsOn Python & R In Data Science
This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:
 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, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
 Part 4  Clustering: KMeans, Hierarchical Clustering
 Part 5  Association Rule Learning: Apriori, Eclat
 Part 6  Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
 Part 7  Natural Language Processing: Bagofwords 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: kfold Cross Validation, Parameter Tuning, Grid Search, XGBoost.
Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some handson practice building your own models.
Beginner Data Science
Books for April  May
You can Download all the required books from here
Mastering Feature Engineering
R for Data Science
Python for Data Analysis
Books for June  Aug
You can Download all the required books from here
The Element of Stastistics Learning
An Introduction to Stastistics Learning
Machine Learning with R
Workflow on Data Analysis
Will Help all the freshers in machine learning for getting started
Day 1Working with Pandas
for code of Day1 click here
Day 2Simple Linear Regression
for code of Day2 click here
Day 3Logisitics Regression
for code of Day3 click here
Day 4 Cheet Sheet on Scikit Learn
Day 5 KMeans Clustering
Top Algorithms for Prediction
Machine Learning vs Artificial Intelligence . What's the difference ? let's see.....
Data Science Resources
Infographics for Youtube Channel
Infographics for Python
26 weeks of ML Code
Infographics and articles from Analytics Vidhya
Week 1 : Git Basics & Introduction to Python
Download infographic (https://bit.ly/2HH9JcG)
more to read (https://bit.ly/2HH9JcG)