There are so many Python machine learning resources freely available online. Where to begin? How to proceed? These all questions will stuck most of the people to get started.Go from zero to Python machine learning hero in 7 steps!

The prime objective of this outline is to help you to find numerous free options that are available; there are many, to be sure, but which are the best? Which complement one another? What is the best order in which to use selected resources?

- These following steps are for complete begginers with no knowledge on programming and what actually machine learning is?

### Step1: Python Basics

- From the very beginning onwards use the standard IDE’s like Pycharm, jupyter notebooks, spyder.
- If you want to install compete Machine learning packages in python it is better to go with anaconda distribution it is easy to install and use.
- There are many great books to learn python what I personally preferred.

- The Automata Boring Stuff With Pyhton.
- Learn the python Hard way.
- Python for kids.

The Good online resources for learning Python are

- Google’s Developers python course.
- Python Documentation: https://docs.python.org/3/

From the resources you can understand and solve basic problems in programming up-to intermediate level .

if you are an experienced Python programmer you will be able to skip this step. Even if so, I suggest keeping the very readable Python documentation handy.

#### Step 2: Foundational Machine Learning Skills

- It has pointed out that there is a lot of variation in what people consider a “data scientist.” This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees.
- The good news is that you don’t need to possess a PhD-level understanding of the theoretical aspects of machine learning in order to practice, in the same manner that not all programmers require a theoretical computer science education in order to be effective coders.
- Andrew Ng’s Coursera course often gets rave reviews for its content; my suggestion, however, is to browse the course notes compiled by a former student of the online course’s previous incarnation. Skip over the Octave-specific notes (a Matlab-like language unrelated to our Python pursuits). Be warned that these are not “official” notes, but do seem to capture the relevant content from Andrew’s course material. Of course, if you have the time and interest, now would be the time to take Andrew Ng’s Machine Learning course on Coursera.

This step will help you to quickly get into machine learning

#### Step 3: Working with Python Scientific Libraries

Alright. We can able to handle Python programming and understand a bit about machine learning. Beyond Python there are a number of open source libraries generally used to facilitate practical machine learning. In general, these are the main so-called scientific Python libraries we put to use when performing elementary machine learning tasks.

The mainly used Pyhton libraries are

- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikitlearn

A good approach to learning these concepts are by visiting the official site of each library

#### Step 4:Getting started Machine Learning with Python

The time has come. Let’s start implementing machine learning algorithms with Python’s standard machine learning library, scikit-learn.

Many of the following tutorials and exercises will be driven by the iPython (Jupyter) Notebook, which is an interactive environment for executing Python. These iPython notebooks can optionally be viewed online or downloaded and interacted with locally on your own computer.

Ipython NoteBook overview from Standford University

#### Resources to learn

- An Introduction to scikit-learn, by Jake VanderPlas.
- Example Machine Learning Notebook, by Randal Olson.
- Model Evaluation, by Kevin Markham.

#### Step 5: Machine Learning topics with python

With a foundation having been laid in scikit-learn, we can move on to some more in-depth explorations of the various common, and useful, algorithms. We start with k-means clustering, one of the most well-known machine learning algorithms. It is a simple and often effective method for solving unsupervised learning problems:

The Good resources to learn

- k-means Clustering, by Jake VanderPlas.
- Linear Regression, by Jake VanderPlas.
- Logistic Regression, by Kevin Markham.

#### Step 6: Advanced Machine Learning topics with python

We’ve gotten our feet wet with scikit-learn, and now we turn our attention to some more advanced topics. First up are support vector machines, a not-necessarily-linear classifier relying on complex transformations of data into higher dimensional space.

#### Resources to learn

- Support Vector Machines, by Jake VanderPlas.

Next, random forests, an ensemble classifier, are examined via a Kaggle Titanic Competition walk-through Kaggle Titanic Competition (with Random Forests), by Donne Martin

Dimensionality reduction is a method for reducing the number of variables being considered in a problem. Principal Component Analysis is a particular form of unsupervised dimensionality reduction.

- Dimensionality Reduction, by Jake VanderPlas.

Before moving on to the final step, we can take a moment to consider that we have come a long way in a relatively short period of time.

Using Python and its machine learning libraries, we have covered some of the most common and well-known machine learning algorithms (k-nearest neighbors, k-means clustering, support vector machines), investigated a powerful ensemble technique (random forests), and examined some additional machine learning support tasks (dimensionality reduction, model validation techniques). Along with some foundational machine learning skills, we have started filling a useful toolkit for ourselves.

Step 7: Deep Learning with Python

Deep learning is everywhere! Deep learning builds on neural network research going back several decades, but recent advances dating to the past several years have dramatically increased the perceived power of, and general interest in, deep neural networks. If you are unfamiliar with deep learning,

There are two most important libraries that are used to implement deep learning

- Theano
- Caffe

#### Resources to learn

- Neural Networks and Deep Learning by Michael Nielsen.

#### I didn’t promise it would be quick or easy, but if you put the time in and follow the above 7 steps, there is no reason that you won’t be able to claim reasonable proficiency and understanding in a number of machine learning algorithms and their implementation in Python using its popular libraries, including some of those on the cutting edge of current deep learning research.It takes minimum of 3 months to maximum of 12 months.

Source: Deep Learning on Medium