How to Master Machine Learning with Python in 7 Simple Steps

Source: Deep Learning on Medium

Machine learning is one of the hot buzzwords right now and has been experiencing its expansion and popularity in recent years. But there is a lack of skilled Machine Learning professionals in the market and It is a great time to kickstart your career in the machine learning field. This article aims to give you an introductory guide to start your machine learning journey with Python in 7 steps. Because Python is considered to be in the first place in the list of all ML development languages. So let’s start!!

STEP 1. BASICS OF PYTHON

Maybe you are thinking you need to be an expert in Python for proceeding in machine learning. Well, this is not true. In fact, Python makes your path to machine learning easier. You need to have a good command over the basics of Python. If you haven’t yet stared with Python and wondering where to learn python for free you can refer this Python Master Sheet.

Along with this, do install an editor or IDE for Python in your machine. There are many IDEs available. You can select any one of them you find appropriate, just start practicing and upgrading your skills.

STEP 2. FOUNDATION OF MACHINE LEARNING

To the beginners, machine learning seems to have many new high-technical concepts and processes. If you think so, then you’ll feel glad to know you are wrong. Machine learning is based on the fundamental subjects which we studied in our school. ML is not a tough job.

For mastering machine learning, you need to be proficient at following mentioned concepts:

  • Mathematics
  • Statistics
  • ML algorithms
  • Programming languages
  • Data wrangling and analysis

Here is the help, Learn the Art of flirting with Machines

STEP 3. PYTHON PACKAGES

Here comes the hero of the picture, Python packages. This is the primary reason why the name of Python is taken with machine learning. After working on the prerequisites mentioned above, know about the Python libraries which are used for ML.

Though in-built Python libraries are more than enough for machine learning, you can also import required libraries from outside. NumPy, Pandas, Matplotlib, Scikit-Learn are the libraries that are widely used in ML.

STEP 4. Machine learning with Python

Moving ahead on the path of machine learning, the next topic you need to work on is data pre-processing and machine learning techniques. In machine learning, we do not require data, we require quality data and for this, data pre-processing is required. Here, you need to go through:

  • Data preprocessing
  • Analyzing data
  • Visualizing data-univariate plots
  • Visualizing data-multivariate plots

Machine learning techniques are the strongest weapons for machine learning. Many people think that ML techniques and algorithms are the same. But this is absolutely wrong. Techniques are the way to solve a problem and when we talk about algorithms we expect output from the given input.

Here are the ML techniques which will take you one step closer to your destination.

  • Regression
  • Anamoly detection
  • Clustering
  • Classification

STEP 5. MACHINE LEARNING ALGORITHMS

Machine learning algorithms are the backbone of machine learning. What does make a machine smart? Of course algorithms. A machine behaves according to the algorithms. I suggest, before getting them with Python, understand these algorithms theoretically. Then proceed towards its practical implementation with Python.

Do have a look at which algorithms makes the machine learning an influential technology.

  • Linear and logistic regression
  • Decision tree
  • Support vector machine (SVM)
  • Naive Bayes
  • KNN
  • K-means
  • Random forest

STEP 6. ADVANCED TOPICS TO GRAB

A journey becomes interesting when there are adventures. Sarcastically, in our journey, the adventures are about to come. After the algorithms, now its turn of the advanced machine learning concepts which will make you more proficient in classification.

So, welcoming our adventures which are support vector machine (SVM), Dimensionality reduction, and gradient boosting algorithm.

STEP 7. DEEP LEARNING WITH PYTHON

Deep learning with Python is another aspect of machine learning which is driving everyone crazy. And when Python is added to deep learning, then it becomes fun to work on such methods. Before learning it with Python, first, understand what is deep learning and why do we need it?

I can precisely say, you need to pay attention to the following mentioned topics in deep learning.

  • Why deep learning?
  • Artificial neural networks (ANN)
  • Deep neural networks
  • Applications

EndNote

Explore the Future of Machine Learning. So these are the seven steps following which you can accomplish your dream of machine learning. Machine learning is not making only machines smart, but humans also.