The trend of using machine learning to solve problems is increasing in almost every field such as medicine, business, research, etc. So, for the present day, it has become an essential skill to learn. To break into machine learning, my advice is to follow the following steps:

**Step-1: Make Your Mindset**. The first and most important step towards learning machine learning is to prepare yourself mentally to begin your journey. Believe in yourself and increase trust. Think, why are you interested in learning Machine Learning, and what is your goal? This will help you a lot to keep track of your journey in the right direction.

**Step-2:Learn the basics of R/Python.**There are multiple languages that can be used for solving machine learning problems. However, these days “R” and “Python” are the most commonly used languages and there are enough resources & Learning communities available for both. Before you get involved in the world of ML, you must have some basic knowledge and hands-on programming in one of these two languages (R or Python) which can help to focus on machine learning and with this node, you can start your journey towards becoming ML expert

**Step-3: Learn Basic Mathematics.** Since Machine Learning includes much of the mathematical uses for interpretation and optimization so one must have sound knowledge of mathematics before starting core algorithms of Machine Learning. The topics to be covered on Mathematics for Machine Learning are

- Linear Algebra
- Probability & Statistics
- Calculus

You can find tons of online resources for studying these topics of mathematics. Some of the online resources for studying mathematics for Machine Learning are:

- Khan Academy’s Linear Algebra, Probability & Statistics, Multivariable Calculus, and Optimization.
- Coding the Matrix: Linear Algebra through Computer Science Applicationsby Philip Klein, Brown University.
- Linear Algebra : Foundations to Frontiers by Robert van de Geijn, University of Texas.
- Applications of Linear Algebra, Part 1 and Part 2. A newer course by Tim Chartier, Davidson College.
- Joseph Blitzstein: Harvard Stat 110 lectures.
- Larry Wasserman’s book: All of statistics: A Concise Course in Statistical Inference.
- Boyd and Vandenberghe’s course on Convex optimization from Stanford.
- Linear Algebra — Foundations to Frontiers on edX.
- Udacity’s Introduction to Statistics.

Once you have the good knowledge of mathematics required for Machine Learning you should learn how to implement the mathematics through the programming language. For this, you should have good practice and knowledge about the libraries used for mathematical computing in Machine Learning. For example, if you have switched to python there are Libraries as Scipy, Numpy, Pandas, etc. there might be similar libraries in R also. You have to learn how to use those libraries according to your requirement in the problem.

**Step-4: Learn Data Handling (Preparation/Interpretation/Analysis).**

This is the first and one of the most important steps of machine Learning wherein you tend to do almost 80% of the whole work which is known as Data Pre-Processing. If you have good practice in data pre-processing and data analysis then you are less likely to have defects in your Machine Learning models and Predictions made by them.

The fact is that the more you clean and pre-process your data as per your business cases or requirement, the better your chance of success is. To become a good ML expert from an average, one should have a sound practice of feature engineering and data cleaning which happens on the original data.

Data preparation includes preparing your data to apply machine learning algorithms to them. Supplied data might not be proper and complete and might need to be cleaned. Data Cleaning can be defined as the process of inserting the most appropriate data in empty places by using mathematical techniques like interpolation, filling with mean or median values, etc.

Once you have cleaned your data, you need to interpret it and visualize it to know about the features which contain important information about the data. This step is very interesting and is used to explain the data via analysis (Using dashboards, Charts, and Diagrams, Histograms). Usually, we have a large amount of data so visual analysis is done to understand the data more clearly. Industry Experts use this phase by putting up dashboards using analytical tools (using Tableau) for exploring the data and to give valuable insights for the same based on your business use case. In some cases, you might need to make your own feature from previous ones which are called feature engineering.

Python contains libraries, like Pandas that contain strong tools(Functions) for data cleaning and pre-processing. In the same way, **Matplotlib** and **seaborn** are the libraries of python that provide tools for data visualizations.

**Step-5: Learn basic Machine learning algorithms**. Here comes the starting core of machine learning. First of all, you need to learn the basics algorithms of machine learning.

Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:

- Linear Regression
- Logistic Regression
- Decision Tree
- SVM
- Naive Bayes
- kNN
- K-Means
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms
- GBM
- XGBoost
- LightGBM
- CatBoost

You can get dozens of online resources to study their algorithms and how to implement them using Python/R. Some of the best materials on youtube include Victor Lavernko, Paul G. Allen School, Andrew ng, Siraj Raval, Sentdex, and many others. In addition to these free materials, you can also take online paid courses that provide you with learning certificates. Some of such online learning platforms are Udemy, Coursera, etc. You can also find several books dealing with the algorithms of machines on the internet.

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**Step-5: Learn Advance Machine learning algorithms.** Once you have mastered basic topics, then you can go for some of the advance and specialized topics like Deep Learning, Reinforcement learning. You can find the materials on these topics over the internet easily. Studying and practicing these topics will make you an expert in the field of machine learning.

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