Machine Learning has become one of the most demanding technologies in the world. It is well capable of automating tasks and too with intelligence (like a human touch). This process allows machines to automate tasks by delivering intelligence via machines. Machine Learning has drastically surged in the past few years and its market is expected to grow from USD 21.17 billion (2022) to USD 209.91 billion (2029), at a CAGR of above 38% during the forecast period.
Today, Machine Learning is actively helping businesses automate and deliver their tasks efficiently. Besides this, it also helps create models that can handle large sets of data that can be highly scalable and functional with less TAT. That is why Engineers and businesses are more involved in building such precise ML models that can leverage profitable opportunities and avoid unknown risks.
Some of the most famous applications of Machine Learning are in Image recognition, text generation, and so on that, we’re seeing in today’s applications and software. These are some of the notable scopes for machine learning experts to shine as sought-after professionals. If you’ll look into it from a career perspective, as of now, there are over 15,000 active job openings in India & above 270k worldwide. Being one of the most demanding, high-paying jobs has made many individuals pave their careers toward Machine Learning.
Perhaps, by the above-mentioned stats, you must have understood the importance of Machine Learning in today’s world. Being a beginner’s level is pretty hard to acknowledge the learning path in well-sync form. This is why we came up with this article to help you with the basics with a deep understanding. After going through this chart, you’ll learn How to Start Your Journey in Machine Learning and become an ML expert in 45 Days.
1. Programming Language (Day 1 – Day 5)
The moment you’ll start your journey of Machine Learning, you must start it by learning one of the most popular programming languages in today’s world, i.e. Python, and following this, you’ll have the basic clarity for applying methods in machine learning.
You can refer to this complete tutorial – Python Programming Language.
2. Learn Python Libraries (Day 6 – Day 10)
Since you’ve already invested 16 days, now you’ll be focusing on applying Python’s libraries and its implementations that are being used while working on Machine Learning.
3. Linear Algebra (Day 11 – Day 16)
Once you’ve gained knowledge of basic understanding of Python, now you’ll be learning the most delicate and highly used function in Machine Learning i.e. Linear Algebra. Now you’ll be looking forward to starting learning concepts of Algebra like Matrix operation, Vectors, Eigenvalues, etc.
- System of Linear Equation
- Matrix Operation
- Properties of Matrix
- Solving Linear Equation using Gaussian Elimination
- LU Decomposition of Linear Equation
- Row Echelon Form
- Determinant
- Eigenvalues and Eigenvectors
- Eigenspace
- Orthogonal and Orthonormal Vectors
- Eigen Decomposition
- Diagonalization
- Singular Value Decomposition — Implementation
- Matrix Approximation
- Vector Operations
4. Statistics (Day 17 – Day 19)
After spending 10 days learning the basics of programming language and mathematics, now you’ll be moving forward to learn statistics as it will help you when you’ll be working on real-life scenarios it has to be read and understood thoroughly.
- Mean, Standard Deviation, and Variance — Implementation
- Descriptive and Inferential Statistics
- Probability Theory and Distribution
- Sampling Distribution
- Linear Regression
- Sample Error and True Error
- Bias Vs Variance and Its Trade-Off
- Hypothesis Testing
- Confidence Intervals
- Correlation and Covariance
- Correlation Coefficient
- Covariance Matrix
- Pearson Correlation
- Spearman’s Rank Correlation Measure
- Kendall Rank Correlation Measure
- Robust Correlations
5. Data Analysis and Pre-processing (Day 20 – Day 25)
Data Analysis is a crucial aspect of Machine Learning as they explain the various features and their representation. This step helps in analyzing and fitting the best model for the data based on its requirements.
The next step is to use the learning from the above steps and then apply them to the data to optimize it for the best possible results. Some of the key aspects of it include:
- Data Processing – Basics
- Identify and Handle Missing Values
- Deal with missing values in a Time series
- Replacing missing values using Pandas in Python
- Drop rows from Pandas Dataframe with missing values or NaN in columns
- Count NaN or missing values in Pandas DataFrame
- Handling Missing Values
- Working with Missing Data
- Handle Missing Data with Simple Imputer
- Handle missing values of categorical variables
- Feature Engineering
6. Core Concepts of Machine Learning (Day 26 – Day 40)
Machine Learning is the basic process of making algorithms that intelligently understand the pattern followed by the data in a particular problem or situation. Based on the patterns observed in the previous data, predictions will be made for similar situations in the future. This essentially is the core concept of machine learning. There are multiple algorithms with different use cases and mathematical formulations which are used based on the requirement. These Machine Learning algorithms can be classified into two categories as follows:
A. Supervised Learning (Day 25 – Day 28)
One of the first and most basic steps of learning Machine Learning is Supervised Learning Algorithms and their Applications to basic datasets.
Supervised Learning is a technique where the model is trained on the data that contains both Dependent and Independent Variables and then based on some testing data containing only Independent variable is used to predict the corresponding dependent variables for the data.
Supervised Learning Algorithms are further classified into two categories based on the kind of predictions they can make.
i) Regression
These algorithms mainly make predictions of numerical variables. The values of the dependent variable can be any number or decimal.
- Linear Regression
- Decision Tree Regression
- Random Forest Regression
- Ridge Regression
- Non-Linear Regression
- Bayesian Linear Regression
- Polynomial Regression
Know more about regression here.
ii) Classification
These algorithms effectively predict the dependent variables which can take only specific values or categories.
- Random Forest
- Decision Trees
- Logistic Regression
- KNN (k-nearest neighbours)
- Support Vector Machines
- Naïve Bayes Classifier Algorithm
Know more about different classification algorithms here.
B. Unsupervised Learning (Day 29 – Day 32)
The second aspect of Machine Learning algorithms is the Unsupervised Algorithms and understanding how can they be used to solve real-life problems.
Unsupervised Algorithms are applied to problems where the prediction or the category of prediction is unknown.
C. Reinforcement Learning (Day 33 – Day 40)
Reinforcement Learning – In this section, you’ll be learning how machines interact and behave in any given circumstance.
- Positive Reinforcement
- Negative Reinforcement
8. Project Work and Deployment (Day 41 – Day 45)
After spending 40 Days with Machine Learning, you will be done with almost all the elements that are required for a beginner professional. This is the time now to move forward to start working on some projects that will enhance your skills and will help you in becoming a proficient expert,
- Loan Approval Prediction using Machine Learning
- House Price Prediction using Machine Learning in Python
- Stock Price Prediction using Machine Learning in Python
Deployment is a very crucial phase while working on any project as it helps to reach (and access) the users. For that, you’ll be required to push your project into production for real-time application. The first step in the process is to learn to connect your python code for an ML model with a website that can be hosted for the public. The most important and famous module you need to learn for python code deployment is Flask. Flask is a simple module that allows you to send and receive data and process it using an ML model. Refer: Model Deployment – Python.
Linked here is a simple flask code tutorial that one can refer.
Finally, if your web flask app work on a local host the final step is to deploy it using any of the following tools:
- Azure
- Heroku
- Render
With this complete roadmap followed religiously one can undeniably say that they can easily build projects in ML projects.
