Machine learning is the process of applying analytical and statistical models to enable computer systems to execute a range of activities in the absence of human-provided step-by-step instructions. As a consequence, machine learning may be used to generate various data-driven hypotheses.

Many industries have benefited from data science, but machine learning has always been a fundamental driver of digital transformation and automation. With the quantity of data created every day increasing exponentially, the world requires experts who can extract insights from that data and forecast the future.

Machine learning is prevalent all around the world. It can be beneficial to data scientists, software engineers, and business analysts. Students must spend months, if not years, mastering the theory and mathematics of machine learning. Without question, this is the best way to start your adventure. If you want to work in the subject of Machine Learning, you'll need a good background in math and statistics.

Are you interested in the opportunities that machine learning offers? Check out the path you may take to become an expert in machine learning.
Step 1. Learn Python/R
Machine learning capabilities are available in a variety of languages. In addition, there is a lot of development work going on in a lot of language addition; there is a lot of development work going on in many languages. The most widely used languages are “R” and “Python,” and both have extensive support and community. Before diving into the realm of machine learning, I recommend picking one of these two languages (R or Python) to assist you in focusing on machine learning.

Here are some book recommendations to get started with,

1. Python Machine Learning
2. R in a Nutshell

Step 2. Learn Basic Statistics
Let's get started or brush up on our statistics knowledge. Before you begin significant machine learning development, you need to have a strong understanding of the below mathematical topics,

Probability distribution
Theory of graphs
Testing of Hypothesis
Aspects of Bayesian thinking
Coordinate geometry of curves
Conditional probability
Linear discriminant analysis
Multivariate calculus.
Prior and posteriors
Least squares and mean square errors.
Mean, median, and mode
Maximum likelihoods.
Principal component analysis (PCA)

Here are some book recommendations to get started with,

1. Practical Statistics for Data Scientists: 50 Essential Concepts
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (“ESL”)

Step 3. Learn Data Preparation
Although each form of data, such as pictures in computer vision, text in natural language processing, and sequence data in time series forecasting, requires specialized processes, data preparation is a crucial issue for all. The quality of their feature engineering and data cleaning on the original data can separate great machine learning professionals from poor ones. Make the most of your stay here. Because this is the most time-consuming phase of the procedure, planning ahead is essential.

Some of the important topics,
Variable Identification
Univariate and Multivariate analysis
Missing values treatment
Outlier treatment
Feature Engineering

Here are some book recommendations to get started with,

1. Data Science from Scratch
2. Best Practices in Data Cleaning
3. Data Wrangling in Python
4. Feature Engineering for Machine Learning

Step 4. Learn Machine Learning
If you want to study in incremental stages and require additional guidance, start with working on some beginner-level machine learning projects. Projects are the best way for beginners to get some hands-on experience with real-world machine learning problems. Also, it's simple and easy to understand concepts by actually working on them. Work on diverse ML projects covering all the fundamental algorithms and a few advanced subjects such as neural networks and recommendation systems. If you understand the concepts and algorithms well, you will be able to code them in R or Python easily.

Under the machine learning concepts, you need to learn
Machine learning models
Machine learning types
Supervised algorithms (regression, classification)
Unsupervised and semi-supervised algorithms (clustering, dimensionality reduction, graph-based algorithms)
Deep learning (CNNs and RNNs)
Reinforcement learning (dynamic programming, Monte Carlo methods, heuristic methods)
Clustering
Separation of features
Output variable
Outliers
Label/ target
Data training
Time series analysis
Clustering

Under the machine learning algorithms, you need to learn
Linear regression
Logistic regression
Decision tree
SVM
KNN
Naive Bayes
Random Forest,
XgBoost
ADABoost etc.

Here are some book recommendations to get started with,

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
2. The Hundred Page Machine Learning Book
3. Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition)
4. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)

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Step 5. Get your hands dirty with Beginner Level ML Projects
Machine learning is a rapidly growing field with applications in various areas, including health, finance, retail, and many more. If you're a novice interested in pursuing a career in new technologies such as machine learning or deep learning, it's important to have hands-on experience with the ideas.

Here is a selected selection of machine learning projects to get you started on your ML adventure,

House Price Prediction
Titanic Survival Prediction
Stock Prices Prediction
Iris Flowers Classification Project
Movie Ticket Pricing Prediction
Handwritten Digit Classification

Step 6. Advanced Machine Learning
Now that you've learned the fundamentals of machine learning, it's time to consider more advanced machine learning techniques, such as Deep Learning and Natural Language Processing (NLP), to better grasp different data formats.

Deep Learning
In machine learning, deep learning allows computers to learn by doing, similar to how people learn. Self-driving cars rely heavily on deep learning because it will enable them to see a stop sign or tell the difference between a pedestrian and a lamppost from a distance. Consumer electronics, such as smartphones, tablets, TVs, and hands-free speakers, include voice control capabilities. Deep learning has gotten a lot of press lately, and for a good reason. It's all about reaching previously unattainable targets.

Here are some book recommendations to get started with,

1. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
2. Deep Learning: A Practitioner's Approach
3. Deep Learning with Python
4. Deep Learning (Adaptive Computation and Machine Learning series)

Natural Language Processing
Natural Language Processing (NLP) is a field of AI that allows machines to comprehend human language. Its objective is to create systems that can automatically understand the text and execute activities like translation, spell check, and classification. However, machine learning will be required to automate these procedures and provide correct replies. Machine learning is the process of teaching machines how to learn and develop without being explicitly programmed by applying algorithms.

Here are some book recommendations to get started with,

1. Natural Language Processing with Python
2. Natural Language Processing in Action: Understanding, analyzing, and generating text with Python

Computer Vision
In the artificial intelligence (AI) field, computer vision is the study of how computers and systems can derive useful information from visual inputs such as digital pictures, videos, and other types of media and take action or make suggestions in response to that data. Computer vision and artificial intelligence go hand in hand because computer vision allows computers to perceive, observe, and comprehend. Computer vision has made great progress in recent years because of advancements in artificial intelligence, deep learning, and neural networks, exceeding humans in numerous tasks related to object identification and classification.

Here are some book recommendations to get started with,

1. Computer Vision: Algorithms and Applications
2. Computer Vision: Models, Learning, and Inference
3. Programming Computer Vision with Python

So, once you've completed this, you can start practicing machine learning skills on a dataset from websites like Kaggle, and once you've become proficient and comfortable with that, the sky's the limit; you can write research papers, compete in live competitions, and win both laurels and cash prizes.