Artificial intelligence is an amalgamation of deep learning and machine learning. Machine learning is a crucial component of AI as it focuses on algorithms and statistical models that allow computers to learn from predictions and make decisions based on data. For instance Image Recognition, Filtering Emails, Fraud detection.
Now let's get into the roadmap!
Programming Language : Master Python for Machine Learning Success
Python's combination of simplicity, extensive libraries, community support, and flexibility makes it an ideal choice for developing machine learning applications.
Here are some python libraries you need to learn
Pandas & Numpy (for data manipulation and numerical computing)
Matplotlib & scikit-learn (for data analysis and visualisation)
Tenserflow & Seaborn (for complex visualisation and building and training neural networks)
These libraries form the core toolkit for most machine learning projects in Python.
Mathematics and Statistics : Build a Strong Foundation in Math and Stats
Linear Algebra : vector, matrices
Calculus : Differentiation, integration, gradients, optimization methods.
Statistics and Probability : Probability, hypothesis testing, data sampling, standard deviation, distribution.
Resources: Become a Probability and Statistics Master by Krista King on Udemy
Machine Learning : Dive into the Core Concepts and Techniques
Start with basics : Begin with an overview of ML topics such as Supervised learning , Unsupervised learning, Reinforcement learning. Understand the differ between them. Explore ML algorithms like linear regression, logistic regression, decision trees, k-nearest neighbors (k-NN), support vector machines (SVM), and clustering algorithms (k-means, hierarchical clustering).
Take online courses and read books : Online courses recommendation Andrew NG’s Machine Learning course on Coursera.
ML and Mathematics courses on Khan Academy.
Recommended book include Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
Stay updated : Go for research papers and conferences, participate in Hackathons, Read ML blogs, join forums like Reddit and Stack Overflow, and participate in ML communities on platforms like LinkedIn and Discord.
Work on projects : Build ML projects on your on interest.Contribute to open-source ML projects on GitHub. This allows you to collaborate with others, gain feedback on your code, and improve your skills.
Network and collaboration : Connect and Grow with the ML Community
Engaging with others in the ML community allows for the exchange of knowledge, insights, and experiences. This can include attending ML workshops, Participating in Hackathons, discussing different approaches to solving problems, sharing best practices, and learning from others' successes and failures.
By diligently following this roadmap, you will not only build a strong foundation in machine learning but also position yourself for a bright and successful future in this dynamic and rapidly evolving field.