What are the best courses to start learning machine learning? I am completely new to it and have some programming background.

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Getting started with machine learning can be an exciting and rewarding journey, especially if you already have a programming background. Machine learning is a dynamic field that combines the power of data and algorithms to make predictions, classify data, and automate tasks.

To kickstart your machine learning education, you can consider a variety of courses that cater to beginners, let’s discuss some of them -

Coursera: Coursera is an excellent platform to start your machine-learning journey. They offer a wide range of courses from top universities and institutions. Here are some highly recommended courses:

Andrew Ng's Machine Learning: Taught by a renowned expert, this course is a perfect starting point for beginners. It covers the fundamentals of machine learning and provides hands-on experience using Octave/MATLAB.

Deep Learning Specialization: If you want to dive deeper into neural networks and deep learning, this specialization is an excellent choice. It's also offered by Andrew Ng and covers topics like convolutional networks, sequence models, and structuring machine learning projects.

Tutort Academy: This innovative online learning platform is led by a team of seasoned professionals with backgrounds in esteemed companies like Google, Microsoft, and NIT.

Full Stack Artificial Intelligence and ML Program - This 10-month online program is made in collaboration with Microsoft, tailored to meet the needs of professionals in various fields. This course cover areas such as Data Science, Machine Learning, and Artificial Intelligence.

This course is thoughtfully crafted by industry experts from major corporations like Google, Amazon, Microsoft, and EY. This ensures that students gain skills that align with industry requirements and meet the high standards of multinational companies.

The platform places a strong focus on interactive learning experiences. Live lectures, hosted by respected industry professionals, give students the chance for one-on-one sessions to clarify doubts. This not only allows direct learning from these experts but also fosters an environment for engagement and collaboration.

Each student receives personalized attention and support thanks to small class sizes. This enables mentors to offer tailored guidance throughout the student's learning journey.

A unique feature of this course is the inclusion of real-time projects in partnership with top companies across various sectors, such as healthcare, e-commerce, finance, and automotive. These projects give students hands-on experience and exposure to industry-level work, boosting their portfolios and providing a competitive advantage during job interviews.

After being selected by the admission committee, students receive personal counseling from a dedicated data scientist, rather than a salesperson. This ensures that students receive guidance in choosing the most appropriate course that aligns with their goals and needs.

Moreover, a variety of resources are available to help students secure their desired job placements. These resources include guaranteed job opportunities, mock interviews led by experts, assistance with optimizing LinkedIn profiles, and expert guidance in crafting resumes.

Recognizing that unexpected circumstances may affect course completion within the scheduled timeframe, the platform offers a flexible two-year pass. This pass allows students to join multiple batches and interact with different mentors during this extended period.

For added convenience in learning, students have access to recorded lectures and learning materials through the Learning Management System (LMS) portal. This enables them to revisit course content at their own pace, reinforcing their understanding.

Simplilearn:

Simplilearn is another great platform that offers a comprehensive set of courses on machine learning. Some courses you might find useful are:

Machine Learning Certification Training: This program offers a broad introduction to machine learning concepts, tools, and techniques. It's well-suited for beginners and provides hands-on experience.

Deep Learning Course: Once you've got the basics down, you can consider Simplilearn's deep learning course to explore advanced topics like neural networks, CNNs, RNNs, and natural language processing.

Other recommended courses outside of these platforms:

edX: Introduction to Artificial Intelligence by MIT: This course is offered by MIT and provides a comprehensive overview of AI and machine learning.

FAST.AI's Practical Deep Learning for Coders: This course offers a unique top-down approach, focusing on the practical application of deep learning with minimal theory. It's a great choice for those who want to quickly get hands-on with deep learning.

When starting your journey, it's essential to grasp the fundamentals of machine learning, such as linear regression, logistic regression, decision trees, and ensemble methods. Once you're comfortable with these concepts, you can move on to deep learning and explore areas like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning.

In addition to these courses, be prepared to get your hands dirty with programming in Python and utilizing libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn. Learning how to use machine learning libraries like TensorFlow and PyTorch is crucial for more advanced work.

To truly excel in machine learning, it's essential to work on projects. You can find datasets and challenges on platforms like Kaggle to practice and improve your skills. Building and deploying your machine learning models will provide invaluable experience.

In summary, machine learning is an exciting field that blends programming and data science, and there are many excellent courses available for beginners. These are great platforms to start your journey but don't hesitate to explore other resources and platforms based on your preferences and availability.

The key is to be persistent, practice consistently, and work on real-world projects to solidify your knowledge and skills in machine learning.

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