Last updated on April 1st, 2024 at 07:52 am
Read, and re-read resources on introductions to Calculus, Mathematical statistics, both differential and inference, algorithm analysis, optimization, differential equations, linear algebra, Python, R and more. Does that sound difficult?
You don’t need advanced learning in them. You will however essentially need to understand how you can apply this learning to handling data analysis of the present and future of nearly every field under the ML, AI, Deep Learning and VR fields.
Here are some advantages of machine learning training in such courses.
- You get to learn ML fundamentals and basic algorithms, statistical pattern recognition, data mining, statistics including Bayesian probability, working with Python, Pandas, and R, the Sci-learn and Tensorflow APIs, and more in a well-paced, learn-at-your-convenience online and classroom training mode.
- The integrated curriculum helps you through practical industry-needed and relevant practical applications like
1. Unsupervised learning (deep learning, clustering, recommender systems, dimensionality reduction)
2. Supervised learning (neural networks, support vector machines, parametric/non-parametric algorithms, kernels)
3. ML best techniques and practices (variance and bias theory, AI, and innovation in the ML process).
- Most learning is through applications, case studies, live-industry-project and effective mentoring, virtual classes, workshops, hackathons, and such support.
- Your certification carries weight as it declares you have applicative knowledge and our job and industry ready.
That having been said, here are some practical tips for ML and discerning learners.
- The first timers in ML rarely get things right. Don’t panic. ML skills are cultivated skills and are meant to be regularly practiced.
- Implement your learning through a model. Compare your implementation skills with others while discovering the open-source libraries, mathematical or program techniques, and tricks, math-tools, etc. that can improve your efficiency.
- Don’t get overwhelmed because leveraging your skills means research-work and doing small projects which help assimilate learning and applying the learning to practical situations whether they be smartphones, VR or chatbots. The tools in Python take care of the math while you get your hands deep into data analysis, data cleaning, and mining and data exploration and predictive analysis.
- It isn’t just about math for beginners. Most often it is about data, data and more data! So get cracking in honing your data analysis skills.
- Apply your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
- Attend hackathons (Kaggle, TechGig, Hackerearth, etc.) which give you support, exposure and mentorship in ML practical ideas.
- Build your portfolio with projects
a. Where you collect the data yourself
b. Where you get exposure to data cleaning, dealing with missing data, etc.
- Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.
As in all fields, it does get easier as you progress and get adept. So why wait? Partner with Imarticus courses and get a head-start in ML. Go ahead and do a machine learning course with a reputed training institute like Imarticus.
