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- You get to learn\u00a0ML fundamentals<\/strong> 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.<\/li>\n
- The integrated curriculum<\/strong> helps you through practical industry-needed and relevant practical applications like<\/li>\n<\/ul>\n
1. Unsupervised learning (deep learning, clustering, recommender systems, dimensionality reduction)<\/p>\n
2. Supervised learning (neural networks, support vector machines, parametric\/non-parametric algorithms, kernels)<\/p>\n
3. ML best techniques and practices (variance and bias theory, AI, and innovation in the ML process).<\/p>\n
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- Most learning is through<\/strong> applications, case studies, live-industry-project and effective mentoring, virtual classes, workshops, hackathons, and such support.<\/li>\n
- Your certification<\/strong> carries weight as it declares you have applicative knowledge and our job and industry ready.<\/li>\n<\/ul>\n
![\"Data](\"https:\/\/imarticus.org\/blog\/wp-content\/uploads\/2017\/03\/PGA-1-300x300.png\")
<\/p>\nThat having been said, here are some practical tips for ML and discerning learners.<\/p>\n
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- The first timers in ML rarely get things right.\u00a0<\/strong>Don\u2019t panic. ML skills are cultivated skills and are meant to be regularly practiced.<\/li>\n
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- Implement your learning through a model<\/strong>. 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.<\/li>\n
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- Don\u2019t get overwhelmed\u00a0<\/strong>because\u00a0<\/strong>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.<\/li>\n
- It isn\u2019t just about math<\/strong> for beginners. Most often it is about data, data and more data! So get cracking in honing your data analysis skills.<\/li>\n
- Apply your learning<\/strong> 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.<\/li>\n
- \u00a0Attend hackathons<\/strong> (Kaggle, TechGig, Hackerearth, etc.) which give you support, exposure and mentorship in \u00a0ML practical ideas.<\/li>\n
- \u00a0Build your portfolio<\/strong> with projects<\/li>\n<\/ul>\n
a. Where you collect the data yourself<\/p>\n
b. Where you get exposure to data cleaning, dealing with missing data, etc.<\/p>\n
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- 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.<\/li>\n<\/ul>\n
- Your certification<\/strong> carries weight as it declares you have applicative knowledge and our job and industry ready.<\/li>\n<\/ul>\n
- The integrated curriculum<\/strong> helps you through practical industry-needed and relevant practical applications like<\/li>\n<\/ul>\n