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Machine Learning Basics to get you into Leading Tech companies.pptx
- 1. Machine Learning Basics toget you into Leading Tech companies NANDA KISHORE M Data Science Advisor Dell Technologies
- 2. What will youget at the end? ● Why Machine Learning is booming? ● Who is a Machine Learning Engineer? ● Applications of ML in industry ● Different jobs within Machine Learning ● Certifications needed for ML
- 3. Rise of ArtificialIntelligence
- 4. Why Machine Learning?/Scope of ML? ● ML is into many sectors ○ Retail - Supply Chain analytics ○ Banking and Finance ○ Medicine ○ Transportation ○ Education ○ Ecommerce ● Highly paid profession. ● More demand for people and less supply. ● Many manual jobs across industries are being automated using Data Science and Machine Learning techniques.
- 5. What is MachineLearning? Is it related to programming!? Data Algorithms Insights and Patterns in the data
- 6. What is MachineLearning? Is it related to programming!? Data Algorithms Insights and Patterns in the data Text Images Survey Data Audio Machine Learning Deep Learning Image Processing Audio Signal Processing Natural Language Processing
- 7. What is datascience? Is it related to programming!?
- 8. Machine Learning Types MLTypes Supervised Unsupervised Reinforcement Semi-supervised
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- 11. How well companiesare using ML in Businesses? ● Time series analysis - Temperature estimation ● House price prediction ● Object Detection ● Face recognition ● Chat Bots ● Voice Recognition ● Autonomous Driving ● Survey Data - Product based
- 12. How well companiesare using ML in Businesses?
- 13. How well companiesare using ML in Businesses?
- 14. What are theprogramming languages needed for ML? ● Python - Extensively used across all industries for any ML applications ● R - good for very basic ML algorithms ● SQL - Very much needed for data science backend ● MATLAB - Good when we wanted to learn
- 15. What are theprogramming languages needed for ML? ● Python libraries ○ TensorFlow - Keras ○ PyTorch ○ Scikit - learn ○ Pandas ○ OpenCV ○ NLTK ○ FastAI
- 16. Different career paths? Deep Learning Cloud Application –AWS, AZURE Data Engineering Computer Vision Timeseries forecasting and predictive analytics Natural Language Processing Big Data Analytic s AR / VR Data/ Business Analyst Data Visualization
- 17. Different career paths? ●Data Science Full stack: ○ Data Analyst -> Data cleaning, Data preprocessing, applying statistics to data ○ Applied Scientist -> Applying algorithms on the data ○ Research Scientist -> Researching new Algorithms ○ Data Engineer -> Backend Engineer for Data Science ○ Business Analyst -> Handling business documents of organization
- 18. Complete Learning Pathneeded for ML Engineer ● For ML/NLP Profile: ○ Natural Language Processing basics ○ Language Models ○ ML Basics ○ Artificial Neural Networks (ANNs) ○ Support Vector Machines (SVMs) ○ Naive Bayes, Decision Trees ○ NLTKs ○ Sentiment Analysis ○ LSTMs ○ Convolutional Neural Nets (CNNs)
- 19. Online materials andsources to quick start career in data science ● Deep Learning book by Ian Goodfellow - Book ● Introduction to Machine Learning with Python: A Guide for Data Scientists ● Kaggle competitions ● AIcrowd Competitions ● GitHub repos ● Online dataset challenges ● Hackerank and TechGig for Python challenges ● Hackerearth for hackathons, algorithms and Data structures
- 20. How to cana fresher get a job in the field of ML? ● Look for internships and freelance positions also ● Build a nice resume with good set of projects ● Update Github profiles and Kaggle profiles ● Get good understanding of few ML algorithms ● Companies are hiring students through hackathons and challenges - look out for them ● Keep improving Algorithms and Data Structures skills - common questions in interview
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