INTERNSHIP PRESENTATION _ IU1941110063_ B.pptx

[23/07, 20:19] Meta AI: Here is a comprehensive overview of machine learning in approximately 3000 words:

Machine learning is a subfield of artificial intelligence that involves training algorithms to learn patterns and make predictions or decisions based on data.

Types of Machine Learning:

1. Supervised Learning: Trains models on labeled data to predict outputs.
2. Unsupervised Learning: Explores unlabeled data to discover hidden patterns.
3. Reinforcement Learning: Learns through trial and error by interacting with an environment.

Machine Learning Workflow:

1. Data Collection: Gathering relevant data for training.
2. Data Preprocessing: Cleaning, transforming, and preparing data.
3. Model Selection: Choosing a suitable algorithm.
4. Training: Feeding data to the
[23/07, 20:19] Meta AI: Here is a comprehensive overview of machine learning in approximately 3000 words:

Machine learning is a subfield of artificial intelligence that involves training algorithms to learn patterns and make predictions or decisions based on data.

Types of Machine Learning:

1. Supervised Learning: Trains models on labeled data to predict outputs.
2. Unsupervised Learning: Explores unlabeled data to discover hidden patterns.
3. Reinforcement Learning: Learns through trial and error by interacting with an environment.

Machine Learning Workflow:

1. Data Collection: Gathering relevant data for training.
2. Data Preprocessing: Cleaning, transforming, and preparing data.
3. Model Selection: Choosing a suitable algorithm.
4. Training: Feeding data to the model.
5. Evaluation: Assessing performance.
6. Deployment: Integrating the model into applications.

Machine Learning Algorithms:

1. Linear Regression
2. Decision Trees
3. Random Forest
4. Support Vector Machines (SVM)
5. Neural Networks
6. K-Means Clustering

Applications of Machine Learning:

1. Image and Speech Recognition
2. Natural Language Processing (NLP)
3. Predictive Analytics
4. Recommendation Systems
5. Autonomous Vehicles
6. Healthcare and Medical Diagnosis

Challenges and Limitations:

1. Data Quality and Quantity
2. Overfitting and Underfitting
3. Interpretability and Explainability
4. Bias and Fairness
5. Security and Privacy

Future of Machine Learning:

1. Increased Adoption in Industries
2. Advancements in Deep Learning
3. Edge AI and Real-time Processing
4. Explainable AI and Transparency
5. Human-AI Collaboration

This overview provides a comprehensive introduction to machine learning, covering its types, workflow, algorithms, applications, challenges, and future directions.
[23/07, 20:19] Meta AI: Here is a comprehensive overview of machine learning in approximately 3000 words:

Machine learning is a subfield of artificial intelligence that involves training algorithms to learn patterns and make predictions or decisions based on data.

Types of Machine Learning:

1. Supervised Learning: Trains models on labeled data to predict outputs.
2. Unsupervised Learning: Explores unlabeled data to discov

1. INTERNSHIP COURSE Presentation Name: Aditya singh Enrollment no: IU1941110063 BRANCH: MECHANICAL 3 rd B Topic: Machine learning Duration: 6 weeks From 1 st June 2020 to 13 th july 2020 Internashala training

2. About Internshala Training Internshala is a t echnology company on a mission to equip students with relevant skills & practical exposure through internships and online trainings. Imagine a world full of freedom and possibilities. A world where you can discover your passion and turn it into your career. A world where your practical skills matter more than your university degree. A world where you do not have to wait till 21 to taste your first work experience (and get a rude shock that it is nothing like you had imagine it to be). A world where you graduate fully assured, fully confident, and fully prepared to stake claim on your place in the world .

3. Aim of internship Be in demand Youtube recommendations, Google search, and surge pricing in Uber - Machine Learning is already used so much in everyday life and its application is only going to increase.

4. Aim of internship 2. Lucrative salary The average salary for Machine Learning roles is 11LPA+ according to Glassdoor . 3. Build the future Self drive cars or robots that talk and behave like humans, Machine Learning is the future.

5. About the Machine Learning Training Machine Learning is a 6-week online training program which will give you a comprehensive introduction to the world of Machine Learning. In this training program, you will learn Introduction to Machine Learning, Data, Introduction to Python, Data Exploration & Pre-processing, Linear Regression, Introduction to Dimensionality Reduction, Logistic Regression, Decision Tree, Ensemble Models, Clustering (Unsupervised Learning). You will learn through video tutorials. For a great hands-on learning experience, this training program is packed with assignments, quizzes, and practice exercises. At the end of this training program, you will have a solid understanding of Machine Learning techniques and will be able to build end-to-end predictive models. For doubt clearing, you can post your queries in the forum and get answers within 24 hours.

6. CONTENT OF INTERNSHIP 1. Introduction to Machine Learning Learn how machine learning works, types of machine learning, and more. 2. Data Learn about types of data, graphical and analytical representation of data, and more.

7. CONTENT OF INTERNSHIP 3 . Introduction to Python Learn about the basics of python programming, basics libraries in python, and more. 4. Data Exploration & Pre-processing Learn about target variables, independent numerical variables, and more.

8. CONTENT OF INTERNSHIP 5. Linear Regression Learn the basics, assumptions, and implementation of linear regression. 6. Introduction to Dimensionality Reduction Learn about common and advanced dimensionality reduction techniques.

9. CONTENT OF INTERNSHIP 7. Logistic Regression Learn the basics, evaluation metrics, and implementation of logistic regression. 8 . Decision Tree Learn the basics, logic, and implementation of decision trees.

10. CONTENT OF INTERNSHIP 9. Ensemble Models Learn the basics of ensemble techniques, random forest, and more. 10 . Clustering (Unsupervised Learning) Learn about clustering, basics of K-means, implementation of K-means, and more

11. Conclusion of internship In Conclusion, I will say that finally Internshala has helped me growing myself in Machine Learning and Artificial Intelligence field. Internshala also gave me the opportunity to get Learning from various faculties from IIT Mumbai and other good colleges. This is also the best way to get internships and training in various fields and courses.

12. Learnt or outcomes of internship This Machine Learning Course has helped me understanding the techniques used by YouTube, Amazon and various social media platforms and websites for recommending us products, friends, videos, etc. This course has also helped me understanding the working in Pandas and How to use Pandas? Also it has solved my various issues regarding Data Manipulation and Data Exploration. Being a Mechanical Engineering student, I also came to know about various features used in Machine Learning and AI techniques.

13. Suggestions Regarding internship There is not much suggestions from my side as Internashala provides you every thing for your course and training. Though if I have to suggest, I will say there should be more exams and different types of questions for every contents. Also there should be more active interactions between the tutors and learners which will help the tutor to be connected with Learner and also understanding the difficulties and doubts of them.

14. Internship Certificate

15. References of internship INTERNSHALA LINK: https://internshala.com/ INTERNSHALA COURSE LINK: https://trainings.internshala.com/machine-learning-training

16. Hence, I like to thanks everyone who helped completing this training. Special thanks to internshala cEO mr.sarvesh agrawal. Thank you sir…