Artificial Intelligence Workshop - Zero to Hero!

Artificial Intelligence Workshop - Zero to Hero!

Join us for an exciting online workshop on Artificial Intelligence! Our workshop is designed for beginners who want to learn about AI from scratch and become a hero in the field. The workshop will start on Sat Jun 17 2023 at 18:00:00 GMT+0530 (India Standard Time).

In this workshop, you will learn the basics of AI, including machine learning, deep learning, and natural language processing. You will also get hands-on experience with popular AI tools and frameworks, such as TensorFlow, Keras, and PyTorch. Our expert instructors will guide you through the entire process and help you build your own AI project.

This workshop is perfect for anyone who wants to start a career in AI or simply wants to learn more about this exciting field. So, what are you waiting for? Sign up now and become an AI hero!

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course can be completed by either doing the Python tutorials. This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

• Part 1 - Data Preprocessing
• Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Part 4 - Clustering: K-Means, Hierarchical Clustering
• Part 5 - Association Rule Learning: Apriori, Eclat
• Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
• Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now. Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

What you’ll learn

• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Handle advanced techniques like Dimensionality Reduction
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Are there any course requirements or prerequisites?

Just some high school mathematics level.

Who this course is for:

• Anyone interested in Machine Learning.
• Students who have at least high school knowledge in math and who want to start learning Machine Learning.
• Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
• Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
• Any students in college who want to start a career in Data Science.
• Any data analysts who want to level up in Machine Learning.
• Any people who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Machine Learning tools.