TechPledge Certified Machine Learning H20 Bootcamp

• Lectures (PDF)
• Lab Manual (PDF)
• Recorded Video
• Xtream Lab / LOD Access
• Cloud Pass
• Project File

- Collect and clean data from various sources, such as databases, APIs, web scraping, etc.
- Explore and analyze data using descriptive and inferential statistics, data visualization, and other techniques.
- Build and test predictive models using machine learning algorithms, such as regression, classification, clustering, etc.
- Communicate and present the results and insights to stakeholders, such as managers, clients, or developers.
- Deploy and monitor the models in production environments, using tools such as cloud computing, containers, or pipelines.

• - Introduction to Machine Learning: Learn the basic concepts and terminology of machine learning, such as supervised and unsupervised learning, classification and regression, model evaluation and validation.
• - Data Preprocessing and Visualization: Learn how to prepare and explore data for machine learning, such as handling missing values, outliers, categorical variables, scaling and normalization. Learn how to use various visualization techniques to gain insights from data, such as histograms, scatter plots, box plots, heat maps and more.
• - Linear Models and Optimization: Learn how to use linear models for regression and classification tasks, such as linear regression, logistic regression, ridge and lasso regularization. Learn how to use gradient descent and other optimization algorithms to find the optimal parameters for the models.
• - Neural Networks and Deep Learning: Learn how to build and train neural networks for various tasks, such as image recognition, natural language processing, sentiment analysis and more. Learn how to use popular frameworks such as TensorFlow and PyTorch to implement deep learning models. Learn how to use convolutional neural networks, recurrent neural networks, attention mechanisms and transformers.
• - Ensemble Methods and Boosting: Learn how to combine multiple models to improve performance and reduce overfitting, such as bagging, random forests, stacking and boosting. Learn how to use popular boosting algorithms such as AdaBoost, Gradient Boosting and XGBoost.
• - Unsupervised Learning and Dimensionality Reduction: Learn how to use unsupervised learning techniques to discover patterns and structure in data, such as clustering, anomaly detection, association rules and recommender systems. Learn how to use dimensionality reduction techniques to reduce the complexity and noise in data, such as principal component analysis, singular value decomposition and t-distributed stochastic neighbor embedding.
• - Machine Learning in Practice: Learn how to apply machine learning techniques to real-world problems and datasets. Learn how to use cloud computing platforms such as AWS and Azure to deploy machine learning models. Learn how to use tools such as Scikit-Learn, Pandas, Matplotlib and Seaborn to perform machine learning tasks in Python.
   

Bootcamp Syllabus