Introduction to Data Science and Machine Learning Bootcamp

6 Weeks of Intense Learning!

Data Wrangling (1 week)

Any Data Science and Machine Learning project starts with becoming an expert on the data. We are going to dive into the different techniques to slice and dice the data with Pandas:

• Exploratory Data Analysis
• Advanced Data Manipulation

Data Visualization (0.5 week)

Visualizing the data allows one to uncover unexpected mechanisms and to present results in a more explicitly and transparent manner:

• Data visualization with Matplotlib
• Data visualization with Seaborn

Probability and Statistics (1.5 weeks)

A solid grounding in probability and statistics is essential for interpreting data, building models, and making data-driven decisions:

• Probability Fundamentals
• Descriptive  Statistics
• Statistical Inference

Supervised Learning (1 week)

Supervised learning is at the core of 99% of ML projects in the industry. It involves building predictive models with labeled data.

• Regression and Classification models
• Model quality estimation

Unsupervised Learning (1 week)

Unsupervised learning captures concepts like clustering and dimension reduction. It is about discovering patterns and structures in unlabeled data.

• Clustering
• Dimension reduction

Feature Engineering (1 week)

Feature engineering is about preparing the data to train a model. We need to be able to deal with categorical variables and missing values, and we need to extract the right features or create better ones:

• Feature Extraction and Selection
• Encoding Techniques
• Scaling and Normalization