• What data science is all about & why it matters.
• Businesses use data to make smart choices.
• A look at the tools you’ll use, such as Python and R.

• The basics of stats, probability, and reading data.
• Breaking down descriptive and inferential stats.
• How to test hypotheses with real examples.

• Getting comfy with Python & its handy libraries.
• Setting up your Python workspace step-by-step.
• Exploring key library like NumPy & Pandas.
• Learning simple coding tricks for data tasks.

Tips for fixing messy data before analysis.

Dealing with missing numbers and odd outliers.

Making data consistent with normalization.

Turning raw data into something usable.

• Seeing patterns by visualizing your data.
• Using cool tools like Matplotlib for charts.
• Spotting trends and connections in the numbers.
• Summing up what your data is trying to tell you.

• What ML is and how it fits into data science.
• Comparing supervised and unsupervised methods.
• A quick tour of popular ML techniques.
• Real-life examples of ML in action.

• Getting the hang of supervised learning basics.
• Splitting data into training and testing sets.
• Checking how well your model performs.
• Seeing supervised learning at work with examples.

• Digging deep into how linear regression works.
• Simple vs. multiple regression explained.
• What to watch for in model assumptions.
• Running it yourself with Python code.

• Using logistic regression for yes/no decisions.
• Grasping odds and probability concepts.
• Ways to check if your model is on track.
• Real-world cases where it shines.

How decision trees help make choices.

Growing and trimming trees effectively.

Figuring out which features matter most.

Using trees to solve practical problems.

• Why combining trees with random forests works.
• Learning about bagging and boosting tricks.
• Tweaking settings for better results.
• Why forests beat single trees sometimes.

• What SVMs do for classification and more.
• How kernel functions change the game.
• Handling tricky non-linear data patterns.
• Trying SVM with hands-on coding.

• K-Means algorithm grouping the data.
• Picking the right number of clusters.
• Ways to measure how good your clusters are.
• Using clusters to segment customer data.

• Putting together a project with what you’ve learned.
• Gathering and organizing your data.
• Building and testing your own model.

• The basics of AI & how it differs from ML.
• A quick history of AI’s journey.
• What makes up an AI system?

• The building blocks of neural networks.
• How neurons work with activation functions.
• Moving data forward and back in training.
• Creating a basic network from scratch.

• What deep learning brings to the table.
• A peek at frameworks like TensorFlow.
• Understanding layers and network complexity.
• Seeing deep learning in action with images and sound.

• The essentials of NLP and text handling.
• Breaking down text with tokenization.
• Analyzing feelings with sentiment tools.
• Building chatbots or translation systems.

• Spotting & fixing bias in algorithms.
• Keeping AI fair and transparent.
• How laws and society view AI challenges.

• What’s new and exciting in AI today.
• Where AI research is headed next.
• How AI is shaping automation.
• Future job opportunities in the AI world.