Become a ML EngineerMachine Learning Course in Pune

Types of Machine Learning

Most data interview take-home tests include a messy CSV — candidates who can clean it quickly and correctly proceed; others do not

Python for ML

Pivot tables and VLOOKUP are still tested in Excel rounds at consulting and banking firms — done at professional speed here

Linear Algebra & Calculus Review

Python Pandas is the tool used by 91% of data analysts in Indian IT companies — you start using it in Week 1, not Week 5

Data Preprocessing

Missing data handling strategies are a common interview question — knowing when to impute vs drop is a judgment skill this module builds

Regression Algorithms

GROUP BY, HAVING, and aggregate functions are tested in every data analyst SQL round — candidates who get them right move forward

Classification Algorithms

JOINs (INNER, LEFT, RIGHT) are asked in every SQL interview — being able to explain the difference with a diagram is the expected standard

Ensemble Methods

Window functions (ROW_NUMBER, RANK, LEAD, LAG) are what separate junior analysts from mid-level ones — most courses do not teach them at all

Model Tuning

Subqueries and CTEs are how complex business questions are answered in SQL — the module covers both so you can write readable, maintainable queries

Clustering

Dashboard design principles (correct chart type, colour for insight, not decoration) are what separate a useful dashboard from a confusing one — and interviewers can tell the difference immediately

Dimensionality Reduction

DAX in Power BI is the most-asked-about tool feature in analytics interviews — the module covers the formulas used in actual business reports

Anomaly Detection

The "walk me through your dashboard" interview question requires you to tell a data story — this module teaches narrative structure for data presentations

Q-Learning Basics

Data refresh and live connections are what make dashboards useful in a production environment — covered from a business context here

Neural Network Architecture

Descriptive statistics (mean, median, mode, standard deviation) are baseline — but interviewers ask you to interpret them in business context, not just calculate them

Backpropagation

Correlation vs causation is a critical thinking test that appears in almost every data interview — having a clear, confident answer with an example is expected

CNNs & RNNs

Hypothesis testing (t-test, chi-square) is how A/B test results are evaluated at product companies — understanding this makes you useful in product analytics roles

Transfer Learning

Normal distribution and its business implications are tested at consulting firms and in financial analytics roles — covered with practical business examples here

Model Versioning with MLflow

Linear regression is the most common model in business analytics — being able to interpret the output (not just run it) is what interviewers test

Feature Stores

Classification models (logistic regression, decision trees) are used in credit scoring, churn prediction, and fraud detection — the industries most likely to hire you first

Model Monitoring

Model evaluation metrics (accuracy, precision, recall, F1) are asked in data science interviews and increasingly in analytics interviews too

Deployment Pipelines

Feature engineering is where most of the value in ML comes from — this is the part that requires domain knowledge, which data analysts have over pure ML engineers