Course 1 — AI/ML Foundation (60-Class Career Track)

What is programming, compiler vs interpreter, VS Code & Jupyter setup, execution flow, writing first python scripts.

Integers, floats, strings, booleans, casting, arithmetic, comparison, logical, and membership operators.

Decision-making logic, nested conditionals, boolean conditions and short-circuiting.

For loops, while loops, range(), break, continue, pass, nested iterations.

List indexing, slicing, methods, tuple immutability, operations and performance.

Key-value mapping, dictionary methods, set mathematical operations and hashing.

String formatting (f-strings), slicing, built-in methods, regex basics.

Defining functions, def, return values, parameters, *args, **kwargs, local vs global scope, lambda.

Reading and writing text, CSV, and JSON files with open() and context managers (with).

Try-except-finally, raising exceptions, assertion debugging, error logs.

Classes, objects, __init__ constructor, attributes, methods, encapsulation.

NumPy ndarrays, array indexing, slicing, broadcasting, vectorized mathematical operations.

Pandas DataFrames, Series, CSV data ingestion, filtering, and Student Data Analysis System.

Real numbers, mathematical notations, precision, variables, linear and polynomial equations.

Linear inequalities, boundary regions, decision boundaries in 2D space.

Domain, range, linear, quadratic, exponential, and logarithmic functions in machine learning.

Cartesian planes, distances between points, Euclidean and Manhattan distance in 2D and 3D space.

Plotting functions, contour plots, visualizing mathematical curves in Matplotlib.

Vector definition, magnitude, direction, vector addition, scalar multiplication, dot product, cosine similarity.

Matrix dimensions, rows as data records, columns as features, identity & transpose matrices.

Matrix addition, matrix multiplication (dot product), determinant, inverse matrix.

Limits, continuity, rate of change, slope of tangent lines to curves.

Derivative rules, partial derivatives, gradient vector pointing toward steepest descent.

Connecting Algebra → ML Equations, Vectors → Features, Matrices → Datasets, Derivatives → Model Optimization.

Descriptive vs Inferential statistics, population vs sample, role of statistics in data science.

Measures of central tendency, calculating in Python, choosing right measure for skewed data.

Measures of dispersion, spread of data, calculating variance and standard deviation.

IQR (Interquartile Range), 25th/50th/75th percentiles, box plots and identifying outlier fences.

Sample space, simple probability, addition rule, multiplication rule, conditional probability basics.

Normal distribution (Bell curve), Empirical Rule (68-95-99.7), skewed distributions, uniform distribution.

Random sampling, stratified sampling, sampling bias, Central Limit Theorem intuition.

Pearson correlation coefficient, positive/negative correlation, correlation vs causation.

Hands-on statistical report generation and Mini Project: Sales / Student Performance Statistical Analysis.

The 8-step Data Science Lifecycle from raw problem statement to actionable insights.

Importing CSV, Excel, JSON, Web APIs, and external datasets into Pandas DataFrames.

Numerical, categorical, ordinal, datetime data types, and converting formats in Pandas.

Renaming columns, string sanitization, trimming whitespace, duplicate record detection and removal.

Detecting NaN/Null values, dropna vs fillna (mean, median, mode, forward-fill imputation strategies).

IQR rule, Z-score outlier detection, capping vs removal, analyzing impact on data integrity.

Univariate, bivariate, and multivariate analysis, summary statistics (describe, info, value_counts).

Histograms, bar charts, line graphs, scatter plots, heatmap correlations in Seaborn & Matplotlib.

Raw Dataset → Data Understanding → Cleaning → Missing Values → Outliers → Statistics → Visualization → Insights.

History of AI, Turing Test, Narrow AI vs General AI vs Super AI, AI milestones.

Understanding the hierarchy, rule-based systems vs data-driven learning models.

Reactive Machines, Limited Memory, Theory of Mind, Self-Aware AI categorization.

Search algorithms, heuristics, knowledge representation, problem formulation in AI.

Computer vision, speech recognition, autonomous systems, medical AI, finance, robotics.

Bias in AI systems, fairness, privacy, hallucination, safety, and ethical AI deployment guidelines.

Supervised vs Unsupervised vs Reinforcement learning overview, preparing datasets for ML.

Conceptualizing an end-to-end AI system problem statement and building data intake pipeline.

What is a database, DBMS vs RDBMS vs Flat Files, tables, rows, columns, Primary Key, Foreign Key, relational schema design.

Creating tables (CREATE TABLE), inserting data (INSERT INTO), basic queries (SELECT, DISTINCT, column aliases with AS).

Filtering data with WHERE, operators (AND, OR, NOT, BETWEEN, IN, LIKE, IS NULL), sorting with ORDER BY ASC/DESC, limiting with LIMIT.

Aggregate functions (COUNT, SUM, AVG, MIN, MAX), grouping data with GROUP BY, filtering groups with HAVING.

Multi-table queries with INNER JOIN and LEFT JOIN, connecting Python to SQLite/MySQL, executing queries and reading SQL directly into Pandas DataFrames (read_sql).

Selecting domain problem (e.g. Student Performance Prediction Data Analysis), setting goals and KPI metrics.

Ingesting raw dataset, handling nulls, type conversions, deduplication, and data validation.

Univariate, bivariate analysis, correlation matrices, key trend discovery, and chart exports.

Writing modular Python script, Jupyter notebook walkthrough, README.md project report.

1-on-1 viva defense, code walkthrough, portfolio demonstration, and Course 1 Certificate Assessment.