Industry-Focused Data Science & AI/ML Program by Anirudh K

The Industry-Focused Data Science & AI/ML Program is a comprehensive online course designed for aspiring data scientists, machine learning engineers, and analytics professionals. It provides a step-by-step learning path from programming fundamentals to advanced AI/ML techniques, model deployment, and interview preparation. This course is ideal for students, fresh graduates, and professionals looking to gain industry-ready skills in data handling, analytics, machine learning, deep learning, and natural language processing.

Through hands-on projects and real-world datasets, students learn to analyze data, build predictive models, deploy applications, and develop a portfolio to demonstrate their practical expertise.

Industry-Focused Data Science & AI/ML Program

PHASE 1 — Programming Foundation (Python for Data Work)

Objective: Build coding confidence and thinking ability required for data handling.

Topics Covered:

• Python syntax and execution flow

• Variables, operators, conditional logic

• Loops and problem solving patterns

• Functions and lambda expressions

• Lists, tuples, dictionaries and sets

• File handling (CSV, JSON, TXT)

• Writing reusable code

Practical Work:

• Student report analyzer

• Log file parser

• Dataset reader utility

Outcome:
Able to write structured programs and manipulate raw data files.

PHASE 2 — Data Analysis & Data Cleaning

Objective: Perform real data analyst tasks on messy datasets.

Topics Covered:

• Introduction to NumPy arrays and vectorization

• Pandas DataFrame operations

• Handling missing values

• Removing duplicates & inconsistencies

• Data transformation & feature creation

• Groupby operations and aggregations

• Exploratory Data Analysis (EDA)

• Data visualization using Matplotlib & Seaborn

Datasets Used:

• Sales dataset

• Movie dataset

• Sports dataset

Outcome:
Able to clean and analyze real-world datasets and extract insights.

PHASE 3 — Statistics for Machine Learning

Objective: Understand how models make decisions.

Topics Covered:

• Types of data & distributions

• Mean, median, variance interpretation

• Correlation vs causation

• Outlier detection logic

• Bias-variance tradeoff

• Sampling concepts

• Hypothesis testing fundamentals

Outcome:
Able to interpret model results instead of blindly trusting accuracy.

PHASE 4 — Machine Learning Fundamentals

Objective: Learn core supervised learning algorithms.

Topics Covered:

• Machine learning workflow

• Train-test split logic

• Regression vs classification problems

• Linear Regression

• Logistic Regression

• K-Nearest Neighbors

• Decision Trees

• Random Forest

• Support Vector Machine

• Naive Bayes

Projects:

• Salary prediction

• Loan approval classification

• Student performance prediction

Outcome:
Able to train and evaluate predictive models.

PHASE 5 — Model Optimization & Feature Engineering

Objective: Improve model performance and reliability.

Topics Covered:

• Overfitting & underfitting diagnosis

• Cross validation

• Hyperparameter tuning

• Feature scaling & encoding

• Handling imbalanced datasets

• Pipeline building

Outcome:
Able to improve weak models into usable models.

PHASE 6 — Unsupervised Learning

Objective: Discover hidden patterns in data.

Topics Covered:

• Clustering intuition

• K-Means clustering

• Elbow method

• Customer segmentation

• Dimensionality reduction basics

Project:

• Customer segmentation system

Outcome:
Able to perform pattern discovery tasks.

PHASE 7 — Deep Learning Basics

Objective: Understand neural networks practically.

Topics Covered:

• Perceptron concept

• Artificial Neural Networks

• Activation functions intuition

• Training neural networks using Keras/TensorFlow

• Model evaluation

Project:

• Image classification system

Outcome:
Able to build basic deep learning models.

PHASE 8 — Natural Language Processing

Objective: Work with text data.

Topics Covered:

• Text preprocessing techniques

• Tokenization and vectorization

• TF-IDF implementation

• Sentiment analysis

• Spam detection

Project:

• Review sentiment classifier

Outcome:
Able to process and analyze textual data.

PHASE 9 — Model Deployment

Objective: Convert models into usable applications.

Topics Covered:

• Saving and loading models

• Building Streamlit applications

• Creating prediction interfaces

• Project structure for portfolio

• GitHub project publishing

Capstone Project:
End-to-end machine learning application with UI

Outcome:
Able to present working projects instead of notebooks.

PHASE 10 — Interview Preparation

Objective: Make students employable.

Topics Covered:

• Resume building for tech roles

• How to explain projects

• Common ML interview questions

• Problem solving approach

• Portfolio review

Outcome:
Prepared for Data Analyst / ML Engineer entry-level interviews.

Final Deliverables

Students complete:

• Multiple datasets analysis

• 4–6 machine learning projects

• 1 deployed end-to-end project

• GitHub portfolio

Teaching Method

The course is delivered online through live interactive sessions, combining lectures, coding exercises, real datasets, and project-based learning. Students receive personalized guidance, portfolio support, and preparation for real-world data science challenges.

Why This Course

This program offers an end-to-end, industry-focused learning experience, combining Python programming, data analysis, ML, AI, deep learning, NLP, and model deployment. Students gain practical skills through hands-on projects and build a portfolio that demonstrates industry readiness.

Benefits and Outcomes

By completing this course, students will:
• Develop proficiency in Python, data analytics, ML, and AI
• Perform data cleaning, analysis, visualization, and feature engineering
• Build and deploy supervised, unsupervised, and deep learning models
• Apply NLP techniques for text data
• Create portfolio-ready projects on GitHub and Streamlit
• Be prepared for data science, ML, and AI entry-level roles