This Machine Learning & Artificial Intelligence course introduces learners to the principles, tools, and real-world applications of intelligent systems. Through hands-on training, students develop skills in Python, data modeling, neural networks, and automation—preparing them to build smart solutions and launch successful careers in today’s AI-driven world.
- Course Duration: 4 Months
- Tools: Python, R (optional), Jupyter Notebook / JupyterLab, Google Colab, Anaconda Distribution, PyCharm, NumPy, Pandas, Matplotlib, Seaborn, SciPy, VS Code, Scikit-Learn, TensorFlow, Keras , PyTorch, XGBoost, LightGBM, CatBoost, MySQL, PostgreSQL, MongoDB, SQLite, Power BI, Tableau Public, TensorBoard, OpenCV, NLTK / SpaCy, Amazon Web Services (AWS), Google Cloud Platform (GCP), MLflow, Kubeflow, etc.
- Version Control: Git & GitHub / GitLab / Bitbucket
- Targeted Audience: This course is for aspiring data scientists, developers, analysts, and tech enthusiasts seeking practical skills to build intelligent, automated, AI-driven solutions.
#510,000
#850,000
PROGRAM OVERVIEW
The Machine Learning & Artificial Intelligence (ML & AI) Program is a comprehensive, industry-driven training designed to equip learners with the core technical skills and practical experience needed to thrive in today’s data-powered world. Over four months, students gain hands-on mastery in Python programming, data analysis, machine learning model development, deep learning, neural networks, and modern AI applications.
The program begins with foundational training in Python, data handling, and exploratory data analysis before progressing into supervised and unsupervised machine learning techniques. Students learn how to clean datasets, engineer features, build predictive models, evaluate performance, and optimize accuracy using real-world datasets.
As the course advances, learners dive into deep learning with TensorFlow and Keras, building neural networks for computer vision, natural language processing, and automation tasks. They also explore ethical AI principles, real-world use cases, and practical deployment strategies using tools like Streamlit, Flask, and cloud platforms.
The program ends with a full capstone project, allowing students to design, train, and deploy an AI solution from scratch.
This course is ideal for beginners, tech enthusiasts, and professionals looking to break into data-driven roles such as Machine Learning Engineer, Data Analyst, AI Specialist, or Research Assistant.
What You'll Learn
Module 1: Introduction to AI & Machine Learning (Week 1)
What is AI, ML, Deep Learning & Data Science
Industry applications (FinTech, HealthTech, Robotics, Marketing, etc.)
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
AI workflow & lifecycle
Module 2: Python for Machine Learning (Week 2)
Python basics & setup (Anaconda, Jupyter, VS Code)
Variables, loops, functions, data types
Working with libraries: NumPy, Pandas
Exploratory Data Analysis (EDA) with Pandas
Module 3: Data Visualization (Week 3-4)
Matplotlib
Seaborn
Plotly (interactive charts)
Building insights from data
Practical Milestone
Build a mini project: Customer Insights Dashboard using Python & Pandas
Module 4: Data Preprocessing & Feature Engineering (Week 5)
Handling missing data
Outliers & noise removal
One-hot encoding & feature scaling
Feature selection techniques
Module 5: Machine Learning Algorithms (Week 6)
Linear & Logistic Regression
Decision Trees & Random Forest
Support Vector Machines
Naïve Bayes
K-Means Clustering
K-Nearest Neighbors
Module 6: Model Evaluation & Optimization (Week 7-8)
Accuracy, precision, recall, F1-score
Confusion matrix
Cross-validation
Bias–variance tradeoff
Hyperparameter tuning (GridSearchCV, RandomizedSearch)
Practical Milestone
✔ Build a complete ML model project: Loan Approval Prediction System
Module 7: Neural Network Foundations (Week 9)
What are Neural Networks?
Activation functions
Loss functions
Backpropagation & optimization
Module 8: Deep Learning with TensorFlow & Keras (Week 10)
Building sequential models
Image classification (CNNs)
Text classification (RNNs/LSTMs)
Transfer learning basics
Module 9: Computer Vision & NLP Basics (Week 11-12)
CNN architectures (VGG, ResNet, MobileNet)
Object detection concepts
NLP Essentials: Tokenization, Lemmatization, Embeddings
Chatbot basics with NLP
Practical Milestone
✔ Two guided projects:
Face Mask Detection with CNN
Movie Review Sentiment Analysis with NLP
Module 10: AI for Business & Real-World Applications (Week 13)
Recommendation systems
Fraud detection systems
Predictive analytics
AI in robotics & automation
Ethical AI & responsible ML
Module 11: Deployment, MLOps & Model Serving (Week 14)
Introduction to MLOps
Model deployment with:
Streamlit
FastAPI
Flask
Version control with Git & GitHub
Using Docker for ML deployment
Hosting models on the cloud (AWS / GCP / Azure options)
Module 12: Capstone Project Development (Week 15-16)
Students select an end-to-end AI project
Dataset selection
Model building & tuning
Deployment & documentation
Final presentation & defense
Capstone Project Examples
AI-powered Customer Churn Predictor
Medical Image Classification (Pneumonia Detection)
AI Chatbot for Customer Support
Stock Price Movement Prediction
Real-time Object Detection App
Career Path After Completion
Machine Learning Engineer
Data Scientist
AI Engineer / Artificial Intelligence Specialist
Data Analyst (Advanced)
Deep Learning Engineer
NLP (Natural Language Processing) Engineer
Computer Vision Engineer
AI Researcher / Research Analyst
Data Engineer
MLOps Engineer
Robotics & Automation AI Engineer, etc.
A forward-thinking tech and vocational training institute committed to equipping students with the digital skills required to thrive in today’s innovation-driven world.
SOLUTIONS
- Cybersecurity Services
- Web Design & Development
- Mobile App Design & Development
- Cloud Infrastructures
- IT Consultancy
- Custom Software Solutions
- Graphic Design & Branding
- Digital Marketing & SMM
© Copyright 2022 Adavi Digital Institute. All Rights Reserved.