Machine learning mini project
Explore a dataset, prepare the features and apply a learning algorithm to a practical problem.
Python · Pandas · NumPyDATA & AI · CAREER PROGRAM
Data Science with AI Course in Calicut
Go from exploring data to building intelligent systems. Learn Python, machine learning and AI through practical work.
- Total duration
- 7 months
- Days a week
- 5
- Hours per day
- 2–3
Hands-on training
Expert trainers
Practical projects
Interview preparation
Placement assistance
Two certificates
On this page
THE OVERVIEW
Build an understanding of intelligent systems.
Start with coding, Python and statistics, then explore how machine learning models learn from data. You’ll work through supervised, unsupervised and reinforcement learning before moving into deep learning.
The syllabus also covers natural language processing, computer vision and visual storytelling with Tableau. Mini projects connect the concepts to practical applications, from sentiment analysis to working with images.
Who can join?
Find a starting point that fits your background and goals.
- Students & graduates
- Aspiring AI practitioners
- Developers upskilling
- Career switchers
WHAT YOU’LL LEARN
Skills you can put to work.
- Data wrangling
- Statistics & probability
- Machine learning
- Deep learning
- Neural networks
- Natural language processing
- Computer vision
- Data visualisation
YOUR LEARNING PATH
From foundations
to practice.
13 modules, with a clear path through the concepts, tools and practical work.
01Data science foundations
- Introduction Concept Of Data Science
- Installation Of All Necessary Modules
02Coding concepts & databases
- Variable declaration. Variables are containers for storing values
- Control structures. A control structure specifies the flow of control in a program
- Data structures
- Object-oriented programming
- Debugging
- Programming tools
03Python programming
- Introduction To Python And Installation, Introducing Pycharm, Variable In Python
- List, Set, Dictionary, Tuple
- Data Types In Python, Operators
- Functions And Modules
- User Input
- If, if Else, If Elif
- Loop In Python: For, While
- Loop Control Statements: Break, Continue, Pass
- Functions In Python
- Filter Map Reduce, Anonymous Functions
- Decorators
- Special Variables And Special Functions
- Object-Oriented Programming
- Class And Objects, Init Method
- Types Of Variables And Types Of Methods
- Inheritance
- Iterators, Generators, File Handling
- Database Connectivity
- Github Integration
04NumPy, Pandas & Matplotlib
- Numpy: Installation And Introduction, Numpy Array, Different Ways To Create Numpy Array, 2d Array, Numpy Array Operations, Slicing, Linear Algebra, Load Data From External Source
- Pandas: Introduction To Pandas, Data Frames, Different Ways To Create Dataframes, Read And Write CSV, Excel, Textfile, SQL Read And Write, Slicing, Reshaping, Operations With Dataframe, Datetime Index, Data Cleaning, Group By, Merge, Concatenate, Analytics Using Pandas
- Matplotlib: Introduction To Matplotlib, Line Plot, Barplot, Scatter Plot, Stack Plot, Pie Chart, Plotting With Seaborn And Advanced Concepts
05Statistics
- Data, Types Of Data, Types Of Statistics, Central Tendency
- Measure Of Spread, Variables And Types Of Variables
- Basics Of Probability
- Random Variables And Types Of Random Variables
- Types Of Distributions (Bernoulli Distribution, Uniform Distribution, Geometric Distributions, Binomial Distribution, Poisson Distribution, Normal Distribution, Exponential Distribution)
- Covariance And Correlation
- Conditional Probability
- Central Limit Theorem And Sampling Distributions
- Frequency (Absolute Frequency, Relative Frequency, Ratio, Rate, Proportion)
- Probability Mass Function, Probability Density Function, Cumulative Distribution Function
- Descriptive And Inferential Statistics (Skewness And Kurtosis, Point Of Estimations-confidence Interval)
- Measure Of Errors (Standard Error, Relative Error, Confidence Interval)
06Machine learning foundations
- What Is Machine Learning?
- How Does Machine Learning Work?
- Types Of Machine Learning (Supervised Learning, Unsupervised Learning, and Reinforcement Learning)
- AI Vs ML Vs DL
- Data Cleaning
- Data Reduction (PCA)
07Supervised learning
- Linear Regression
- Logistic Regression
- Support Vector Machine (SVM)
- Decision Tree Algorithm
- Random Forest Algorithm
- K-Nearest Neighbors (KNN)
- Naive Bayes Algorithm
08Unsupervised learning
- K Means Algorithm
- Hierarchical Clustering
- Association Rules
- Apriori Algorithm
09Reinforcement learning
- Q Learning
- Machine Learning Mini Project
10Deep learning
- What Is Deep Learning?
- What Is AI?
- Applications Of AI
- Limitations Of Machine Learning
- How Deep Learning Works
- Artificial Neuron
- Perceptron (Single Layer And Multilayer)
- What Are The Activation Function And Different Types Of Activation Functions
- Back Propagation And Gradient Descent Optimizers
- Vanishing Gradient And Exploding Gradient Problems
- What Are Tensors And TensorFlow Basic Operations
- Keras Basic Operations
- PyTorch Basic Operations
- Fully Connected Network
- What Is CNN (Convolutional Neural Network)?
- CNN Layers
- What Is RNN (Recurrent Neural Network)?
- Difference between CNN and RNN
- Structure of RNN (RNN Layers)
- The Disadvantage of Traditional RNN
- LSTM (Long Short Term Memory)
- GRU
- Encoders and Decoders
- What is Chatbots?
- Chatbot walkthrough
- Deep Learning Project
11Natural language processing
- What is NLP..?
- Applications of NLP
- NLP Basic Steps
- Tokenization
- Stemming
- Lemmatization
- POS (Parts of Speech) tags
- NER (Named Entity Recognition)
- Chunking
- NLTK
- What Is Stop Words? How To Remove It?
- Spacy
- Word Embedding Or Vectorization
- Tf-idf
- Count Vectorizer
- Bag Of Words
- Unigrams, Bigrams, Ngrams
- NLP Models (LSTM & GRU)
- Pipeline Transformers
- Sentiment Analysis
- Speech To Text And Text To Speech Conversion Using Python
- Building Alexa
- NLP Mini Project
12Computer vision
- What Is An Image?
- OpenCV
- What Is Anaconda?, Installation Of Anaconda
- How To Read, Display, And Write An Image
- Drawing Functions In OpenCV (Line, Rectangle, Ellipse)
- How To Put Text To An Image
- Open Webcam Using cv2
- Open And Save A Video
- Channel Conversion (Gray Scale, HSV, YCbCr)
- Copy & Paste Images
- What Is Image Thresholding..?
- Different Methods In Thresholding
- Image Filtering
- Histogram Of An Image
- Morphological Transformations
- Erosion
- Dilation
- Closing
- Opening
- Gradient
- Top Hat
- Black Hat
- Edge Detection
- Contouring
- Face Detection Using Haarcascades
- Eye Detection
- Smile Detection
- Face Recognition Using Dlib
- CV Project
13Tableau
- Tableau Intro
- Installation Of Tableau
- Creating Charts In Tableau
- Filters In Tableau
- Creating Calculated Field
- Applying Analytics To The Worksheet
- Dashboard In Tableau
- Tableau Mini Project
Get familiar with the tools.
A practical toolkit for your learning journey.
- Python
- Pandas
- NumPy
- Jupyter
- TensorFlow
- PyTorch
- OpenCV
- Tableau
LEARN BY DOING
Put your learning into practice.
Explore practical work connected to the topics in your syllabus.
Natural language project
Explore text preparation and sentiment analysis, connecting language-processing steps into a working flow.
Python · NLTK · spaCyComputer vision project
Work with images, filtering and detection techniques as you put computer vision concepts into practice.
Python · OpenCVOne learning journey. Two certificates.
Complete your training and practical work to receive a NACTET certificate alongside your Gen Corpus Data Hub certificate.
-
NACTET certificate -
Gen Corpus certificate
BEYOND THE CLASSROOM
Prepare for your next chapter.
Practical support to help you present your skills and approach opportunities with confidence.
Resume & portfolio
Present your skills, learning and practical work clearly.
Interview practice
Prepare through mock interviews and technical discussions.
Career guidance
Talk through your goals and the opportunities you want to explore.
Placement assistance
Get support as you prepare for your next professional step.
LEARNER STORIES
Learning, in
their words.
Building a dashboard helped me connect the lessons. I could explain the patterns I found, and why they mattered.
Aarya N.Data Analytics with Gen AI
BEFORE YOU BEGIN
Your questions,
answered.
How long is the course?
Data Science with AI runs for 7 months, with classes 5 days a week for 2–3 hours a day.
What coding and maths will I study?
The course covers Python from the basics, then statistics, probability and the mathematics used to understand data and learning models.
Will I learn more than machine learning?
Yes. The syllabus also includes deep learning, neural networks, natural language processing, computer vision and Tableau, with dedicated practical projects.
What are the batch timings?
Classes run 5 days a week for 2–3 hours a day. Contact our career team for the next available batch and its exact class timings.
Which certificates will I receive?
After successful completion of the training and practical work, you receive a NACTET certificate alongside your Gen Corpus Data Hub certificate.
What placement support is included?
Support includes resume and portfolio preparation, interview practice, mock interviews and career guidance. Speak with our team about the placement assistance available for your course.
YOUR NEXT CHAPTER
Ready to start
something new?
Talk to a career expert about Data Science with AI, your goals and your next step.