Artificial Intelligence

Master AI concepts, Python programming, Machine Learning, Deep Learning, NLP, and Computer Vision through hands-on projects and real-time applications.

Learning Mode

Online

Real-Time Projects

Hands-On Practical Learning

Expert Mentorship

Guidance from Industry Experts

Career Support

Placement Assistance & Certification

About the Course

The Artificial Intelligence Course by SkillDzire covers Python programming, data manipulation using Numpy & Pandas, ML workflow, supervised & unsupervised learning, Deep Learning, NLP, and Computer Vision. Gain hands-on experience with real-world AI projects and prepare for a career in data science and AI applications.

Comprehensive Python and AI programming skills

Hands-on experience with real-time AI projects

Master ML, DL, NLP & Computer Vision workflows

Industry-recognized SkillDzire certification & career guidance

Curriculum

Python Fundamentals

  • Introduction to Python, setting up IDE
  • Basic syntax, variables, data types, type conversion
  • Operators: Arithmetic, relational, logical, assignment
  • Control flow: if, if-else, elif, nested conditions
  • Loops: for, while, nested loops, break & continue
  • Functions: def, return, parameters, scope
  • Error handling: try-except, finally
  • File operations: open, read, write, append

Numpy For Numerical Computing

  • Introduction to NumPy, installing & importing
  • Creating arrays: np.array, zeros, ones, arange, linspace
  • Array attributes: dtype, shape, size, ndim
  • Indexing and slicing: 1D, 2D, 3D
  • Broadcasting and vectorized operations
  • Mathematical functions: sum, mean, median, std, var
  • Linear algebra: dot, matmul, transpose, inverse
  • Random module: np.random, seeding, normal, randint
  • Missing data: np.nan, np.isnan, handling techniques
  • File I/O: np.loadtxt, np.savetxt, np.save, np.load

Pandas For Data Manipulation

  • Introduction to Pandas and data science relevance
  • Data structures: Series and DataFrame
  • Creating DataFrames from dict, lists, CSV, Excel
  • Inspecting data: Head, tail, shape, describe, info
  • Selecting and filtering: loc, iloc, conditions
  • Handling missing data: isnull, dropna, fillna
  • Removing duplicates, replacing values
  • Mapping, applying functions, type conversion
  • GroupBy, aggregation, pivot tables
  • Merging, joining, concatenating DataFrames

SQL For Data Handling

  • Introduction to Databases & SQL: What is a database? Importance of SQL in AI/ML/Data Science. Connecting Python with databases (SQLite/MySQL overview)
  • Basic SQL Operations: SELECT, WHERE, ORDER BY, LIMIT; Filtering and sorting data
  • Intermediate SQL: JOINs (INNER, LEFT, RIGHT, FULL), GROUP BY, HAVING, aggregate functions (SUM, COUNT, AVG)
  • SQL with Python: Using sqlite3 / SQLAlchemy in Python, querying SQL results into Pandas DataFrames, ETL workflow, hands-on queries

Data Visualization

  • Importance of visualization in AI/ML
  • Matplotlib: line, scatter, bar, pie, histogram, box plot
  • Plot customization: labels, titles, legends, colors
  • Subplots and multiple plots
  • Seaborn: bar, scatter, box, violin, KDE
  • Heatmaps, pairplot, jointplot, FacetGrid
  • Styling & themes in Seaborn

Statistics & Probability

  • Data types: Categorical, numerical, continuous
  • Central tendency: Mean, median, mode
  • Dispersion: Variance, std, range, IQR
  • Visualization: Histogram, box, scatter, distplot
  • Probability basics: Experiment, event, sample space
  • Rules: addition, multiplication, conditional probability
  • Distributions: normal, binomial, Poisson
  • Random variables, expectation, variance

Statistical Inference

  • Sampling: Techniques, bias, distribution
  • Point and interval estimation
  • Central Limit Theorem (CLT)
  • Hypothesis testing: null/alt hypothesis, Type I/II error
  • t-tests: one-sample, two-sample
  • Chi-square test for independence
  • ANOVA: single-factor
  • Regression analysis: linear & multiple
  • Bayesian stats: Bayes theorem, posterior update

ML Workflow & Data Preprocessing

  • AI vs ML vs DL: Overview
  • Machine Learning workflow: pipeline, lifecycle
  • Types of ML: supervised, unsupervised, reinforcement
  • Data preprocessing: cleaning, handling missing data
  • Encoding: label, one-hot
  • Feature scaling: normalization, standardization
  • Train/test split, model evaluation metrics

Supervised Learning Algorithms

  • Linear regression, logistic regression
  • Decision Tree and Random Forest
  • SVM: kernel trick, hyperplane
  • K-Nearest Neighbors
  • Naive Bayes: GaussianNB
  • Evaluation: Accuracy, precision, recall, F1-score

Unsupervised Learning & Model Tuning

  • Clustering: K-means, elbow method, silhouette score
  • Hierarchical clustering: dendrograms
  • PCA: dimensionality reduction
  • Cross-validation: K-Fold
  • Overfitting vs underfitting
  • GridSearchCV, RandomizedSearchCV
  • Ensemble learning: Bagging, Boosting, Stacking

Intro To Deep Learning

  • Neural networks basics: perceptron, activation functions
  • Forward & backpropagation
  • TensorFlow & Keras: environment setup, basics
  • Model building: input, hidden, output layers
  • CNN: architecture, Conv layers, pooling
  • Transfer Learning: pre-trained models (e.g., MobileNet)

Natural Language Processing

  • NLP overview & applications
  • Text preprocessing: tokenization, stopwords, stemming, lemmatization
  • Text representation: Bag of Words, TF-IDF
  • Text classification: sentiment analysis / spam detection
  • Vectorization, model training using Sklearn

Computer Vision

  • Introduction to Computer Vision & OpenCV Basics
  • Image Color Spaces & Basic Transformations (Rotate, Flip, Resize, Blur)
  • Drawing, Annotation & Thresholding
  • Edge Detection, Contours & Object Detection Basics
  • Face Detection (Haar Cascades)
  • Intro to Deep Learning in CV + CNN Image Classification
  • Transfer Learning (VGG, ResNet, MobileNet)

Hands-On Real-Time Exposure Tasks

  • Home Value Prediction using Machine Learning
  • Flipkart/Amazon Reviews Sentiment Analysis
  • Cancer Cell Classification using Scikit-Learn & Deep Learning
  • Handwritten Digit Recognition with Deep Learning (MNIST)
  • General Disease Prediction using Machine Learning
  • Customer Sentiment & Satisfaction Analysis (Nykaa/Lakme Case Study)
  • Traffic Sign Detection with Deep Learning (YOLO/ResNet)
  • Music Genre Classification with Deep Learning
  • Melanoma Skin Cancer Detection using CNNs
  • AI-Powered Chatbot using NLP
Download Full Curriculum (PDF)

Projects You Will Work On

Home Value Prediction

Predict house prices using ML regression models based on location, area, and amenities.

Reviews Sentiment Analysis

Perform NLP sentiment analysis on Flipkart/Amazon reviews for product insights.

AI-Powered Chatbot

Develop transformer-based chatbot (BERT/GPT-like) for customer service and FAQs.

Student Testimonials

"This AI course gave me practical exposure and helped me land my first data science job."

- Rahul Verma

"Hands-on projects and real-world AI applications boosted my confidence."

- Sneha Kapoor

"SkillDzire’s AI program helped me build a strong portfolio for interviews."

- Aman Singh