Artificial Intelligence and Machine Learning Training and Internship.
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Unlock the future with our comprehensive
Artificial Intelligence (AI) and Machine Learning (ML) course designed for
beginners, students, and aspiring data professionals. This course combines
strong Python programming skills with real-world applications of AI and ML to
prepare you for cutting-edge careers in tech. You’ll start with the fundamentals
of Python and data handling, then dive deep into machine learning algorithms,
model building, and practical AI applications using libraries like
Scikit-Learn, Pandas, NumPy, and TensorFlow/Keras. By the end of this course,
you'll be equipped to build intelligent systems, analyze large datasets, and
deploy your own ML models for real-world use cases.
Demand
& Growth·
Python is the most used language in Artificial
Intelligence (AI) and Machine Learning (ML).
·
The
AI industry in India is projected to reach $7.8 billion by 2025, creating over
1 million jobs.
·
Global
tech giants like Google, Microsoft, Amazon, and Indian companies like TCS,
Infosys, and Wipro are actively hiring AI/ML professionals.
Required
skills
1.Strong Python programming
Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras,
PyTorch
Concepts: ML algorithms, deep learning, neural networks,
NLP
2.Data handling & visualization
3.Model training & deployment
4.Knolwedge of tools like Jupyter,Git,AWS/GCP
·
Python is the most used language in Artificial
Intelligence (AI) and Machine Learning (ML).
·
The
AI industry in India is projected to reach $7.8 billion by 2025, creating over
1 million jobs.
·
Global
tech giants like Google, Microsoft, Amazon, and Indian companies like TCS,
Infosys, and Wipro are actively hiring AI/ML professionals.
1.Strong Python programming
Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras,
PyTorch
Concepts: ML algorithms, deep learning, neural networks,
NLP
2.Data handling & visualization
3.Model training & deployment
4.Knolwedge of tools like Jupyter,Git,AWS/GCP
Python installation and setup (Jupyter, VS Code, Anaconda)
Variables, Data Types, Operators
Conditional Statements and Loops
Functions and Lambda
Lists, Tuples, Sets, Dictionaries
String manipulation
File handling
Error handling (try/except)
Modules and Packages
Object-Oriented Programming (OOP)
NumPy: Arrays, operations, broadcasting, slicing
Pandas: Series, DataFrames, reading/writing files, filtering, groupby
Matplotlib: Basic plotting (line, bar, histogram)
Seaborn: Advanced visualizations (heatmap, pairplot, distplot)
Data Cleaning & Preprocessing:
Handling missing data
Encoding categorical variables
Feature scaling (Standardization/Normalization)
Supervised Learning
Linear Regression
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Support Vector Machines (SVM)
Introduction to Neural Networks
Perceptron and Multilayer Perceptron (MLP)
Activation functions (ReLU, Sigmoid, Softmax)
Loss functions and optimizers
Building neural networks using TensorFlow and Keras
Training vs overfitting vs underfitting
Convolutional Neural Networks (CNNs) – Basic image classification
Text preprocessing: tokenization, stop words, stemming, lemmatization
Bag of Words and TF-IDF
Sentiment analysis
Intro to LSTM or Transformers (for advanced learners)
House Price Prediction using Linear Regression
Customer Segmentation using K-Means
Email Spam Classifier
Image Classification with CNN (if deep learning included)
Sentiment Analysis on Movie Reviews
Git & GitHub
Jupyter Notebook & Google Colab
Streamlit or Flask for ML model deployment
Hosting models on web (Heroku, Render, or Hugging Face)