12+ Lesson
9hr 30min
32 students enrolled
AI & MI COURSE CONTENT
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Overview of AI — What & Why
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History and Evolution of AI
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AI vs Machine Learning vs Deep
Learning
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Applications of AI in real
life
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AI Tools & Environments
(Python, Jupyter, Anaconda)
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Python Basics (Data Types,
Loops, Functions, Modules)
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Numpy for numerical
computation
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Pandas for data handling and
preprocessing
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Matplotlib & Seaborn for data
visualization
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Scikit-learn basics for ML
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Linear Algebra (Vectors,
Matrices, Operations)
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Probability & Statistics
(Distributions, Mean, Variance)
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Calculus (Derivatives,
Gradient Concept)
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Optimization (Gradient
Descent, Cost Functions)
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What is Machine Learning
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Types of ML
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Data Preprocessing
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Linear Regression
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Logistic Regression
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Decision Trees
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Random Forests
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K-Nearest Neighbors (KNN)
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Support Vector Machines (SVM)
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Naïve Bayes Classifier
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Model Evaluation (Confusion
Matrix, Precision, Recall, F1 Score)
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Clustering
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Dimensionality Reduction
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Association Rule Learning
(Apriori, Eclat)
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Introduction to Neural
Networks
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Perceptron & Multi-Layer
Perceptron (MLP)
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Activation Functions
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Forward & Backpropagation
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TensorFlow & Keras Frameworks
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Convolutional Neural Networks
(CNNs) for Image Processing
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Recurrent Neural Networks
(RNNs) & LSTMs for Sequential Data
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Text Preprocessing
(Tokenization, Lemmatization, Stop Words)
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Bag of Words & TF-IDF
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Sentiment Analysis
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Named Entity Recognition (NER)
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Chatbots and Text
Classification
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Introduction to Transformers &
BERT (Optional Advanced)
-
Basics of Reinforcement
Learning
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Agents, Environment, Rewards
-
Q-Learning
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Deep Q Networks (DQN)
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Applications in Games &
Robotics
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Model Saving and Loading
(Pickle, Joblib)
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Flask / FastAPI for model
deployment
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Streamlit for building AI apps
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Working with Cloud Platforms
(Google Colab, AWS, Azure, etc.)
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Mini Projects
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Major Project
-
Students and professionals
interested in Artificial Intelligence and
Data Science
-
Beginners with a passion for
automation, deep learning, and predictive
modeling
-
Software developers looking to
transition into AI-driven careers
-
Anyone interested in building
smart, data-driven applications
-
FREE Demo Session to explore
AI & ML concepts and career scope
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Comprehensive training
covering Python, Data Science, Machine
Learning, and Deep Learning
-
Hands-on experience with
real-world AI and ML projects using datasets
-
Practical implementation using
libraries like TensorFlow, Keras, and
Scikit-learn
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Career-oriented guidance with
resume building and interview training
-
Small batch sizes ensure
personalized mentoring and focused learning.
-
Expert trainers with hands-on
experience in AI and Machine Learning
projects
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Interactive sessions combining
theory, coding, and real data analysis
-
Updated curriculum aligned
with latest AI trends and tools
-
Comprehensive career support
with mock interviews and portfolio building
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Receive an industry-recognized
AI & ML Professional Certificate upon
completion.
-
Understand the core principles
of Artificial Intelligence and Machine
Learning
-
Apply supervised,
unsupervised, and deep learning techniques
to real-world problems
-
Use Python-based tools like
NumPy, Pandas, and TensorFlow for AI
applications
-
Build and deploy predictive
models and intelligent systems
Enrolled: 32 students
Duration: 20 hours
Chapters: 15
Video: 12 hours
Level: Beginner