Machine Learning & AI with TensorFlow and Python
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Timing : 9.00 AM - 06.00 PMAbout This Course
ML & AI course offers a comprehensive journey into Machine Learning (ML) and Artificial Intelligence (AI), equipping learners with the knowledge, tools, and hands-on skills to design, build, and deploy intelligent systems. Beginning with the fundamentals of AI, ML, mathematics, and data preprocessing, learners progress through key concepts such as supervised and unsupervised learning, deep learning, neural networks, and reinforcement learning. The course also introduces specialized domains of AI, including Natural Language Processing (NLP), Computer Vision, Time Series Analysis, and Generative AI.
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Course Duration
6-8 Months
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Certificate
Upon Completion
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Students Enrolled
1000+
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Learning Mode
Online & Offline
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Branches
Maninagar | Gandhinagar | Kudasan | Bopal | Nikol | Mehsana | Gurukul
Course Curriculum
- Python basics - Variables, data types, operators
- Control flow - if-else, loops, functions
- Data structures - lists, tuples, dictionaries, sets
- File handling and exception handling
- Object-oriented programming (OOP) concepts
- Working with libraries and modules
- Hands-on projects and exercises
- Introduction to NumPy arrays and ndarrays
- Array indexing, slicing, and reshaping
- Mathematical operations on arrays
- Broadcasting and vectorization
- Linear algebra operations with NumPy
- Statistical functions - mean, median, std, variance
- Working with random numbers and simulations
- Introduction to Series and DataFrame
- Reading and writing data (CSV, Excel, JSON, SQL)
- Data cleaning - handling missing values, duplicates
- Data filtering, sorting, and grouping
- Merging, joining, and concatenating DataFrames
- Pivot tables and cross-tabulations
- Time series analysis with Pandas
- Introduction to Matplotlib - line plots, bar charts
- Scatter plots, histograms, and pie charts
- Customizing plots - colors, labels, legends, titles
- Subplots and figure layout management
- Introduction to Seaborn - statistical visualizations
- Heatmaps, pairplots, and distribution plots
- Creating publication-ready visualizations
- Introduction to Scikit-Learn and its ecosystem
- Data preprocessing - scaling, normalization, encoding
- Train-test split and cross-validation
- Supervised learning algorithms - Linear Regression, Logistic Regression
- Decision Trees, Random Forest, and SVM
- Unsupervised learning - K-Means, PCA
- Model evaluation metrics and hyperparameter tuning
- Supervised Learning - Regression and Classification
- Linear Regression, Ridge, Lasso, ElasticNet
- Logistic Regression and Naive Bayes
- Decision Trees, Random Forest, Gradient Boosting
- Support Vector Machines (SVM)
- Unsupervised Learning - K-Means, Hierarchical Clustering
- Dimensionality Reduction - PCA, t-SNE
- Introduction to TensorFlow and Keras
- Building and training neural networks
- Activation functions and optimization algorithms
- Convolutional Neural Networks (CNN) for Computer Vision
- Recurrent Neural Networks (RNN) and LSTMs
- Transfer learning and pre-trained models
- Model deployment using TensorFlow Serving
- Neural networks architecture and backpropagation
- Deep Neural Networks (DNN)
- Convolutional Neural Networks (CNN) - Image classification
- Recurrent Neural Networks (RNN) - Sequence data
- LSTM and GRU for time series
- Autoencoders and Generative models
- Hyperparameter tuning for deep learning
- Text preprocessing - tokenization, stemming, lemmatization
- Bag of Words, TF-IDF, and Word Embeddings
- Sentiment analysis and text classification
- Named Entity Recognition (NER)
- Transformers and BERT models
- Text summarization and machine translation
- Chatbots and conversational AI
- Introduction to AI and intelligent agents
- Search algorithms - BFS, DFS, A*
- Game playing and minimax algorithm
- Knowledge representation and reasoning
- Expert systems and fuzzy logic
- Reinforcement Learning - Q-Learning, Deep Q Networks
- Generative AI and LLMs (GPT, Gemini)
- Real-world ML/AI project implementation
- End-to-end ML pipeline - data collection to deployment
- Building and training custom models
- Model evaluation and optimization
- Deploying ML models using Flask/Streamlit
- Portfolio building for job applications
- Final project presentation and feedback
