AI and ML using Python
- Extensive Libraries and Frameworks
- Versatility and Community Support
- Ease of Learning and Prototyping
Key Highlights
Holistic Learning Approach
Expert Guidance
Flexible Online Learning
Collaborative Learning Environment
Career Readiness
Course Completion Certificate
About the Program
In our 6-month AI and Machine Learning course with Python, you’ll gain a comprehensive education covering foundational to advanced skills. Begin with Python for data manipulation, visualization, and statistical analysis using NumPy, Pandas, and Matplotlib. Progress to machine learning with supervised/unsupervised learning, regression, classification, and clustering. Hands-on projects simulate real-world scenarios: cleaning data, feature engineering, and building models for actionable insights. Explore deep learning with TensorFlow/PyTorch, and generative AI like GANs/VAEs for synthetic data and content generation. Expert mentors offer personalized feedback, fostering collaborative learning and problem-solving. Build a portfolio showcasing Python proficiency in data science for competitive job markets or career advancement in analytics.
What Will You Learn?
Python Fundamentals
Variables, loops, functions, and essential libraries for data manipulation and machine learning.
Data Handling
Using libraries like NumPy and Pandas to manipulate, clean, and preprocess data for machine learning tasks.
ML Algorithms
Implementing supervised (regression, classification) and unsupervised (clustering) algorithms with Scikit-Learn.
Deep Learning
Neural networks, TensorFlow/PyTorch, image recognition, and NLP tasks.
Model Evaluation
Performance evaluation, cross-validation, hyperparameter tuning, and overfitting mitigation.
Real-World Applications
AI/ML in image classification, sentiment analysis, and recommendation systems.
AI and ML Course Curriculum
- Lesson 1: Introduction to Machine Learning and Python Basics
- Lesson 2: Definition of Machine Learning (ML)
- Lesson 3: Types of Machine Learning
- Lesson 4: Machine Learning Terminology Part I
- Lesson 5: Machine Learning Terminology Part II
- Lesson 6: Python Fundamental: Part I
- Lesson 7: Python Fundamental : Part II
- Lesson 8: Python Fundamental : Part III
- Lesson 9: Data Manipulation with NumPy
- Assignment 1: Python Basics 1
- Assignment 2: Python Basics 2
- Assignment 3: Python Basics 3
- Assignment 4: Python Advance with Usecase
- Assignment 5: NumPy CaseStudy & Solutions
- Assignment 6: Pandas Case Studies
- Quiz 1: MCQs on Machine Learning
- Lesson 1: Exploratory Data Analysis (EDA) and Data Visualization
- Lesson 2: Data Manipulation and Analysis with Pandas
- Lesson 3: Descriptive Statistics using Pandas, Numpy, Scipy
- Lesson 4: Inferential Statistics using Pandas , Numpy, Scipy
- Lesson 5: Linear Algebra using NumPy
- Lesson 6: ML Algorithm: Decision Tree Classifier
- Lesson 7: Data Visualization using Matplotlib, Seaborn, Plotly
- Lesson 8: ML Algorithms: Naive Bayes algorithm
- Lesson 9: Supervised Learning – Regression
- Lesson 10: ML Algorithms : Linear Regression Part 1(Basic Funda)
- Lesson 11: ML Algorithms : Linear Regression Part 2(Cost Function Curation)
- Lesson 12: ML Algorithms : Linear Regression Part 3(Training Gradient Descent)
- Lesson 13: ML Algorithms : Linear Regression Part 4(Univariate, Multiple, Multivariate, Polynomial)
- Lesson 14: Evaluation Metrics : Regression
- Lesson 1: Supervised Learning – Classification
- Lesson 2: ML Algorithm: Logistic Regression Part 1(Intuition)
- Lesson 3: ML Algorithm: Logistic Regression Part 2 (Sigmoid & Decision boundary)
- Lesson 4: ML Algorithm: Logistic Regression Part 3 (Cost Function Curation)
- Lesson 5: ML Algorithm: Decision Tree Classifier
Lesson 6: ML Algorithm : KNN Algorithm - Lesson 7: ML Algorithm : Naive Bayes Classifier
Lesson 8: ML Algorithm: Support Vector Machines I - Lesson 9: ML Algorithm: Support Vector Machines II
- Lesson 10: Underfitting, Just Right, Overfitting
- Lesson 11: Regularization : Overfitting Solution with Gradient Descent
- Lesson 12: ML Algorithm: Unsupervised Learning(Clustering)
- Lesson 13: ML Algorithm: Dimensionality Reduction (Data Compression, PCA)
- .Lesson 14: ML Algorithm: Random Forest
- Lesson 15: ML Algorithm: k-means for Anomaly Detection
- Lesson 1: Introduction to Neural Networks I
- Lesson 2: Introduction to Neural Networks II(with XNOR Example)
- Lesson 3: Introduction to Neural Networks III(Cost Function, MNIST)
- Lesson 4: Introduction to Neural Networks IV(Simulation, Keras Intro)
- Lesson 1: Computer Vision: Introduction
- Lesson 2: Computer Vision : Image Recognition, Convolution , CNNs
- Lesson 3: Computer Vision : Object Detection, HOG, RCNN
- Lesson 4: Week 17-18: Natural Language Processing (NLP)
- Lesson 5: Week 19-20: Reinforcement Learnin
- Lesson 1: Week 21-22: Model Deployment and Productionization on Cloud (Azure, AWS, GCP)
- Lesson 2: Week 23-24: Big Data and Distributed Computing
- Lesson 3: Week 25-26: Capstone Project and Final Assessment
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Our Empowered Alumni
Who Can Apply for the Course?
- Recent graduates aiming to enter the AI and ML field
- Professionals from diverse sectors transitioning into AI and ML roles.
- Data enthusiasts keen on data analysis, ML, and visualization.
- Current students looking to complement academic studies with practical AI and ML skills.
- Managers and executives seeking data science knowledge for informed decision-making in their organizations.
- Managers and executives seeking AI and ML insights for strategic decision-making.
Roles That AI and ML Engineers Can Fulfill
ML Engineer
Designing and implementing machine learning models and systems.
AI Research Scientist
Conducting research to advance AI algorithms and techniques.
AI Software Engineer
Developing software solutions integrating AI capabilities.
AI Consultant
Advising businesses on AI strategy and implementation.
AI Project Manager
Conducts advanced data research and experiments to develop new methodologies.
AI Ethics Specialist
Ensuring ethical considerations in AI design and deployment.
Skill Covered
Python Programming
Unsupervised Learning
NLP
Neural Networks
ML Algorithms
Reinforcement Learning
Data Preprocessing
Time Series Analysis
Tools Covered
Frequently Asked Questions
Most courses offer flexible formats, including online, on-campus, and hybrid options, allowing you to choose the one that best suits your needs.
While some courses require basic knowledge of programming and statistics, others are designed for beginners. Check the prerequisites for each course before applying.
Yes, you will receive a certificate of completion or a degree, depending on the course you choose. These credentials can enhance your resume and career prospects.
Data science skills are in high demand across various industries. Completing these courses can open up new career opportunities, lead to higher salaries, and provide job security.