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Attend all scheduled classes, whether online or in-person, to gain in-depth knowledge and hands-on experience. Engage actively in lectures, discussions, and practical exercises to maximize your learning outcomes.
Work on hands-on assignments and projects to apply the concepts learned in real-world scenarios. Successfully complete the capstone project, demonstrating your ability to implement AI solutions.
Take the final assessment or evaluation to showcase your skills and understanding of AI concepts. Upon successfully completing the training and evaluation, receive an AI Certification to validate your expertise and boost your career.
Learning Path
Day 1: Introduction to AI and Python
Hour 1: Python Basics for AI
○ Data types, variables, and basic operators
○ Control structures: if-else, loops (for, while)
○ Functions and modules
Hour 1.5: Python Libraries for AI
○ Introduction to essential libraries: NumPy, pandas, scikit-learn
○ Data manipulation with pandas
○ Numpy array operations for AI applications
Day 2: Advanced Python Concepts for AI
Hour 1: Object-Oriented Programming (OOP) in Python
○ Classes and objects
○ Inheritance, polymorphism, and encapsulation
Hour 1.5: Working with Python Libraries
○ Advanced usage of pandas: DataFrames and Series operations
○ Introduction to scikit-learn for machine learning models
○ Data preprocessing: handling missing values, scaling, encoding
Day 3: Data Handling and Preprocessing
Hour 1: Data Exploration with pandas
○ Analyzing data using pandas
○ Data cleaning: removing duplicates, dealing with missing values
○ Visualizing data using Matplotlib and Seaborn
Hour 1.5: Data Transformation
○ Normalization, scaling, and encoding categorical variables
○ Feature engineering: Creating new features
○ Preparing data for machine learning models
Day 4: Introduction to Machine Learning
Hour 1: Understanding Machine Learning
○ What is Machine Learning?
○ Types of machine learning: Supervised and unsupervised learning
○ Key algorithms and their use cases
Hour 1.5: Hands-On: Building Your First ML Model
○ Linear Regression using scikit-learn
○ Model evaluation metrics (MSE, RMSE)
○ Hands-on project: Building a simple linear regression model
Day 5: Supervised Learning Techniques
Hour 1: Classification Mode
○ Logistic Regression, Decision Trees, Random Forest
○ Evaluating classification models: Accuracy, Precision, Recall, F1 Score
Hour 1.5: Hands-On: Building a Classification
○ Implementing Logistic Regression and Decision Trees
○ Hyperparameter tuning and model evaluation
○ Visualizing decision boundaries and classification results
Day 6: Advanced Supervised Learning
Hour 1: Ensemble Methods
○ Random Forest, Gradient Boosting, XGBoost
○ Advantages of ensemble methods for improving model performance
Hour 1.5: Hands-On: Random Forest and XGBoost
○ Building and tuning a Random Forest model using scikit-learn
○ Implementing XGBoost for classification task
Day 7: Unsupervised Learning
Hour 1: Clustering Algorithms
○ K-Means Clustering, Hierarchical Clustering
○ Dimensionality reduction with PCA
Hour 1.5: Hands-On: Clustering and PCA
○ Implementing K-Means clustering using scikit-learn
○ Applying PCA for dimensionality reduction and visualizing clusters
Day 8: Deep Learning Fundamentals
Hour 1: Introduction to Deep Learning
○ Neural Networks: Architecture, weights, activation functions
○ Backpropagation and Gradient Descent
Hour 1.5: Working with TensorFlow and Keras
○ Introduction to TensorFlow for deep learning
○ Building a simple neural network using Keras
○ Model evaluation metrics in deep learning
Day 9: Convolutional Neural Networks (CNNs)
Hour 1: CNN Architecture
○ Convolutional layers, pooling layers, fully connected layers
○ CNNs for image classification and recognition
Hour 1.5: Hands-On: Building a CNN Model
○ Implementing a CNN with Keras for image classification
○ Evaluating and tuning the CNN model
Day 10: Capstone Project and Assessment
Hour 1: Capstone Project Implementation
○ Participants work on a project using Python, machine learning, or deep
learning techniques
○ Example: Image classification, text classification, or prediction tasks.
Hour 1.5: Project Presentation and Feedback
○ Teams or individuals present their final project
○ Instructor feedback and group discussion
Completion Certification
○ Participants who complete the course and capstone project will receive a
certificate of completion, which they can add to their resumes or Linked
profile.
Shaping the Future: Guiding Minds and Innovating with Excellence in AI Education!
Our AI instructors bring 5–12 years of hands-on expertise in teaching and applying advanced machine learning, data analysis, and data visualization techniques. With a proven track record of building and deploying AI models across various industries, they are passionate about simplifying complex concepts for learners. Their in-depth knowledge, combined with a practical, project-driven approach, ensures every student gains the skills needed to excel in real-world AI applications. Whether you’re a beginner or looking to advance your career, their guidance will inspire and empower your AI journey.
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Testimonial
One of the best and First Data Science services includes Tutoring in python, Data Analysis, Model Building. I am very satisfied with their services. Highly recommended.
Geeta Bajaj
Neerja is one of the best instructors I ever had.
The ability to clearly explain the concepts. Being available to help her students whenever they ask for is something I respect a lot about her.
Navnoor
I strongly recommend Neerja as an exceptional teacher of data visualization. Her ability to break down complex concepts into easy-to-understand parts made the learning experience both enjoyable and practical. I enjoyed so much being a student in her class. Her teaching style was interactive and engaging and provided opportunities for students to practice their skills through hands-on exercises and real-world examples. The knowledge I got from her is making me excel in my projects, so if you want to get a widely experienced professional don’t hesitate to contact her.
Isreal Draiz
Neerja was able to explain complex ideas of Machine Learning in a way that is very easy to understand. She is also a very good communicator and knows how to get the message across in a very clear and concise manner. I would highly recommend Neerja for any Machine Learning related material.
Imran
I wanted to take a moment to express my sincere appreciation for your outstanding work as a Data Science Instructor. Your expertise, teaching abilities, and positive attitude have made a significant impact on the team. Neerja actively embraces new responsibilities and consistently pursues opportunities to learn and develop as both a data scientist and instructor. I wholeheartedly recommend her to anyone seeking a skilled professional who is passionate about teaching and fostering growth in the field of data science. Thank you for being an invaluable asset!
Purvi
I highly recommend Neerja as a data science teacher. Her expertise in the field is evident in the way she communicates complex concepts in a clear and concise manner, making it easy for students to grasp difficult concepts. Neerja is also highly dedicated to her students and goes above and beyond to ensure they have the resources they need to succeed, whether that's providing extra help outside of class or creating engaging and relevant assignments. Overall, Neerja is an outstanding teacher who is passionate about helping her students develop the skills they need to succeed in the exciting and rapidly-evolving field of data science.
Daniel
I enthusiastically recommend Neerja for an instructional position in Data Science. Neerja has been an asset to our instructional team at Coding Dojo. She is knowledgeable, impactful, and thoughtful in her roles as instructor and teammate. She is committed to her students and provides both the technical and emotional support that leads them to success. Neerja eagerly welcomes new responsibilities and constantly seeks to learn and grow as a data scientist and instructor.
Brenda
I have had the pleasure of knowing Neerja as a colleague at Coding Dojo. Neerja is a wonderful instructor! When she initially started with Coding Dojo, one of her main focuses was to find ways to help students understand difficult concepts with ease. She tailored her lectures so that the information expressed while she was teaching would engage students and also be comprehendible by all of her students.
Also, Neerja is an amazing asset to our team. Both students and staff alike adore her. As an excellent team member, she has made contributions to the curriculum and content for the data science program. She is extremely knowledgeable in python, modeling, machine learning, time series analysis, and Tableau. Whether you are looking for an exceptional instructor or a well-rounded data scientist, Neerja will exceed your expectations!
Sherlin
I whole-heartedly recommend Neerja Jhingan for any position working on analyzing or modeling data, or teaching others to do so. I've worked with Neerja for the last year and I've found her to be pleasant, competent, caring, and coachable. She's a joy to work with and she completes assignments reliably and with quality. She also connects to her students and colleagues easily and is a benefit to the morale of our team.
Neerja's students often comment on her caring nature, the clarity with which she explains complex topics, her depth of knowledge on the topics, and her tangible devotion to their success. She is a very popular instructor with excellent KPIs.
Josh
Not only, Neerja is an enthusiastic, dedicated and highly skilled data scientist, but also a good mentor. She is actively and eagerly sharing her knowledge with the data science lovers through the YouTube channel ‘Brainy Data Science’. Her vast knowledge in the data analytics, feature engineering, machine learning algorithms helped many to implement the practical concepts over the Kaggle. Neerja’s ability to handle multiple tasks made a dramatic difference in the productivity. She would be an asset to any team and earns my highest recommendation.
Harpreet
Neerja is one of the dedicated and motivated data scientist I have ever known. Neerja is eagerly learning all the skills in the field of data science at a very quick pace. I must say about her analytics skills are out of the box. She has also proven that her explanation skills in data science field is at best. One must watch her youtube series where she is proving her skills very well.
Deepanshu
I met Neerja through an online study platform, she has always been very committed and seeks to acquire more and more knowledge and experience in the field of Data Science. I would like to recommend her hard skills, like python, and a huge data science toolkit, but also recommend her soft skills like curiosity, communication, negotiation and self-learning.
Lucas