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Delve into the principles of Python with AI with expert-led training at Croma Campus.

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  • A Python AI course is important if you want to start a career in artificial intelligence or improve your programming skills. Python is a popular language for AI because its easy to learn and has many tools that help with tasks like data analysis, machine learning, and deep learning. The course will teach you how to create AI models, automate tasks, and solve problems. Since AI is a fast-growing field, learning Python can boost your job opportunities in areas like tech, finance, and healthcare, making you more valuable to employers.
  • What You Will Learn:
    • Python Basics

      AI Tools

      Data Cleaning

      Machine Learning

      Deep Learning

      Text Analysis

      Model Testing

  • Personal Skills Development:
    • Critical Thinking

      Creative Problem-solving

      Time Management

      Effective Communication

      Adaptability Skills

      Team Collaboration

      Self-Discipline

      Emotional Awareness

  • Prerequisites:
    • Before starting a Python for AI Course, it helps to have a few basics in place. Knowing some Python programming is important, as its the main language used. A basic understanding of math, like algebra and statistics, is also useful because AI involves a lot of number crunching. You dont need to be an expert, but being comfortable with these concepts will make learning easier. Lastly, having a curious attitude and a willingness to learn new things will help you get the most out of the course.

  • Who Should Attend:
    • Tech Enthusiasts

      Data Analysts

      Aspiring Coders

      Machine Learners

      Career Changers

      Software Developers

      Students Interested

Python with AI Course

About-Us-Course

  • The goal of a Python with AI course is to teach you how to use Python to build and work with artificial intelligence. Youll learn the basics of AI and how to use Python tools to create smart programs that can learn and make decisions. The course covers important topics like machine learning and data analysis.

  • For newcomers starting in AI and Python jobs in India, salaries can vary depending on the company and location. Typically, entry-level positions offer salaries ranging from 5,00,000 to 8,00,000 per year. In some high-demand areas or top companies, this can be higher. As the AI field continues to grow, salaries for freshers are expected to rise, reflecting the increasing demand for these skills.

  • After completing a Python with AI course, you can explore several exciting career paths. You might become a Data Scientist, where you analyze data to help companies make smart choices. As a Machine Learning Engineer, youd create and improve smart algorithms that power various technologies. If you enjoy exploring new ideas, you could work as an AI Researcher, finding new ways to advance AI. You could also become a Software Developer, building apps with AI features, or a Data Analyst, turning data into useful insights. Additionally, roles like Business Intelligence Developer involve making tools to help businesses understand their data better.

  • A Python Artificial Intelligence course is really important today because technology is changing fast and more businesses are using AI to make better decisions and improve their work. Python is a popular language for AI because it's easy to learn and very powerful. This course teaches you how to use Python to build smart systems and work with data, which are skills in high demand.

  • Key Roles and Responsibilities:
    • Build AI Models: Create and train smart systems to solve problems.

      Handle Data: Clean and prepare data for analysis.

      Implement Algorithms: Write code for tasks like predicting or grouping data.

      Optimize Code: Make sure the code runs efficiently.

      Test Models: Check if models work well and are accurate.

      Integrate Systems: Add AI models into apps or software.

      Fix Issues: Troubleshoot and resolve code problems.

      Document Work: Keep records of code and processes.

  • Python with AI skills are highly sought after across various industries. Technology companies lead the way, using AI for software development, automation, and data analysis. Finance and banking also rely on AI to improve fraud detection, algorithmic trading, and customer service. In the healthcare sector, AI helps in diagnostics, personalized medicine, and predictive analytics. Retail and e-commerce use AI for customer recommendations, inventory management, and sales forecasting. Additionally, the automotive industry is increasingly adopting AI for self-driving technology and smart manufacturing.

  • Upon completing the Python with AI Course, you will earn a widely recognized certificate. Certification is a great way to demonstrate your expertise and gain an advantage over those who are not certified.

Why Should You Learn Python with AI Course?

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we train you to get hired.

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CURRICULUM & PROJECTS

Python with AI Training Program

    Introduction To Python

    • Installation and Working with Python
    • Understanding Python variables
    • Python basic Operators
    • Understanding the Python blocks.
    • Version Control with Git & GitHub

    Python Keyword and Identiers

    • Python Comments, Multiline Comments.
    • Python Indentation
    • Understating the concepts of Operators
      • Arithmetic
      • Relational
      • Logical
      • Assignment
      • Membership
      • Identity
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    Data Analysis and Visualization using Pandas.

    • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
    • How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
    • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
    • Exploring the Data Plotting, Correlations, and Histograms
    • Getting statistical information about the data Analysis Concepts, Handle the None Values
    • Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
    • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
    • Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
    • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
    • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
    • Exporting the results to Excel Joins
    • Python | Pandas Data Frame Inner Join
    • Under sting the properties of Data Frame Left Join (Left Outer Join)
    • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
    • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
    • Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
    • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
    • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

    Data Analysis and Visualization using NumPy

    • Introduction to NumPy Numerical Python
    • Importing NumPy and Its Properties
    • NumPy Arrays
    • Creating an Array from a CSV
    • Operations an Array from a CSV
    • Operations with NumPy Arrays
    • Two-Dimensional Array
    • Selecting Elements from 1-D Array
    • Selecting Elements from 2-D Array
    • Logical Operation with Arrays
    • Indexing NumPy elements using conditionals
    • NumPy’s Mean and Axis
    • NumPy’s Mode, Median and Sum Function
    • NumPy’s Sort Function and More

    Data Analysis and Visualization using MatPlotLib

    • Bar Chart using Python MatPlotLib
    • Column Chart using Python MatPlotLib
    • Pie Chart using Python MatPlotLib
    • Area Chart using Python MatPlotLib
    • Scatter Plot Chart using Python MatPlotLib
    • Play with Charts Properties Using MatPlotLib
    • Export the Chart as Image
    • Understanding plt. subplots () notation
    • Legend Alignment of Chart using MatPlotLib
    • Create Charts as Image
    • Other Useful Properties of Charts.
    • Complete Understanding of Histograms
    • Plotting Different Charts, Labels, and Labels Alignment etc.

    Introduction to Data Visualization with Seaborn

    • Introduction to Seaborn
    • Making a scatter plot with lists
    • Making a count plot with a list
    • Using Pandas with seaborn
    • Tidy vs Untidy data
    • Making a count plot with a Dataframe
    • Adding a third variable with hue
    • Hue and scattera plots
    • Hue and count plots
    • Visualizing Two Quantitative Variables
      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots
    • Visualizing a Categorical and a Quantitative Variable
      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whisk
      • Point plots
      • Customizing points plots
      • Point plot with subgroups
    • Customizing Seaborn Plots
      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels Part 1
      • Face Grids vs. Axes Subplots
      • Adding a title to a face Grid object
      • Adding title and labels Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
      • Well done! What’s next
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    Foundation for AI: Learn traditional ML models, evaluation, and workflows.

    • Introduction to ML, AI, and Deep Learning
    • Types of ML (Supervised, Unsupervised, Reinforcement)
    • ML Pipeline: Data Cleaning, Feature Engineering
    • Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
    • Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
    • Overfitting, Underfitting, Cross-Validation
    • Hands-on Project: Titanic Dataset (or similar)
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    Understand the inner workings of neural networks and train them with Keras.

    • Introduction to Neural Networks & Deep Learning
    • Activation Functions (ReLU, Sigmoid, Tanh)
    • Feedforward Neural Network
    • Backpropagation & Gradient Descent
    • Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
    • Softmax, Cross-Entropy Loss
    • Keras Basics: Sequential API & Functional API
    • Fully Connected Layer – Forward/Backward Pass
    • Regularization – Dropout, Batch Normalization
    • Data Preprocessing & Data Augmentation
    • Weight Initialization Strategies
    • Babysitting Learning: Overfit detection, TensorBoard Monitoring
    • Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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    Master CNNs, object detection, segmentation, and deployment.

    • Basics of Images, Image Preprocessing
    • Convolution: 2D Conv, Forward & Backward
    • Pooling, Padding, Stride, Transposed Conv
    • CNN Architectures: LeNet, AlexNet, VGG, ResNet
    • GPU vs CPU for DL
    • Transfer Learning: Inception, MobileNet, fine-tuning
    • Semantic Segmentation using UNet
    • Object Detection – YOLO, SSD, Region Proposal
    • Bounding Box Regressor
    • Siamese Networks for Similarity Search
    • Hands-on:
      • Image Classification with CNN
      • Object Detection with YOLOv8
      • Visual Search with Embeddings
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    Train text models from scratch and with BERT.

    • Introduction to NLP and Use Cases
    • Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
    • Feature Extraction: BOW, TF-IDF, N-Grams
    • Word Embeddings: Word2Vec, GloVe, Dense Vectors
    • POS Tagging, Named Entity Recognition
    • RNN, LSTM – Forward Pass and BPTT
    • Advanced LSTM Applications + Architectures
    • Attention Mechanism + Encoder-Decoder
    • Transformers, BERT, Hugging Face Pipelines
    • NLP Evaluation Metrics: BLEU, ROUGE
    • Hands-on:
      • Sentiment Classifier (LSTM or BERT)
      • Deploy NLP Model with Streamlit
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    Take models from notebooks to real-world applications.

    • Saving & Loading Models (Pickle, Joblib, Keras)
    • Flask vs FastAPI – Serving ML models
    • Streamlit/Gradio for Web Apps
    • Hosting Models on Hugging Face Spaces, Streamlit Cloud
    • MLflow Intro – Model Tracking & Versioning
    • Hands-on:
      • Deploy CV or NLP model with Streamlit
      • Create API using FastAPI
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    Build, evaluate, and deploy a mini AI project end-to-end.

    • Project Selection: Tabular, CV, or NLP
    • Data Collection/Exploration
    • Preprocessing + Feature Engineering
    • Model Training & Tuning
    • Evaluation & Interpretation
    • App Creation (Streamlit/Gradio)
    • Deployment + Final Presentation/Submission
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    Fake News Detection (NLP)

    Plant Disease Detection (CV)

    Job Match/Resume Screening (Tabular + NLP)

    Visual Product Search Engine (CV)

    Chatbot for Customer Support (NLP)

    Energy Consumption Forecasting (Time Series + Tabular Data)

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FAQ's

Python AI means using Python programming to create smart systems and applications.

It helps to know some programming, especially Python, but beginners can still join and learn.

You can build smart models, work with data, and find jobs in areas like data science and machine learning.

It usually takes a few weeks to several months, depending on the course and how much time you can commit.

Yes, there are many job opportunities in tech, finance, healthcare, and other fields that use AI.

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