Best AI & Machine Learning
Course in Indore

• NumPy arrays: creation, indexing, slicing, broadcasting
• Array math, aggregations & random number generation
• Pandas Series and DataFrame fundamentals
• Reading/writing CSV, Excel, and SQL data
• Data cleaning: handling nulls, duplicates, outliers
• Filtering, sorting, grouping & aggregation
• Merge, join & concatenation of datasets
• Exploratory Data Analysis (EDA) techniques

• Line, bar, scatter, histogram and pie charts in Matplotlib
• Seaborn: heatmaps, pairplots, violin & box plots
• Customising visuals: themes, annotations, subplots
• Power BI interface and connecting data sources
• Power Query for data transformation
• DAX formulas for calculated columns and measures
• Building interactive business dashboards
• Publishing and sharing reports on Power BI Service

• Supervised, unsupervised & reinforcement learning overview
• Feature engineering, encoding & feature scaling
• Train-test split, cross-validation & ML pipelines
• Linear Regression & Logistic Regression
• Decision Trees & Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbours (KNN)
• Naive Bayes classifier
• Clustering: K-Means, DBSCAN, Hierarchical
• Model evaluation: accuracy, precision, recall, F1, ROC-AUC
• Hyperparameter tuning with GridSearchCV
• Saving and loading models with Pickle

• Introduction to Neural Networks and the Perceptron
• Activation functions, forward & backpropagation
• Building ANNs with Keras Sequential API
• Optimisers: Adam, SGD, RMSprop; loss functions
• Regularisation: Dropout, Batch Normalisation
• Convolutional Neural Networks (CNNs) for image tasks
• Recurrent Neural Networks (RNNs) & LSTM for sequences
• Transfer Learning with VGG16, ResNet, MobileNet
• Model saving, loading & deployment basics with Flask/Streamlit

• Text preprocessing: tokenisation, stemming, lemmatisation
• Bag of Words & TF-IDF vectorisation
• Word embeddings: Word2Vec, GloVe
• Sentiment analysis and opinion mining
• Named Entity Recognition (NER)
• Text classification pipelines
• Introduction to Transformer models & BERT basics
• Building a simple chatbot with NLP

• Image fundamentals: reading, resizing, colour spaces
• Image transformations and geometric operations
• Edge detection: Canny, Sobel operators
• Feature detection: SIFT, ORB
• Face detection with Haar cascades
• Object detection using YOLO
• Real-time video processing with OpenCV

• Relational database concepts & SQL fundamentals
• CREATE, INSERT, UPDATE, DELETE operations
• Filtering with WHERE, LIKE, IN, BETWEEN
• JOINs, GROUP BY and aggregate functions
• Subqueries and views
• Python–MySQL integration with PyMySQL / SQLAlchemy
• Loading ML datasets directly from SQL databases

• Problem statement selection & real-world data sourcing
• End-to-end EDA and feature engineering
• Model building, tuning and evaluation
• Building a web interface using Streamlit or Flask
• Deploying model to Render / Heroku cloud platform
• Final presentation with business insights & recommendations

Capstone Ideas: House Price Predictor · Fake News Detector · Crop Disease Classifier · Customer Lifetime Value Model