Top Machine Learning Course in Pune and AI ML Course in Pune with Placement

• Explains what machine learning is and why it’s a big deal today with examples like fraud alerts and voice assistants.
• Breaks down the three main types: supervised (labeled data), unsupervised (no labels), and reinforcement (learning by trial).
• Walks through the ML process—collecting data, picking a model, training it, and using it—with a focus on how this leads to jobs.
• Highlights how Pune’s tech scene ties into ML careers to get learners motivated.

• Covers Python essentials like variables, loops, and if-statements to get coding started.
• Shows how to write small scripts and functions for practical tasks.
• Introduces NumPy for quick number crunching and Pandas for sorting data tables.
• Includes exercises like calculating averages or filtering lists to build ML-ready skills.

• Teaches vectors and matrices to handle data in rows and columns.
• Explains derivatives and slopes to tweak models step-by-step.
• Goes over stats basics—averages, spreads, and probabilities—to make sense of messy data. 
• Ties these to ML with examples like adjusting a price predictor.

• Shows how to fill in missing data spots with smart guesses or averages.
• Teaches ways to spot and handle weird outliers that mess up results.
• Covers scaling numbers so they’re fair for models, plus turning text into usable digits.
• Uses real datasets for practice, like fixing a sales report full of holes.

• Explains making visuals like bar charts and dot plots with Matplotlib to see patterns.
• Teaches calculating averages, spreads, and connections between numbers to back up what you see.
• Guides learners to ask questions like “What’s odd here?” or “What matters most?”
• Practice involves plotting customer data to spot buying trends.

• Introduces learning from data with answers, like predicting prices or flagging emails.
• Explains regression (numbers) versus classification (categories) with clear examples.
  Lays out the steps: gather data, train a model, test it, and tweak it.
• Uses examples like guessing house costs to show how it works.

• Shows how to draw a line through data points to predict numbers like sales.
• Explains minimizing errors with a method called least squares and checking if it fits.
• Teaches coding it in Python from scratch, then using shortcuts with scikit-learn.
• Practice predicts things like monthly revenue based on ad spend.

• Covers predicting yes/no outcomes, like if a customer will leave, using probabilities.
• Explains the sigmoid curve that turns numbers into 0-to-1 chances.
• Shows how to code it and check results with accuracy and error charts.
• Practice involves sorting emails into spam or not-spam piles.

• Teaches splitting data into branches based on yes/no questions, like a flowchart.
• Explains keeping trees short to avoid overcomplicating things.
• Shows coding trees in Python to predict stuff like loan approvals.
• Practice builds a tree to guess if someone will buy a product.

Combines lots of decision trees to make smarter guesses with less error.

Explains picking random bits of data and features to keep things balanced.

Teaches tuning settings like tree count with Python coding.

Practice uses it on health data to predict disease risks

• Shows finding the best line or curve to split data into groups, like cats versus dogs.
• Explains tricks like kernels to handle tricky, twisty data shapes.
• Covers coding SVMs in Python and tweaking settings for better fits.
• Practice sorts handwritten digits into numbers using image data.

• Explains guessing categories by looking at nearby data points, like friend groups.
• Teaches picking the right number of neighbors and measuring distances.
• Shows coding KNN in Python and testing it on simple tasks.
• Practice groups customers by shopping habits for marketing.

• Uses probability rules to classify fast, like spotting spam by word chances.
• Explains assuming features don’t overlap much to keep it simple.
• Teaches coding it in Python for text-based tasks.
• Practice filters out junk emails with real message data.

• Introduces finding patterns without any answers, like grouping similar songs.
• Explains clustering (making groups) and cutting data size as key goals.
• Shows examples like sorting customers without knowing their habits.
• Sets up learners for digging into data’s hidden stories.

• Teaches grouping data into clumps by finding center points, like organizing dots.
• Explains picking how many groups with tricks like the elbow curve.
• Shows coding it in Python and drawing the clumps on a chart.
• Practice sorts people into types based on survey answers

• Builds a tree of groups, starting small and combining up, like a family tree.
• Explains different ways to link groups, like closest or average points.
• Teaches coding it in Python and reading the tree diagram.
• Practice clusters plant types from biology data.

• Shows shrinking data into fewer pieces while keeping the good stuff.
• Explains finding the main directions data spreads, like a shortcut map.
• Teaches coding PCA in Python to simplify big datasets.
• Practice compresses photo data to see how it holds up

• Covers checking number predictions with errors like MSE or RMSE.
• Explains category checks with accuracy, precision, and recall scores.
• Teaches using charts like confusion tables and ROC curves.
• Practice tests past models to see what worked best.
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• Shows splitting data into chunks to test models fairly, like a round-robin.
  Explains why it beats a single split by catching flukes.
• Teaches coding k-fold checks in Python on real models.
• Practice runs it on a tree model to prove its worth.

• Explains tweaking settings like learning speed or tree size for better results.
• Covers trying all combos with grid search or guessing smart with random search.
• Teaches coding these in Python to polish models.
• Practice tunes an SVM to nail a tough prediction.

• Introduces brain-like models with layers and connections for complex tasks.
• Explains feeding data through, adjusting with errors, and picking functions.
• Shows building a basic network in Python with TensorFlow.
• Practice predicts digits from scratchy handwriting.

• Teaches spotting image patterns with sliding windows and pooling tricks.
• Explains stacking layers to recognize shapes and details.
• Shows coding CNNs in Python for picture tasks.
• Practice sorts animal photos into types.

• Covers handling sequences like text or time data with memory loops.
• Explains fixes like LSTMs to keep long-term info flowing.
  Teaches coding RNNs in Python for step-by-step predictions.
• Practice guesses next words in sentences.

• Shows chopping text into words, trimming extras, and counting patterns.
• Explains turning words into numbers with bags or frequency scores.
• Teaches coding simple NLP in Python for text tasks.
• Practice sorts reviews into happy or mad.

• Combines weak models into strong ones with bagging or boosting steps.
• Explains piling up guesses or weighting smartly for better wins.
• Shows coding ensembles in Python to lift scores.
• Practice blends trees to predict sales spikes.

• Teaches spotting trends and cycles in data like stock prices over time.
• Explains smoothing data or using ARIMA to guess what’s next.
• Shows coding forecasts in Python with real numbers.
• Practice predicts weather shifts from past days

• Covers wrapping models in APIs with Flask for live use.
• Explains saving models and hooking them to cloud spots like AWS.
• Teaches coding a live predictor in Python.
• Practice sets up a price guesser online.