Machine Learning Training

Unlock the potential of AI: Mastering Machine Learning for Tomorrow’s Innovations.

What you'll learn

Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
Real life case studies and projects to understand how things are done in the real world
Learn to pre process data, clean data, and analyze large data
Supervised and Unsupervised Learning
Explore large datasets using data visualization tools like Matplotlib and Seaborn
Explore large datasets and wrangle data using Pandas
Learn NumPy and how it is used in Machine Learning

Learn to perform Classification & Regression modelling
Make robust Machine Learning models
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Machine Learning on Time Series data
Know which Machine Learning model to choose for each type of problem
Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Our Training Process

Practical Session

Assignment

Projects

Be Job Ready

Interview Preparation

Resume Building

Key Highlights

Personalised career coach
90% Practical Training
Certification
100% Placement Assistance
Study material
Instant doubt solving
Mock Interviews
Case studies and Projects

Course Content

Introduction to Machine Learning (ML)

What is Machine Learning?
Use Cases of Machine Learning.
Types of Machine Learning – Supervised to Unsupervised methods.
Machine Learning workflow.

Statistics for Data Science

Common charts used.
Inferential Statistics.
Probability, Central Limit theorem, Normal Distribution & Hypothesis testing.

Data Visualization

Plotting basic statistical charts in Python.
Data visualization with Matplotlib.
Statistical data visualization with Seaborn.
Interactive data visualization with Bokeh.

Exploratory Data Analysis

Introduction to Exploratory Data Analysis (EDA) steps.
Plots to explore relationship between two variables.
Histograms, Box plots to explore a single variable.
Heat maps, Pair plots to explore correlations.

Data Preprocessing

Preprocessing techniques like missing value imputation.
Encoding categorical variables.
Scaling, Too many nulls, Same values/skew.
Data types, Missing value imputation.
When column doesn’t have missing values.
Categorical Attributes, Related Attributes.

Linear Regression

Introduction to Linear Regression.
Use cases of Linear Regression.
How to fit a Linear Regression model?
Evaluating and interpreting results from Linear Regression models.
Project: How linear regression helps determine demand in Restaurant.

Logistic Regression

Introduction to Logistic Regression.
Logistic Regression use cases.
Understand use of odds & Logit function to perform logistic regression.
Project:
- Predicting default cases in the Banking Industry.

Decision trees & Random Forests

Introduction to Decision Trees & Random Forest.
Understanding criterion(Entropy & Information Gain) used in Decision Trees.
Using Ensemble methods in Decision Trees.
Applications of Random Forest.
Project:
- Predict passengers survival in a Ship mishap.

Model evaluation techniques

Introduction to evaluation metrics and model selection in Machine Learning.
Importance of Confusion matrix for predictions.
Measures of model evaluation – Sensitivity, specificity, precision, recall & f-score.
Use AUC-ROC curve to decide best model.
K-fold Cross Validation.
Parameter Tuning.
Grid Search.
XGBoost.
Project:
- Applying model evaluation techniques to prior projects.

Dimensionality Reduction using PCA

Unsupervised Learning: Introduction to Curse of Dimensionality.
What is dimensionality reduction?
Technique used in PCA to reduce dimensions.
Applications of Principle component Analysis (PCA).
Project:
- Classify smokers among,
- Classify malicious websites using close neighbour technique,
- Credit scoring analysis using weighted k nearest neighbor.

KNN (K–Nearest neighbours)

Introduction to KNN.
Calculate neighbours using distance measures.
Find optimal value of K in KNN method.
& Disadvantages of KNN.
Project:
- Optimize model performance using PCA on high dimension dataset.

Naive Bayes classifier

Introduction to Naive Bayes classification.
Refresher on Probability theory.
Applications of Naive Bayes Algorithm in Machine Learning.
Project:
- Classify Spam SMS, based on probability.

K-means clustering technique

Introduction to K-means clustering.
Decide clusters by adjusting centroids.
Find optimal ‘k value’ in kmeans.
Understand applications of clustering in Machine Learning.
Project:
- Predict flower species in Iris flower data.

Support vector machines (SVM)

Introduction to SVM.
Figure decision boundaries using support vectors.
Find optimal ‘k value’ in kmeans.
Applications of SVM in Machine Learning.
Project:
- Predicting wine quality without tasting the wine,
- Personal Info App.

Time series forecasting

Introduction to Time Series analysis.
Stationary vs non stationary data.
Components of time series data.
Interpreting autocorrelation & partial autocorrelation functions.
Stationarize data and implement ARIMA model.
Project:
- Forecast demand for Air travel.

Ensemble learning

Introduction to Ensemble Learning.
What are Bagging and Boosting techniques?
What is Bias variance trade off?
Project:
- Predict annual income classes from adult census data
- Personal Info App.

Stacking

Introduction to stacking.
Use Cases of stacking.
How stacking improves machine learning models?
Project:
- Predict survivors in Titanic case

Gradient Descent Algorithm

Introduction to Gradient Descent.
Why we use Gradient Descent?
Gradient Descent optimization of a single variable, Part-1
Gradient Descent optimization of a single variable, Part-2
Gradient Descent optimization of two or more variables.

Advanced Topics

Cost function.
Training & testing.
- Overfitting and Underfitting Problem.
- Bias and Variance.
- Bias – Variance Trade off.
Regularization Techniques
- Lasso (L1) Regression.
- Lasso (L2) Regression.
Metrics
- Confusion Matrix.
- TP, FP, TN, FN
- Precision and Recall
- F1 Score

Deep Learning

Neural Networks.
Activation Functions.
Gradient Descent for Neural Networks.
- Forward Propagation
- Backward Propagation
Backward Propagation.

Convolution Neural Networks(CNN)

Types of Layers.
- Convolution Layer – Math and representation
- About activation Layer
- About pooling Layer
- About fully connected Layer
- Padding
- Strides
Vanishing Gradient Problem.
Hyperparameters and its’ effects.
- Learning Rate
- Epoch
- Batch
Transfer Learning.
- Learning Rate
- VGG-16
- ResNet
- Inception Network

Sequence Models & NLP

One Hot Encoding, Bag of Words, TFIDF, Word2Vec.
Recurrent Neural Networks (RNN Structure).
Bidirectional RNN.
LSTM.
Attention Mechanism.
Transformer Networks.

Generative AI

Large Language Models (LLMs).
LLM’s Industry use cases.
Prompting Techniques.
- One Shot Prompting.
- FewS hot Prompting.
OpenAI – GPT 3.5 / GPT 4.
LangChain.
Hugging Face.
Google PaLM
Google Gemini / Gemini Pro / Gemini Pro Vision.

Optimization

Introduction to optimization in ML.
Applications of optimization methods.
Optimization techniques: Linear Programming using Excel solver.
How Stochastic Gradient Descent(SGD) Works?
Projects:
- Apply SGD on Regression data (sklearn dataset).
- Personal Info App.

Capstone Projects

Stock Market Prediction.
Medical Diagnosis System.
Natural Language Processing (NLP) for Customer Support.
Sentiment Analysis on Social Media.
Gesture Recognition.
Fake News Detection.

Skills you will gain

Machine Learning Python Generative AI NLP Deep Learning Data Preprocessing Model Selection Hyperparameter Tuning Evaluation Metrics Cross-Validation Ensemble Methods Machine Learning Training in Mumbai India Data Science

Course Certification

Become a Certified Machine Learning Expert with TryCatch Classes and enhance your career prospects to the next level.

This certificate serves as an official badge of your successful Machine Learning training course completion, highlighting your expertise.

Students Reviews

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TryCatch Classes offers an excellent learning environment. All the teaching staff is exceptional. As a newcomer to web development, all concepts were explained clearly.

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Full Stack Developer
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Hi my name is Bhagyashri Gunjate. I have completed full stack web development course from Trycatch and saying this happily that I also got my first job from TryCatch. Although I have gap of 4 years after completing my engineering Mohnish and Mehul Sir gave me confidence that I can do it and at this age also I can be succeed in web development field. Talented and passionate faculty helps me to resolve my query and made my base and logic strong. They initiated new ways of thinking to improve project query and my personal performance as well. Also this helped me to improve my speed to produce codes faster and get things done more accurately. Mehul Sir and Monish Sir was so helpful that they always kept my motivation and confidence high. They gave me projects that are more skilful and as per industry standards which directly helps me to get my first job journey. I highly recommend Try Catch classes to everyone who wanted to upscale their knowledge and career in Web Development field.

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Software engineer
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Genuine reviews for our Machine Learning Training

Darshan

Machine Learning Engineer

First and foremost, I was thoroughly impressed with the quality of education, it was on point of my expectations. They were able to break down complex concepts into easily digestible pieces, and their explanations were always clear and concise. Also every faculty present there is the industry expert himself.I also appreciated the emphasis that Try Catch Tech placed on hands-on learning. Rather than simply lecturing about the topic, the classes provided ample opportunities to put theory into practice. This approach not only helped me understand the concepts better, but it also gave me a sense of confidence when working with these technologies.Another aspect of Try Catch Tech that stood out to me was the supportive community that surrounded the classes. From fellow students to instructors, everyone was incredibly encouraging and willing to help each other out. This made for a collaborative learning environment that I truly appreciated. And their support is so much that even after completing my course I was always welcomed to sit and study there in the classes.Overall, I would highly recommend Try Catch Tech to anyone looking to learn anything from the course they offer. Personally I did python and machine learning which was a great experience for me. The quality of instruction, hands-on learning opportunities, and supportive community make it an excellent choice for both beginners and experienced programmers alike. Thank you, Try Catch Tech, for a wonderful learning experience!

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Sudarshan Paranjape

Machine Learning Engineer

I completed TryCatch Group’s Data Science course, and it was an enlightening journey! The comprehensive curriculum covered essential topics, and the instructors were knowledgeable and approachable. Hands-on projects boosted my confidence, and provided ample resources. The supportive team promptly addressed queries, and industry case studies enriched the experience. The mentorship provided by Mohnish sir and Mehul sir was truly exceptional! Their expertise and passion for data science shone through in every aspect of the course. Highly recommended for all skill levels, this course lays a strong foundation for a successful data science career. Thank you, TryCatch Group, for this amazing learning opportunity!

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Mansi Rathod

Machine Learning Engineer

I wanted to go to USA to do my Masters in Data Science. So I joined Trycatch and completed the Data Science course at TryCatch. Anyone who’s willing to pursue Data Science in the future but has no to little knowledge about it, this class will definitely help you enhance your knowledge. Mehul and Mohnish being knowledgeable, will be there to guide you whenever needed. Overall experience was really good so I would definitely recommend this course.

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Mansi Rathod

Machine Learning Engineer

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Jamil Shaikh

Machine Learning Engineer

I have done Data Science course from Trycatch Classes. I would recommend this course as it gives good idea about the field also the staff are friendly and helpful. This course has helped me to understand my subjects easily.

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Jamil Shaikh

Machine Learning Engineer

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Sujwal Shetty

Machine Learning Engineer

I had taken Python+ ML course here. The overall experience was very good, I got the internal switch to Data Science domain which i was looking for in my organisation. The faculty had great knowledge within the subject.

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Google

Pankit Rojasra

Machine Learning Engineer

In my experience, this class is hands-down the best option if you’re interested in a career in IT or computer coding. I completed the Data Science course here, and I can say all the faculty are highly knowledgeable, experienced, and effective instructors. Following the course, I secured an internship in the backend field with an insurance company.

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Software Developer

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Student's Portfolio

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Mansi Sanghani

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Mansi Sanghani

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Mansi Sanghani

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Mansi Sanghani

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Mansi Sanghani

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Success Stories

Frequently Asked Questions (FAQs)

What is the duration of the course?

Total duration is approximately 3 months along with Live Projects.

Is there 100% Placement Guarantee after the course is over?

We provide 100% placement assistance in our Machine Learning training course in Mumbai.

Are there any prerequisites before starting Machine Learning Training?

It is required to know Python before starting this Machine learning course in Mumbai.

Who teaches Machine Learning?

At TryCatch, our team consists of seasoned experts with over 15 years of experience. A skilled Data Scientist will be guiding students, encouraging them to ask questions without hesitation, and enabling us to effortlessly address all your inquiries.

Is the course Online or Offline?

This Machine Learning course is available offline & online both. You may choose whatever is feasible for you.
Offline course can be done at our Borivali Branch in Mumbai.
Online Live Course can be done on Zoom.

Who can learn Machine Learning?

This course is designed for everyone, even if you’re studying Commerce, Arts, or Mechanical subjects, or if you’re still in school. It doesn’t matter what your background is, you can definitely learn this course.

Do I need prior experience in Machine Learning?

No, prior experience is not required.

What software and tools do I need for this course?

All the tools required for this training will be installed during the course

Will I receive a certificate upon course completion?

Upon completion of the course, you will receive an official global Machine Learning certificate. This certificate serves as an official badge of your successful course completion, highlighting your expertise.

Can I interact with instructors and ask questions during the course?

Absolutely! Our instructors are always available to answer all your questions and solve your doubts.

Are there any real-world projects or case studies in the course?

Yes, we incorporate real-world projects and case studies into the course to help you apply what you’ve learned in practical scenarios.

Is there a money-back guarantee if I’m not satisfied with the course?

We offer a satisfaction guarantee. If you are not satisfied with the course within a specified timeframe, you can request a refund.