Valley experience is all about learning to code in an effective and proper way.
Before you get started with learning from valley, start your pre work. You will be given a certain amount of study material to finish before you start learning in valley.
Learn at valley
At valley, you will undergo a stringent hands on coding exercise in the supervision of some renowned instructors. Get ready to put your coding hat on.
The moment you finish learning in the bootcamp, we directly start you placement training. This is again instructor led. We know what is usually asked in the interviews, and we prepare you exactly for that.
Post valley bootcamp
We provide students support once they get placed too. You will have access to our instructors, study materials etc even after you graduate.
Know the concepts that you are going to learn in weekly manner
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is
different than descriptive statistics. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive
models will be described. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify
the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write
python code to carry out an analysis
Download the Curriculum
Take a look at our syllabus teaching methods.
- Install Ubuntu / VirtualBox
- Explore Linux commands and shell scripting
- Install Python, PIP, Virtualenv, Git
- Setup Slack, GitHub, Google Drive
Introduction to Python
- Python Loops, Iterations, Conditionals,
- Logic, Simple Programs, OOP
Introduction to ML.
- Unsupervised, Supervised Machine Learning
- Clustering, Classification, Regression
- Jupyter Notebook
- pandas, numpy, ggplot, sklearn
- Reading data,Data visualization
Intro to Machine Learning
- Kmeans clustering
- DB Scan clustering
- Hierarchical clustering
- Simple Linear regression
- Multiple Linear regression
- Polynomial regression
Clustering and Regression
- Decision Trees
- Random Forests
- Logistic regression
Naive Bayes,SVMs, PCA
- Naive Bayes
- Kernel SVM
Artificial Neural Networks
- Perceptron, Optimization, Loss function
- Deep Learning
- Introduction to image pre-processing
- Convolutions, CNNs, Transfer Learning
- Object detection, Siamese networks
- Triplet loss, facial recognition
NLP and text based DL
- Introduction to RNNs, LSTMs, new models
- Word2Vec, Video Processing, Text based models
- Trigger Word Detection
- Introduction to Big Data at scale
- Intro to Spark, Hadoop, AWS
- Capstone Project, Scope and Future paths
You graduate from Valley and you will be prepared to face interviews.
Once you graduate, you will have strong and unique portfolio presence in github. You will be doing close to 3 projects during the class hours. We help you increase your marketability along with helping you to learn to code.
Excellent placement team
We have a top notch placement team which is constantly looking to place our candidates into various companies. Our proven track record in placement is an example on how good our placement team is.
Practice, practice, practice
And at last, you will undergo rigorous practice sessions in the form of mock interviews, whiteboard coding challenges, group discussions etc.
You can take courses now and can pay later
We partnered with Propelld to provide our students with the education loan
Founded by ex-I bankers and consultants, Propelld goes beyond traditional CIBIL scores to value a student based on not just his current creditworthiness but signals that show his potential. We see a lot of factors to this effect and
reward a student's performance to identify high-quality borrowers in spite of limited credit or work history.