What Are the Essential AI and Machine Learning Skills Every Woman in Tech Should Master?

A successful career in AI and machine learning demands a deep understanding of algorithms, data structures, and programming languages like Python and R. Proficiency in mathematics, data preprocessing, and visualization is crucial. Expertise in deep learning, machine learning algorithms, and model evaluation optimizes development. Ethical AI practices, knowledge of cloud computing, and big data technologies are essential. Continuous learning and adaptability to new tools are key for staying ahead.

A successful career in AI and machine learning demands a deep understanding of algorithms, data structures, and programming languages like Python and R. Proficiency in mathematics, data preprocessing, and visualization is crucial. Expertise in deep learning, machine learning algorithms, and model evaluation optimizes development. Ethical AI practices, knowledge of cloud computing, and big data technologies are essential. Continuous learning and adaptability to new tools are key for staying ahead.

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Understanding of Algorithms and Data Structures

Essential for building efficient, scalable machine learning models, a strong foundation in algorithms and data structures is a must. This includes knowledge of sorts, searches, trees, and graphs, as well as understanding how to manipulate and store data efficiently.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

One tip that actually made a difference for me? Stop waiting to “feel ready” and just start showing up for two hours a day. I used to avoid DSA because I thought I had to be good at it to even begin. But once I stopped trying to do it perfectly and just gave it consistent time—things started to click. What helped me the most was using free resources like MIT’s intro to algorithms, Neetcode, and even ChatGPT to walk through problems when I got stuck. I didn’t always understand things on the first try. Still don’t, sometimes. But giving it regular time—even when it felt slow or frustrating—built the kind of thinking that actually helps in AI and ML work. Not just for interviews, but for understanding how data moves, how memory works, and how to build things that scale. So if you’re overwhelmed, try this: two hours a day, no pressure to be perfect. Just show up. Let yourself learn. That’s what worked for me.

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Proficiency in Programming Languages

Python and R are the lingua franca of AI and machine learning. Mastering these programming languages, along with libraries such as TensorFlow, PyTorch, and scikit-learn, allows for the development and implementation of machine learning models.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

One thing I wish someone told me earlier: don’t fall into the “learn Python in 12 hours” trap. There’s this video by one of my favorite creators, Sahil Gabba (shoutout to you, Sahil—if you ever see this, you’re one of the people I look up to so much) called “FASTEST Way to Learn Coding and ACTUALLY Get a Job.” It completely shifted my mindset. He talks about how so many of us get stuck in tutorial hell—watching videos on repeat, learning the same syntax over and over, trying to “perfect” our knowledge before we even use it. I’ve been there. It feels productive, but it’s a trap. The best advice I ever got? Build small projects. Let them be ugly. Let them be inefficient. Just build. You’ll learn more by stumbling through one messy personal project than by watching ten more Python crash courses. One resource that actually helped me bridge the gap between learning and building was the book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. It’s practical, hands-on, and forces you to apply what you're reading. Highly recommend it if you're ready to stop looping and start creating. You don’t need to carry all the tools before you start. This isn’t a war. You’re allowed to try. You’re allowed to grow. And yes—Python, R, scikit-learn, TensorFlow, PyTorch—these are the tools, but they’re learnable when you’re in motion. Kylie yang is another creator whose work reminds me of this: try, fail, reflect, try again. It’s not about being perfect. It’s about building momentum—and keeping your curiosity alive.

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Mathematical Skills

A solid grasp of statistics, probability, linear algebra, and calculus is fundamental. These mathematics principles are the building blocks of machine learning algorithms, helping in understanding how models learn from data and make predictions.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Okay, let’s talk about the thing nobody glamorizes enough: the math is hard. Statistics, probability, linear algebra, calculus—these aren’t just buzzwords in AI. They’re the backbone. But that doesn’t make them easy. In my first semester of grad school, I took a reinforcement learning course (shoutout to my professor Bruno Castro da Silva), and it wrecked me. The math was heavy. The pace was brutal. And now I’m in “Algorithms for Data Science” in my second semester and somehow… it’s worse?! I won’t lie—there were days I stared at equations and just felt like giving up. But here’s the thing: STEM is hard for everyone. And like Ben Cichy said in his tweet—he had a 2.4 GPA his first semester and went on to land spacecraft on Mars. Curiosity and persistence matter way more than being perfect. Math still kicks my butt regularly, but weirdly, I love it. There’s something satisfying about seeing a matrix operation finally make sense, or watching gradient descent click. You don’t have to be a math genius to be in AI. You just have to be someone who’s willing to keep going even when it’s tough. So yeah—it’s a lot. But don’t let that scare you off. Stick with it. Bit by bit, it does start making sense. I know it’s hard. But I also know you’ve got this.

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Data Preprocessing and Visualization

The ability to clean, preprocess, and visualize data is critical. Understanding how to handle missing values, normalize data, and use tools like Matplotlib and Seaborn for data visualization can uncover insights and improve model performance.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

This is way more important than I thought! Along with models or fancy algorithms, you have to know how to clean and understand your data. Handling missing values, normalizing features, and just… making sense of messy real-world datasets is a skill. Tools like Pandas, Matplotlib, and Seaborn help a lot, and honestly, visualizing the data is where I often get my “aha” moments. So if you're just starting out, don’t skip this step. Great models start with good data. And you will get better the more you practice.

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Niruta Talwekar
Staff Data Engineer at Meta Platforms

Data preprocessing is one of those behind-the-scenes steps that often gets overlooked, but it’s actually where the real magic starts. If you zoom out and look at the full lifecycle of building a machine learning model, more than half of the time is typically spent not on modeling, but on collecting, cleaning, and preparing the data so it's actually usable. Think of it like cooking: you can have the best recipe (aka model), but if your ingredients (the data) aren’t fresh or well-prepped, the final dish won’t turn out right. The same goes for machine learning—messy or misaligned data can tank your model's performance, no matter how fancy your algorithms are. This is where data engineering plays a huge role. It involves building the pipelines and processes to gather, clean, transform, and serve data in the right way. Yet, it’s a part of the process that many people underestimate or skip over. For example, in one of my past projects, we spent weeks just aligning data from different sources—some in CSVs, some in APIs, and some stored in outdated databases. Once we got that foundation solid, the actual model training took just a few days. And because we invested that time upfront, the model's performance and reliability were significantly better. If you're serious about building strong AI/ML skills, don’t sleep on data preprocessing and engineering—it’s not just a technical necessity, it’s a competitive advantage.

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Deep Learning Understanding

Deep learning, a subset of machine learning, is behind many advancements in AI. Knowledge of neural networks, CNNs, RNNs, and reinforcement learning, as well as frameworks like TensorFlow and PyTorch, is essential for cutting-edge development.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Deep learning can feel like a black box when you’re starting out. Neural networks, CNNs, RNNs, reinforcement learning—it’s a lot. But once you get into it, it’s also kind of magical. I took a Reinforcement Learning course last semester, and it absolutely stretched my brain. One of the things that stuck with me while learning Markov Decision Processes was this idea: “The future is independent of the past, given the present.” It sounds like a life mantra, honestly—and in RL, it actually is. (Also, existential crises mid-homework? Highly likely.) If you’re diving into deep learning, I’d say start with Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow—it makes the concepts practical and less terrifying. And don’t underestimate the power of lurking in the r/MachineLearning or r/DeepLearning subreddits. Some of the discussions there are chaotic, brilliant, and weirdly comforting. You realize everyone’s kind of lost sometimes. Also, real talk: understanding how to use frameworks like TensorFlow or PyTorch is important—but understanding why things work matters more in the long run. Play with models. Break them. Make a CNN that does nonsense. Build an RNN that writes poetry. Let yourself experiment. It’s okay to not “get it” all at once. Deep learning is deep. That’s the point. Dig anyway.

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Machine Learning Algorithms

A comprehensive understanding of various machine learning algorithms—supervised, unsupervised, and reinforcement learning—is crucial. Knowing when and how to apply algorithms like decision trees, SVMs, k-nearest neighbors, and clustering is key to solving diverse problems.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

There are some absolute legends out there teaching machine learning—and I hope I get to meet them in real life someday. Shout out to Dr. Charles Severance (Dr. Chuck!), who taught me Python when I was 19. I’ll never forget those lessons. Shout out to Kylie Ying, my comfort YouTuber and a total sweetheart—if you’re starting out, go subscribe. And of course, the OG himself: Andrew Ng. Please, please take his Machine Learning course on Coursera. The man is basically a machine learning fairy. He makes it all feel possible. The key with ML algorithms—whether it’s decision trees, SVMs, KNN, clustering, or reinforcement learning—isn’t memorizing everything. It’s understanding when and why to use them. It’s okay to not get it all at once. But with the right teacher? It clicks. There are so many accessible courses out there. And if money’s a barrier? Apply for financial aid on Coursera. I’ve done it. You just have to write a short essay and boom—free courses. Please don’t let cost stop you. You deserve to learn. So yeah—take the courses. Ask dumb questions. Break the code. Cry a little (it’s part of it). And keep going. You got this, sister.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

There are some absolute legends out there teaching machine learning—and I hope I get to meet them in real life someday. Shout out to Dr. Charles Severance (Dr. Chuck!), who taught me Python when I was 19. I’ll never forget those lessons. Shout out to Kylie Ying, my comfort YouTuber and a total sweetheart—if you’re starting out, go subscribe. And of course, the OG himself: Andrew Ng. Please, please take his Machine Learning course on Coursera. The man is basically a machine learning fairy. He makes it all feel possible. The key with ML algorithms—whether it’s decision trees, SVMs, KNN, clustering, or reinforcement learning—isn’t memorizing everything. It’s understanding when and why to use them. It’s okay to not get it all at once. But with the right teacher? It clicks. There are so many accessible courses out there. And if money’s a barrier? Apply for financial aid on Coursera. I’ve done it. You just have to write a short essay and boom—free courses. Please don’t let cost stop you. You deserve to learn. So yeah—take the courses. Ask dumb questions. Break the code. Cry a little (it’s part of it). And keep going. You got this, sister.

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Model Evaluation and Optimization

Learning how to evaluate model performance using metrics such as accuracy, precision, recall, and AUC-ROC curve, as well as techniques for hyperparameter tuning and optimization, is vital for developing effective machine learning models.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Okay, real talk—I didn’t even know about techniques like Grid Search when I first started. I would literally test random combinations of hyperparameters like, “uhh let’s try learning rate = 0.001 and maybe 0.01 too?” and let the code run forever. No strategy. Just vibes. I’m still learning this stuff properly—hyperparameter tuning, cross-validation, performance metrics—but what I have learned so far is that just building a model isn’t enough. You need to evaluate it intentionally. I’m beginning to understand how things like accuracy can be misleading in imbalanced datasets, and how precision, recall, F1-score, and AUC-ROC tell a much more nuanced story. GridSearchCV and tools like RandomizedSearchCV are slowly becoming less intimidating. I still make mistakes (a lot), but I now pause and ask: “Am I evaluating this right? Am I tuning this smartly? Or am I just hoping for the best again?” And honestly, I think that’s what growth in ML looks like—learning to ask better questions and knowing what you don’t know yet.

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Ethical AI and Bias Mitigation

With great power comes great responsibility. Knowledge of ethical AI principles, understanding biases in data and algorithms, and learning how to mitigate these biases are crucial skills to ensure the development of fair and unbiased AI systems.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Biases in AI are insane—and the worst part is, they often go unnoticed until someone is excluded or harmed. I remember working on a group project during my undergrad where we were building a system to control computers with hand gestures. It was exciting—until it wasn’t. My hand literally wouldn’t get recognized by the model. It just… didn’t work for me. At first I was confused. Then it hit me: the dataset barely had any examples of darker-skinned hands. That was my first real experience of algorithmic bias. And it was surreal. I couldn’t even fathom it back then. But this isn’t just about minor inconveniences. The long-term consequences of biased AI can be serious—even dangerous. Think about: Facial recognition tech misidentifying people of color at disproportionately high rates (which has led to wrongful arrests). Healthcare algorithms that underestimate the severity of illness in Black patients. Hiring tools trained on biased data that systematically filter out women and marginalized groups. These aren’t just technical bugs. They’re reflections of the biases we feed into our systems—often unconsciously. That’s why understanding ethical AI principles isn't optional. It’s essential. I’m still learning this field. I don’t have all the answers. But I know that building fair, inclusive systems starts with asking better questions about who’s represented, who’s missing, and who might be harmed. It means pushing for transparency, advocating for diverse datasets, and being aware that fairness isn't a one-time checkbox—it’s a constant responsibility

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Cloud Computing and Big Data Technologies

Familiarity with cloud platforms like AWS, Azure, or Google Cloud, and big data technologies such as Hadoop and Spark, can greatly enhance the ability to work with large datasets and deploy machine learning models at scale.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

I’m still exploring this area, but one thing I do know is that cloud tools make life so much easier—especially when working with large datasets or building models that need more power. I’ve personally used Google Colab a lot (shoutout to all the assignments I’ve survived because of it). It’s been such a great way to get started with cloud-based development without needing fancy hardware. It’s fast, accessible, and honestly kind of a lifesaver. I don’t know everything about AWS or Spark yet—but I’ve learned that just becoming comfortable with one tool at a time can make a huge difference. You don’t need to master it all today. Just start where you are, and build from there.

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Continuous Learning and Adaptability

The field of AI and machine learning is rapidly evolving. A commitment to continuous learning, staying updated with the latest research and technologies, and adaptability to new tools and frameworks are indispensable qualities for success.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Honestly? There’s so. much. to learn. It feels neverending. Every time I think I understand something, ten new frameworks or papers drop and I’m back to feeling behind. But I’ve realized that’s just… the nature of this field. AI doesn’t slow down. And maybe we don’t need to “catch up”—maybe we just need to keep showing up. One new concept at a time. One paper at a time. One tiny win at a time. Some days I feel like I’m drowning in information. Other days, I realize I’m learning more than I give myself credit for. That’s what adaptability means to me now—not mastering everything, but learning how to keep moving even when it feels overwhelming. You don’t need to know it all. You just need to stay curious. That’s more than enough.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Honestly? There’s so. much. to learn. It feels neverending. Every time I think I understand something, ten new frameworks or papers drop and I’m back to feeling behind. But I’ve realized that’s just… the nature of this field. AI doesn’t slow down. And maybe we don’t need to “catch up”—maybe we just need to keep showing up. One new concept at a time. One paper at a time. One tiny win at a time. Some days I feel like I’m drowning in information. Other days, I realize I’m learning more than I give myself credit for. That’s what adaptability means to me now—not mastering everything, but learning how to keep moving even when it feels overwhelming. You don’t need to know it all. You just need to stay curious. That’s more than enough.

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What else to take into account

This section is for sharing any additional examples, stories, or insights that do not fit into previous sections. Is there anything else you'd like to add?

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

If you’re reading all of this and still feeling like you’re behind, like you don’t know enough, like you’ll never catch up—I just want to say: me too. I’m still learning. I still mess things up. I still google basic things. I still stare at error messages and question everything. But I’ve learned that growth doesn’t always look like confidence or clarity. Sometimes it just looks like trying again tomorrow. You don’t need to feel “ready” to belong in tech. You don’t need to know everything to take up space in this field. Keep showing up. Keep learning. Keep messing up and learning again. That’s the path. That’s the work. And you’re doing it. If no one’s told you lately: I’m proud of you. Keep going.

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